Canonical Microcircuits for Predictive Coding
Open accessAbstract
This Perspective considers the influential notion of a canonical (cortical) microcircuit in light of recent theories about neuronal processing. Specifically, we conciliate quantitative studies of microcircuitry and the functional logic of neuronal computations. We revisit the established idea that message passing among hierarchical cortical areas implements a form of Bayesian inference-paying careful attention to the implications for intrinsic connections among neuronal populations. By deriving canonical forms for these computations, one can associate specific neuronal populations with specific computational roles. This analysis discloses a remarkable correspondence between the microcircuitry of the cortical column and the connectivity implied by predictive coding. Furthermore, it provides some intuitive insights into the functional asymmetries between feedforward and feedback connections and the characteristic frequencies over which they operate.
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Claims the paper makes
36 of 125 cited sources checked · 23 supported · 1 flagged · accuracy not yet measured
- Premisecitation
In predictive coding, attention is mediated by increasing the gain of populations encoding prediction error.
“in predictive coding, attention is mediated by increasing the gain of populations encoding prediction error (Spratling, 2008; Feldman and Friston, 2010).”
Quote in the paperSubstantial claim2 of 2 sources checked- cites Spratling, 2008 source
In RECONCILING PREDICTIONS (page unavailable), Spratling says that top-down predictions suppress error-detecting neurons while enhancing a subset of prediction neurons. The paper locates attention-related enhancement in the prediction nodes, not in increased gain of populations encoding prediction error. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured - cites Feldman and Friston, 2010 source
- Rests on an assumption: In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
- Premisecitation
In mouse V1/V2 and A1/A2, stimulating feedforward or feedback afferents produced both driving-like and modulatory-like responses.
“In both cases, driving-like responses as well as modulatory-like responses were observed (Covic and Sherman, 2011; De Pasquale and Sherman, 2011).”
Quote in the paperSubstantial claim2 of 2 sources checked - Premisecitation
About 95% of neurons labelled by a retrograde tracer lie within roughly 2 mm of the injection site, indicating intrinsic connections dominate by number.
“By numbers alone, intrinsic connections appear to dominate – 95% of all neurons labelled with a retrograde tracer lie within about 2 mm of the injection site (Markov et al., 2011).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Markov et al., 2011 source
The Results (FLN Values: Local, Long Distance, and Subcortical) say that 95% of *intrinsic* labeled neurons were within 1.9 mm of the injection site, not 95% of all labeled neurons. The same section reports that intrinsic neurons accounted for a mean of 79% of labeled neurons, supporting the conclusion that intrinsic connections dominate by number but not the claim's 95% figure for all neurons. (LLM, conf=0.92)
Experimental — accuracy not yet measured
- Interpretationcitation
The suppression of responses to predictable stimuli cannot be explained by local adaptation because the predictable attributes are not represented in early sensory cortex.
“Crucially, this suppression cannot be explained in terms of local adaptation, because the attributes of the stimuli that can be predicted are not represented in early sensory cortex (e.g., Harrison et al. 2007).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Harrison et al. 2007 source
- Premisecitation
Electrophysiological mismatch negativity studies suggest that neural responses to deviant stimuli that violate sensory predictions are enhanced relative to predicted stimuli.
“For example, electrophysiological studies of the mismatch negativity suggest that neural responses to deviant stimuli – that violate sensory predictions established by a regular stimulus sequence – are enhanced relative to predicted stimuli (Garrido et al., 2009).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Garrido et al., 2009 source
- Premisecitation
Neurons in monkey inferotemporal cortex respond significantly less to a predicted sequence of natural images than to an unpredicted sequence.
“Furthermore, neurons in monkey inferotemporal cortex respond significantly less to a predicted sequence of natural images, compared to an unpredicted sequence (Meyer and Olson, 2011).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Meyer and Olson, 2011 source
- Premisecitation
Neuroimaging studies show that predictable stimuli evoke smaller responses in early cortical areas.
“These studies show that predictable stimuli evoke smaller responses in early cortical areas.”
Quote in the paperSubstantial claim2 of 3 sources checked - Interpretationcitation
Enhanced responses to deviant stimuli are thought to reflect higher cortical areas failing to predict and suppress prediction-error populations.
“These enhanced responses are thought to reflect an inability of higher cortical areas to predict, and thereby suppress, the activity of populations encoding prediction error (Garrido et al., 2007; Wacongne et al., 2011).”
Quote in the paperSubstantial claim2 of 2 sources checked- cites Garrido et al., 2007 source
The predictive-coding passage says that higher-level predictions are passed down the cortical hierarchy and that evoked responses correspond to prediction error that is explained away or suppressed. The paper models responses to deviant tones, but the passages do not specifically attribute enhanced deviant responses to a failure of higher cortical areas to predict and suppress prediction-error populations. Section and page numbers were not supplied for these passages. (LLM, conf=0.81)
Experimental — accuracy not yet measured - cites Wacongne et al., 2011 source
- Rests on an assumption: In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
- Premisecitation
Recent studies show that cortical feedback connections express not only modulatory but also driving characteristics.
“However, recent studies show that cortical feedback connections express not only modulatory but also driving characteristics:”
Quote in the paperSubstantial claim2 of 2 sources checked - Premisecitation
Violating expectations of auditory repetition causes enhanced gamma-band responses in early auditory cortex.
“Similarly, violating expectations of auditory repetition causes enhanced gamma-band responses in early auditory cortex (Todorovic et al., 2011).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Todorovic et al., 2011 source
- Premisecitation
There is mounting evidence that feedforward driving connections, and perhaps feedback, in cortex could be mediated by transthalamic pathways.
“Finally, while many studies have focused on extrinsic connections that project directly from one cortical area to the next, there is mounting evidence that feedforward driving connections (and perhaps feedback) in the cortex could be mediated by transthalamic pathways (Sherman and Guillery, 1998, 2011).”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites Sherman and Guillery, 1998 source
The Conclusions propose that driving communication between cortical areas could pass through a higher-order thalamic relay, supporting the claim’s core possibility. But the paper calls this an intriguing possibility, says the classification of corticocortical pathways remains largely untested, and does not establish mounting evidence or a feedback route. (LLM, conf=0.87)
Experimental — accuracy not yet measured - cites Sherman and Guillery, 2011 source
- Premisecitation
In the somatosensory system, the posterior medial nucleus of the thalamus can relay information between S1 and S2.
“The strongest evidence for this claim comes from the somatosensory system, where it was shown recently that the posterior medial nucleus of the thalamus (POm) – a higher-order thalamic nucleus that receives direct input from cortex – can relay information between S1 and S2 (Theyel et al., 2009).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Theyel et al., 2009 source
- Premisecitation
Canonical circuits have segregated parallel processing streams.
“A key property of canonical circuits is the segregation of parallel streams of processing.”
Quote in the paperNot a substantial claim1 of 3 sources checked- cites Fitzpatrick et al., 1994 source
- cites Briggs and Usrey, 2009 source
The Discussion section, “Feedforward and feedback pathways in the primate visual system” (page unavailable), calls separation into parallel streams a major hallmark of sensory processing and describes segregated streams in the primate visual system. The claim extends that property to canonical circuits generally, which this paper does not establish. (LLM, conf=0.82)
Experimental — accuracy not yet measured - cites Zeki and Shipp, 1988 source
- Premisecitation
Layer 1 cells provide strong monosynaptic inhibition to L2/3 pyramidal cells whose apical dendrites project into L1.
“Simultaneous whole cell patch clamp recordings show that they provide strong monosynaptic inhibition to L2/3 pyramidal cells, whose apical dendrites project into L1 (Chu et al., 2003; Wozny and Williams, 2011).”
Quote in the paperSubstantial claim2 of 2 sources checked - Premisecitation
Computational considerations and recent experimental findings point to a potentially important role for higher-order thalamic nuclei in coordinating and synchronizing cortical responses.
“Furthermore, computational considerations and recent experimental findings point to a potentially important role for higher-order thalamic nuclei in coordinating and synchronizing cortical responses (Vicente et al., 2008; Saalmann et al., 2012).”
Quote in the paperNot a substantial claim2 of 2 sources checked - Interpretationnone given
The computational dependencies required by predictive coding match the physiological dependencies found in quantitative studies of intrinsic connectivity.
“showing the computational dependencies – implied by predictive coding – recapitulate the physiological dependencies implied by quantitative studies of intrinsic connectivity.”
Quote in the paperNot a substantial claim- Rests on an assumption: In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
- Interpretationreasoning
Deriving canonical forms for predictive coding computations allows specific neuronal populations to be assigned specific computational roles.
“By deriving canonical forms for these computations, one can associate specific neuronal populations with specific computational roles.”
Quote in the paperSubstantial claim - Interpretationown data
The paper's analysis finds a close match between the cortical column's microcircuitry and the connectivity that predictive coding implies.
“This analysis discloses a remarkable correspondence between the microcircuitry of the cortical column and the connectivity implied by predictive coding.”
Quote in the paperSubstantial claim- Rests on an assumption: In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
- Premisecitation
The Douglas-Martin circuit could amplify transient thalamic input into sustained cortical activity while keeping excitation and inhibition balanced.
“These interconnections allowed the circuit to amplify transient thalamic inputs to generate sustained activity in the cortex, while maintaining a balance between excitation and inhibition”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Douglas and Martin, 1991 source
- Interpretationreasoning
The spectral asymmetries require a cortical column to synthesize inputs with distinct frequencies and segregate its outputs.
“These asymmetries call for something quite remarkable: namely, a synthesis of spectrally distinct inputs to a cortical column and the segregation of its outputs.”
Quote in the paperSubstantial claim - Premisecitation
In that model, deep pyramidal cells received weak thalamic drive but strong inhibition.
“The deep pyramidal cells received relatively weak thalamic drive but strong inhibition (Figure 1).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Douglas and Martin, 1991 source
- Premisecitation
Douglas and Martin's cat visual cortex model produced a stereotypical fast excitation followed by slower, longer-lasting inhibition.
“They noted a stereotypical pattern of fast excitation, followed by slower and longer-lasting inhibition.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Douglas and Martin, 1991 source
- Premisecitation
Feedforward connections use relatively higher frequencies than feedback connections, as demonstrated empirically.
“these differences suggest that feedforward connections use relatively high frequencies, compared to feedback connections, as recently demonstrated empirically (Bosman et al., 2012).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Bosman et al., 2012 source
- Premisecitation
In mouse motor cortex, the L2/3 to L5A/B connection is the strongest, accounting for one-third of total synaptic current in the circuit.
“They found that the L2/3 to L5A/B was the strongest connection – accounting for one-third of the total synaptic current in the circuit.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Weiler et al., 2008 source
- Interpretationreasoning
An overall inhibitory effect of feedback connections is consistent with in vivo studies.
“An overall inhibitory effect of feedback connections is consistent with in vivo studies.”
Quote in the paperSubstantial claim - Interpretationreasoning
This restricted L4 inhibition supports the idea that L4 responses are dominated by thalamic input while other laminae integrate afferents from a wider cortical patch.
“This supports the notion that L4 responses are dominated by thalamic inputs, while the remaining laminae integrate afferents from a wider cortical patch.”
Quote in the paperSubstantial claim - Premisecitation
Suppression of predictable responses can be regarded as repetition suppression, observed in single-unit recordings from macaque inferior temporal cortex.
“The suppression of predictable responses can also be regarded as repetition suppression, observed in single unit recordings from the inferior temporal cortex of macaque monkeys (Desimone, 1996).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Desimone, 1996 source
- Premisecitation
As hierarchical distance between two areas increases, the proportion of feedforward projections from lower levels and feedback projections from higher levels becomes increasingly biased toward superficial and deep layers respectively.
“as the hierarchical distance between two areas increases, the percentage of cells that send feedforward (resp. feedback) projections from a lower (resp. higher) level becomes increasingly biased towards the superficial (resp. deep) layers (Barone et al., 2000; Vezoli, 2004)”
Quote in the paperNot a substantial claim0 of 2 sources checked - Premisecitation
In mouse motor cortex, the second strongest interlaminar connection is the reciprocal L5A to L2/3 pathway.
“The second strongest interlaminar connection was the reciprocal L5A to L2/3 connection.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Weiler et al., 2008 source
- Premisecitation
Feedback connections were typically observed when pyramidal cells in one layer targeted inhibitory cells in another.
“Feedback connections were typically seen when pyramidal cells in one layer targeted inhibitory cells in another (see Thomson and Bannister, 2003 for a review).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Thomson and Bannister, 2003
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationcitation
Feedback connections can mediate both modulatory and driving effects.
“In summary, feedback connections can mediate modulatory and driving effects.”
Quote in the paperSubstantial claim - Premisecitation
In the absence of geniculate input, feedback from area 18 can drive superficial cells in area 17.
“However, superficial cells were silenced when area 18 was lesioned. This is consistent with a driving effect of feedback connections from area 18, in the absence of geniculate input.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Mignard and Malpeli, 1991 source
- Premisecitation
Patients with prefrontal cortex damage show disinhibition of event-related potential responses to repeating stimuli.
“Patients with damage to the prefrontal cortex (PFC) show disinhibition of event related potential responses (ERP) to repeating stimuli (Knight et al., 1989; Yamaguchi and Knight, 1990; but see Barceló et al., 2000).”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
Attentional modulation can interact with top-down predictions to override their suppressive influence.
“The resulting attentional modulation (e.g., Hopfinger et al., 2000) can interact with top-down predictions to override their suppressive influence – as demonstrated empirically (Kok et al., 2011).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Hopfinger et al., 2000 source
- cites Kok et al., 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
Because feedback connections convey predictions that reduce lower-level prediction errors, their effective polysynaptic connectivity is generally assumed to be inhibitory.
“Crucially, because feedback connections convey predictions – that serve to explain and thereby reduce prediction errors in lower levels – their effective (polysynaptic) connectivity is generally assumed to be inhibitory.”
Quote in the paperSubstantial claim- Rests on an assumption: In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
- Interpretationreasoning
The L5A to L2/3 pathway in motor cortex may be homologous to the prominent L4/5A to L2/3 pathway in sensory cortex.
“This pathway may be homologous to the prominent L4/5A to L2/3 pathway in sensory cortex.”
Quote in the paperSubstantial claim - Premisecitation
In mouse motor cortex, recurrent intralaminar connections are prominent, particularly in L2, L5A/B, and L6.
“Also – as in sensory cortex – recurrent (intralaminar) connections were prominent, particularly in L2, L5A/B and L6.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Weiler et al., 2008 source
- Premisecitation
The primary source of inhibition in mouse cortex is cells in the same layer, reflecting prevalent inhibitory intralaminar connections.
“Second, the primary source of inhibition originates from cells in the same layer, reflecting the prevalence of inhibitory intralaminar connections.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kätzel et al., 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationcitation
For hierarchically proximate areas, feedback connections can drive their targets as strongly as feedforward connections.
“This indicates that – for these hierarchically proximate areas – feedback connections can drive their targets just as strongly as feedforward connections.”
Quote in the paperSubstantial claim - Interpretationcitation
The finding that feedback can both facilitate and suppress firing in lower areas depending on classical and extra-classical receptive field content is consistent with predictive coding.
“The empirical finding that feedback connections can both facilitate and suppress firing in lower hierarchical areas – depending on the content of classical and extra-classical receptive fields – is consistent with predictive coding:”
Quote in the paperSubstantial claim- Rests on an assumption: In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
- Interpretationreasoning
The Hupé and Bullier findings can be explained by predictive coding: deactivating feedback removes top-down predictions, causing disinhibition in earlier areas only when stimuli can be predicted over multiple CRFs.
“This provides a simple explanation for the findings of Hupé et al (1998) and Bullier et al (1996): when feedback connections are deactivated, there are no top-down predictions to explain responses in lower areas – leading to a disinhibition of responses in earlier areas, when – and only when – stimuli can be predicted over multiple CRFs.”
Quote in the paperSubstantial claim- Rests on an assumption: In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
- Interpretationcitation
Corticocortical feedback connections could deliver strong inhibition if they recruit the inhibitory potential of L1.
“These studies suggest that corticocortical feedback connections could deliver strong inhibition, if they were to recruit the inhibitory potential of L1.”
Quote in the paperSubstantial claim - Interpretationreasoning
A stimulus confined to a CRF would elicit a strong prediction error signal because it cannot be predicted.
“In this setting, a stimulus that is confined to a CRF would elicit a strong prediction error signal (because it cannot be predicted).”
Quote in the paperSubstantial claim- Rests on an assumption: In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
- Interpretationreasoning
Intrinsic cortical circuitry allows processing to proceed in discrete steps that can produce response properties in outputs that are absent from the inputs.
“These models suggest that intrinsic cortical circuitry allows processing to proceed along discrete steps that are capable of producing response properties in outputs that are not present in inputs.”
Quote in the paperSubstantial claim0 of 3 sources checked - Interpretationcitation
Predictive input from higher cortical areas might have an important impact via the distal dendrites of pyramidal neurons.
“In terms of the excitatory and modulatory effect of feedback connections, predictive input from higher cortical areas might have an important impact via the distal dendrites of pyramidal neurons (Larkum et al., 2009).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Larkum et al., 2009 source
- Premisecitation
Biological systems are homoeostatic or allostatic and minimise the dispersion, or entropy, of their interoceptive and exteroceptive states.
“Biological systems are homoeostatic (or allostatic), which means that they minimise the dispersion (entropy) of their interoceptive and exteroceptive states.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Friston, 2010 source
- Interpretationcitation
Higher visual areas come to predict that visual stimuli will span the receptive fields of cells in lower visual areas.
“Hence, higher visual areas come to predict that visual stimuli will span the receptive fields of cells in lower visual areas.”
Quote in the paperSubstantial claim - Premisecitation
Layer 1 contains less than 0.5% of all cells in a cortical column.
“Indeed, a recent study concluded that L1 contains less than 0.5% of all cells in a cortical column (Meyer et al., 2011).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Meyer et al., 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationcitation
The evidence for an excitatory effect of feedforward connections is unequivocal.
“While the evidence for an inhibitory effect of feedback connections has to be evaluated carefully, the evidence for an excitatory effect of feedforward connections is unequivocal.”
Quote in the paperSubstantial claim - Interpretationcitation
The inhibitory effects of feedback connections may depend on natural stimuli that require integration over the visual field.
“These results indicate that the inhibitory effects of feedback connections may depend on (natural) stimuli that require integration over the visual field.”
Quote in the paperSubstantial claim - Interpretationreasoning
Layer 1 inhibitory cells are in a prime position to mediate inhibitory effects of extrinsic feedback.
“This means L1 inhibitory cells are in a prime position to mediate inhibitory effects of extrinsic feedback.”
Quote in the paperSubstantial claim - Premisecitation
In monkey, V1 projects monosynaptically to V2, V3, V3a, V4, and V5/MT.
“For example, in the monkey, V1 projects monosynaptically to V2, V3, V3a, V4, and V5/MT (Zeki, 1978; Zeki and Shipp, 1988).”
Quote in the paperNot a substantial claim0 of 2 sources checked - Premisecitation
When V1 is reversibly inactivated by cooling, single-cell activity in target areas is strongly suppressed.
“In all cases – when V1 is reversibly inactivated through cooling – single-cell activity in target areas is strongly suppressed (Girard and Bullier, 1989; Girard et al., 1991a, 1991b, 1992).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Girard and Bullier, 1989 source
- cites Girard et al., 1991a, 1991b, 1992
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationcitation
The proposed circuitry agrees remarkably well with quantitative characterisations of the canonical microcircuit.
“In particular, we find that the proposed circuitry agrees remarkably well with quantitative characterisations of the canonical microcircuit (Haeusler and Maass, 2007).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Haeusler and Maass, 2007 source
- Rests on an assumption: A cortical column contains the circuitry needed for the relevant computations, and this circuitry is replicated with only minor variations across cortex.
- Premisecitation
A specific type of GABAergic neuron appears to control distal dendritic excitability, gating top-down excitatory signals differentially during behavior.
“Furthermore, there is a specific type of GABAergic neuron that appears to control distal dendritic excitability, gating top down excitatory signals differentially during behavior (Gentet et al., 2012).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Gentet et al., 2012 source
- Interpretationcitation
Feedback connections could mediate selective inhibition via their terminations in layer 1.
“However, one important route by which feedback connections could mediate selective inhibition is via their termination in L1 (Anderson and Martin, 2006; Shipp, 2007):”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationcitation
Activity in higher cortical areas depends on driving inputs from earlier cortical areas that establish their receptive field properties.
“These studies illustrate that activity in higher cortical areas depends on driving inputs from earlier cortical areas that establish their receptive field properties.”
Quote in the paperSubstantial claim - Premisecitation
Cooling area V5/MT while recording in V1, V2, and V3 decreased unit activity in earlier areas when stimuli were in the classical receptive field, suggesting an excitatory effect of extrinsic feedback.
“When visual stimuli were presented in the classical receptive field (CRF), cooling of area V5/MT decreased unit activity in earlier areas, suggesting an excitatory effect of extrinsic feedback (Hupé et al., 1998).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Hupé et al., 1998 source
- Premisecitation
In V2 and V3, cooling V1 nearly totally silences single-unit activity.
“In the cases of V2 and V3, the result of cooling area V1 is a near-total silencing of single unit activity.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Girard et al., 1991a, 1991b, 1992
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
In rat barrel cortex, stimulating and inactivating L1 showed that L1 exerts a powerful inhibitory effect on whisker-evoked responses.
“Indeed, a study of rat barrel cortex – that stimulated (and inactivated) L1 – showed that it exerts a powerful inhibitory effect on whisker-evoked responses (Shlosberg et al., 2006).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Shlosberg et al., 2006 source
- Interpretationreasoning
The form of the recognition dynamics in Equation (1) implies that higher frequencies are anticipated in superficial pyramidal cells relative to deep pyramidal cells.
“If we focus on the superficial and deep pyramidal cells, the form of the recognition dynamics in Equation (1) tells us something quite fundamental: We would anticipate higher frequencies in the superficial pyramidal cells, relative to the deep pyramidal cells.”
Quote in the paperSubstantial claim - Premisereasoning
Maximising Bayesian model evidence corresponds to Bayesian filtering of sensory inputs, also known as predictive coding.
“Maximising Bayesian model evidence corresponds to Bayesian filtering of sensory inputs. This is also known as predictive coding.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Friston, 2010 source
- Premisereasoning
Since surprise is the negative logarithm of Bayesian model evidence, the brain must continually maximise the Bayesian evidence for its generative model of sensory inputs.
“In statistics, surprise is the negative logarithm of Bayesian model evidence, which means biological systems – like the brain – must continually maximise the Bayesian evidence for their (generative) model of sensory inputs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Friston, 2010 source
- Interpretationreasoning
There is a remarkable correspondence between the anatomy and physiology of the canonical microcircuit and the formal constraints implied by generalised predictive coding.
“In conclusion, there is a remarkable correspondence between the anatomy and physiology of the canonical microcircuit and the formal constraints implied by generalised predictive coding.”
Quote in the paperSubstantial claim- Rests on an assumption: A cortical column contains the circuitry needed for the relevant computations, and this circuitry is replicated with only minor variations across cortex.
- Interpretationreasoning
The form of the generative model determines the form of the inversion, such as predictive coding.
“Crucially, the form of the generative model dictates the form of the inversion – for example, predictive coding.”
Quote in the paperSubstantial claim - Interpretationreasoning
To maintain homeostasis, the brain must predict its sensory states using a model.
“In other words, to maintain a homoeostasis the brain must predict its sensory states on the basis of a model.”
Quote in the paperSubstantial claim - Interpretationnone given
Spiny stellate excitatory cells in the granular layer receive feedforward connections that encode prediction errors on the hidden causes of the level below.
“Third, we know that the (spiny stellate) excitatory cells in the granular layer receive feedforward connections encoding prediction errors ξv(i) on the hidden causes of the level below.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The nature of this message passing is remarkably consistent with the anatomical and physiological features of cortical hierarchies.
“The nature of this message passing is remarkably consistent with the anatomical and physiological features of cortical hierarchies.”
Quote in the paperSubstantial claim - Interpretationcitation
The ensuing architecture bears a striking correspondence to the Haeusler and Maass microcircuit, with nearly every connection required by predictive coding present in quantitative measures of intrinsic connectivity.
“The ensuing architecture bears a striking correspondence to the microcircuit in (Haeusler and Maass, 2007) in the left panel of Figure 5 – in the sense that nearly every connection required by the predictive coding scheme appears to be present in terms of quantitative measures of intrinsic connectivity.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Haeusler and Maass, 2007 source
- Rests on an assumption: A cortical column contains the circuitry needed for the relevant computations, and this circuitry is replicated with only minor variations across cortex.
- Interpretationreasoning
Prediction errors should express higher frequencies than the predictions that accumulate them.
“In short, prediction errors should express higher frequencies than the predictions that accumulate them.”
Quote in the paperSubstantial claim - Interpretationreasoning
Neuronal activity can be interpreted as message passing among the nodes of a generative model, with each canonical microcircuit containing representations or expectations about hidden states and causes.
“These dependencies mean that we can interpret neuronal activity as message passing among the nodes of a generative model, where each canonical microcircuit contains representations or expectations about hidden states and causes.”
Quote in the paperSubstantial claim - Premisecitation
To predict sensations, the brain must have a generative model of how those sensations are caused.
“To predict sensations, the brain must be equipped with a generative model of how its sensations are caused (Helmholtz, 1860).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Helmholtz, 1860
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- May combine two claims. 47
- Classified as probably not a substantial claim. 14
- The quote only partly states this claim. 2
May combine two claims.
PremisecitationSuperficial cortical layers show neuronal synchronization and spike-field coherence mainly at gamma frequencies, whereas deep layers prefer lower alpha or beta frequencies.
“recent findings suggest that the superficial layers of cortex show neuronal synchronization and spike-field coherence predominantly in the gamma frequencies, while deep layers prefer lower (alpha or beta) frequencies (Roopun et al., 2006, 2008; Maier et al., 2010; Buffalo et al., 2011).”
Quote in the paperNot a substantial claim1 of 3 sources checked- cites Roopun et al., 2006, 2008
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Maier et al., 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Buffalo et al., 2011 source
May combine two claims.
PremisecitationOptogenetic driving of L6 principal cells in V1 inhibited their extrinsic targets in LGN and intrinsic targets in cortical layers 2 to 5.
“the authors showed that deep L6 principal cells inhibited their extrinsic targets in the LGN and their intrinsic targets in cortical layers 2 to 5 (Olsen et al., 2012).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Olsen et al., 2012 source
May combine two claims.
PremisecitationThe LGN-to-V1 connection is only the sixth strongest input to V1 but has a substantial effect on V1 responses.
“A case in point is the LGN to V1 connection: although it is only the sixth strongest connection to V1, LGN afferents have a substantial effect on V1 responses (Markov et al., 2011).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Markov et al., 2011 source
The Results (Interareal Connectivity Profiles; page unavailable) describe a small LGN input to V1, and the Discussion (Connectivity Profiles: Local Versus Long Distance, and Subcortical; page unavailable) reports an LGN-to-V1 FLNt of 0.16% and discusses amplification of sparse thalamic signals by local circuits. Our copy does not establish that LGN is specifically the sixth strongest input or directly measure its effect on V1 responses. (LLM, conf=0.83)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationIn Olsen et al., LGN visual responses were suppressed by 76%, and V1 suppression was around 80-84%.
“This suppression was powerful – in the LGN, visual responses were suppressed by 76%. Suppression was also high in V1, around 80-84% (Olsen et al., 2012).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Olsen et al., 2012 source
May combine two claims.
PremisecitationSherman and Guillery identified properties distinguishing driving from modulatory connections: driving connections have strong ionotropic components, large EPSPs, and depressing responses to multiple EPSPs; modulatory connections have metabotropic and ionotropic responses, weak EPSPs, and paired-pulse facilitation.
“Driving connections tend to show a strong ionotropic component in their synaptic response, evoke large EPSPs, and respond to multiple EPSPs with depressing synaptic effects. Modulatory connections produce metabotropic and ionotropic responses when stimulated, evoke weak EPSPs, and show paired-pulse facilitation (Sherman and Guillery, 1998; 2011).”
Quote in the paperSubstantial claim1 of 2 sources checkedMay combine two claims.
PremisecitationThe remaining 5% of labelled cells give rise to extrinsic connections, which are sparse but can be very effective in driving targets.
“The remaining 5% represent cells giving rise to extrinsic connections, which – although sparse – can be extremely effective in driving their targets.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Markov et al., 2011 source
The Results section (FLN Values: Local, Long Distance, and Subcortical; page unavailable) assigns about 5% of labeled neurons to long-range corticocortical connections, but also reports about 16% from adjoining cortical areas; 5% is therefore not the total for extrinsic connections. The Discussion (Connectivity Profiles: Local Versus Long Distance, and Subcortical; page unavailable) says weak cortical inputs can be robust and reliable and may transmit effectively, but does not establish that these connections are extremely effective at driving their targets. (LLM, conf=0.86)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationIn mouse barrel cortex, the strongest connections are intralaminar, and the strongest interlaminar connections are ascending L4 to L2 and descending L3 to L5.
“As in visual cortex, the strongest connections were intralaminar and the strongest interlaminar connections were the ascending L4 to L2, and descending L3 to L5.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Lefort et al., 2009 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationFeedforward connections are traditionally viewed as strong and driving, able to elicit spiking and confer classical receptive field properties, while feedback connections are thought to modulate extra-classical receptive field properties according to context.
“The traditional view holds that feedforward connections are strong and driving, capable of eliciting spiking activity in their targets and conferring classical receptive field properties – the prototypical example being the synaptic connection between LGN and V1 (Sherman and Guillery, 1998). Feedback connections are thought to modulate (extra-classical) receptive field characteristics according to”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Sherman and Guillery, 1998 source
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationLayer 1 cells are almost all inhibitory and interconnect strongly via electrical connections and chemical synapses.
“These L1 cells are almost all inhibitory and interconnect strongly with each other, via electrical connections and chemical synapses (Chu et al., 2003).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Chu et al., 2003 source
The Abstract says histochemical studies suggest most layer 1 neurons are GABAergic, but it does not establish that almost all are inhibitory. The ‘Electrical coupling and inhibitory connections among LS cells’ section reports electrical coupling in 83% and chemical connections in 26.2% of tested late-spiking cell pairs. Those findings support interconnection within that cell class, not a strong interconnection among layer 1 cells generally. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe thalamic reticular nucleus has been proposed to mediate inhibition underlying cross-modal attention or top-down predictions.
“In addition, the thalamic reticular nucleus has been proposed to mediate the inhibition that might underlie cross-modal attention or top-down predictions (Yamaguchi and Knight, 1990; Crick, 1984; Wurtz et al., 2011).”
Quote in the paperNot a substantial claim1 of 3 sources checked- cites Yamaguchi and Knight, 1990 source
- cites Crick, 1984 source
- cites Wurtz et al., 2011 source
In “Modulation of Thalamic Reticular and Lateral Geniculate Neurons by Attention” (page unavailable), the paper describes inhibitory projections from the thalamic reticular nucleus and a proposed role in visual attention. It does not connect that inhibition to cross-modal attention or top-down predictions; its discussion of the attentional mechanism is limited to visual spatial attention. (LLM, conf=0.84)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationIn visual cortex, superficial-layer neuron spiking is more coherent with gamma LFP oscillations, while deep-layer neuron spiking is more coherent with alpha oscillations.
“the spiking activity of neurons in the superficial layers of visual cortex are more coherent with gamma frequency oscillations in the local field potential, while neurons in deep layers are more coherent with alpha frequency oscillations (Buffalo et al., 2011).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Buffalo et al., 2011 source
May combine two claims.
PremisecitationThe corticogeniculate feedback pathway from L6 preserves stream segregation: upper L6 cells preferentially synapse onto parvocellular LGN cells, and lower L6 cells target magnocellular LGN layers.
“The corticogeniculate feedback pathway from L6 maintains this segregation, as upper L6 cells preferentially synapse onto parvocellular cells in the LGN, while lower L6 cells target the magnocellular LGN layers (Fitzpatrick et al., 1994; Briggs and Usrey, 2009).”
Quote in the paperNot a substantial claim1 of 2 sources checkedMay combine two claims.
InterpretationcitationFeedback connections could arise directly from cells encoding conditional expectations in supragranular layers, and emerging evidence indicates that feedback connections between proximate hierarchical levels originate from both deep and superficial layers.
“For example, feedback connections could arise directly from cells encoding conditional expectations in supragranular layers. Indeed, there is emerging evidence that feedback connections between proximate hierarchical levels originate from both deep and superficial layers (Markov et al 2011).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Markov et al 2011 source
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
May combine two claims.
InterpretationcitationThe contribution of any angular frequency in prediction errors to expectations is suppressed in proportion to that frequency, so high frequencies are attenuated when passing from superficial to deep pyramidal cells.
“This equation says that the contribution of any (angular) frequency ω in the prediction errors (encoded by superficial pyramidal cells) to the expectations (encoded by the deep pyramidal cells) is suppressed in proportion to that frequency (Friston 2008). In other words, high frequencies should be attenuated when passing from superficial to deep pyramidal cells.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Friston 2008 source
The Results section (Neuronal Implementation; page unavailable) identifies superficial pyramidal cells with prediction errors and deep pyramidal cells with expected causes. Its Summary says cells elaborating predictions may have low-pass dynamics relative to error-encoding cells, supporting high-frequency attenuation as a possibility. Our copy does not state that each frequency’s contribution is suppressed in proportion to its angular frequency. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S2 (checkable).
PremisecitationPredictive coding is arguably the most plausible neurobiological candidate for making these inferences.
“In terms of implementation, predictive coding is, arguably, the most plausible neurobiological candidate for making these inferences (Srinivasan et al., 1982; Mumford, 1992; Rao and Ballard, 1999).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationFeedforward connections originate predominantly from superficial layers and feedback connections from deep layers.
“Since feedforward connections originate predominately from superficial layers and feedback connections from deep layers”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Felleman and Van Essen, 1991 source
May combine two claims.
PremisecitationIn Douglas and Martin's model, superficial and deep pyramidal cells plus a common inhibitory pool all received thalamic drive and were fully interconnected.
“Their model contained superficial and deep pyramidal cells with a common pool of inhibitory cells. All three neuronal populations received thalamic drive and were fully interconnected.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Douglas and Martin, 1991 source
Probably not a substantial claim: may not pass S4 (based).
InterpretationreasoningSuch output segregation can only arise from local neuronal computations that are structured and precisely interconnected.
“This segregation can only arise from local neuronal computations that are structured and precisely interconnected.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationThe brain uses internal or generative models to infer the causes of sensory input.
“This notion builds on a long history of proposals that the brain uses internal or generative models to make inferences about the causes of its sensorium (Helmholtz, 1860; Gregory 1968, 1980; Dayan et al., 1995).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Helmholtz, 1860
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Gregory 1968, 1980
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Dayan et al., 1995 source
Classified as probably not a substantial claim.
PremisecitationThe brain actively constructs explanations for its sensory inputs.
“The idea that the brain actively constructs explanations for its sensory inputs is now generally accepted.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Helmholtz, 1860
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Gregory 1968, 1980
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Dayan et al., 1995 source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe Douglas-Martin circuit was proposed as a basic motif that might be replicated with minor variations across the cortical sheet.
“Their circuit, although based on recordings from cat visual cortex, was also proposed as a basic theme that might be present and replicated, with minor variations, throughout the cortical sheet (Douglas et al., 1989).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Douglas et al., 1989 source
The quote only partly states this claim.
PremisecitationIn mouse M1, S1, and V1, L4 inhibitory connections have a more restricted lateral extent than those in other layers.
“first, L4 inhibitory connections are more restricted in their lateral extent, relative to other layers.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kätzel et al., 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationCortical feedback contributes to surround suppression of feline LGN cells; without feedback, LGN cells are disinhibited and show weaker surround suppression.
“This evidence is in line with classical studies of corticogeniculate contributions to length tuning in the LGN, showing that cortical feedback contributes to the surround suppression of feline LGN cells: without feedback, LGN cells are disinhibited and show weaker surround suppression (Murphy and Sillito, 1987; Sillito et al., 1993; but see Alitto and Usrey, 2008).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationFeedforward connections originate largely from superficial pyramidal cells and target L4, while feedback connections originate largely from deep pyramidal cells and terminate outside L4.
“Feedforward connections originate largely from superficial pyramidal cells and target L4, while feedback connections originate largely from deep pyramidal cells and terminate outside of L4 (Felleman and van Essen 1991).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Felleman and van Essen 1991 source
May combine two claims.
Premisenone givenNormal subjects rapidly adapt to deviant stimuli as they become predictable, an effect not seen in prefrontal patients.
“Furthermore, normal subjects show a rapid adaptation to deviant stimuli as they become predictable – an effect not seen in prefrontal patients.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningPredictive coding requires feedback connections to drive lower-level cells in a context-sensitive way, which necessitates a modulatory component in their postsynaptic effects.
“In short, predictive coding requires feedback connections to drive cells in lower levels in a context sensitive fashion, which necessitates a modulatory aspect to their postsynaptic effects.”
Quote in the paperSubstantial claim- Rests on an assumption: In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
- Rests on an assumption: Top-down predictions are context sensitive.
May combine two claims.
PremisecitationIn cat and rat visual cortex, the most frequently connected cells are in the same layer, and the largest interlaminar projections are feedforward from L4 to L3 and L3 to L5.
“The most frequently connected cells were located in the same cortical layer, where the largest interlaminar projections were the ‘feedforward’ connections from L4 to L3 and from L3 to L5.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Thomson et al 2002 source
May combine two claims.
PremisecitationPatients with prefrontal damage show reduced-amplitude P300 ERPs to novel stimuli, as if top-down predictions fail to reach sensory cortex.
“In contrast, they show reduced-amplitude P300 ERPs in response to novel stimuli – as if there were a failure to communicate top-down predictions to sensory cortex (Knight, 1984).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Knight, 1984 source
May combine two claims.
PremisecitationExcitatory reciprocal feedback connections were absent from L3 to L4 or less common from L5 to L3, suggesting excitation spreads within the column in a feedforward manner.
“Excitatory reciprocal ‘feedback’ connections were not observed (L3 to L4) or less commonly observed (L5 to L3), suggesting that excitation spreads within the column in a feedforward fashion.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Thomson et al 2002 source
May combine two claims.
PremisecitationIn granular cortex, several interlaminar inhibitory motifs appear general, especially strong inhibition from L4 onto supragranular L2/3 and from infragranular layers onto L4.
“Third, several interlaminar motifs appeared to be general – at least in granular cortex: principally, a strong inhibitory connection from L4 onto supragranular L2/3 and from infragranular layers onto L4.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kätzel et al., 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationThe largest fraction of synaptic input in mouse motor cortex arrives in L5A/B, consistent with a key role in accumulating information from diverse afferents before corticospinal output.
“The largest fraction of synaptic input arrived in L5A/B, consistent with its key role in accumulating information from a wide range of afferents – before sending its output to the corticospinal tract.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Weiler et al., 2008 source
Classified as probably not a substantial claim.
PremisecitationThe cortex is hierarchically organized.
“Current dogma holds that the cortex is hierarchically organized.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Felleman and Van Essen, 1991 source
Classified as probably not a substantial claim.
PremisecitationExtrinsic cortical connections are classified as feedforward, feedback, or lateral, distinguished by laminar origins and targets.
“The idea of a cortical hierarchy rests on the distinction between three types of extrinsic connections: feedforward connections, that link an earlier area to a higher area, feedback connections, that link a higher to an earlier area, and lateral connections, that link areas at the same level (reviewed in Felleman and Van Essen, 1991).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Felleman and Van Essen, 1991 source
May combine two claims.
Premisenone givenIn primates, parvocellular input enters cortex mainly in layer 4Cβ, while magnocellular input enters mainly in layer 4Cα.
“in primates, parvocellular input enters the cortex primarily in layer 4Cβ, whereas magnocellular inputs enter in 4Cα.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIn rat somatomotor cortex, L2/3 shows prominent gamma oscillations co-expressed with beta oscillations in L5, and both rhythms persist when superficial and deep layers are disconnected at L4.
“Roopun et al. (2006) showed that L2/3 of rat somatomotor cortex shows prominent gamma oscillations that are co-expressed with beta oscillations in L5. Both rhythms persisted, when superficial and deep layers were disconnected at the level of L4.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Roopun et al. (2006) source
May combine two claims.
PremisereasoningBecause entropy is the average surprise over time, biological systems minimise the surprise associated with their sensory states at each moment.
“Entropy is the average of surprise over time, which means biological systems minimise the surprise associated with their sensory states at each point in time.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Friston, 2010 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationExtrinsic corticocortical connections are established as exclusively excitatory, although recent evidence suggests inhibitory extrinsic connections exist and may synchronize distant regions.
“The established view is that extrinsic corticocortical connections are exclusively excitatory (using glutamate as their excitatory neurotransmitter) – although recent evidence suggests that inhibitory extrinsic connections exist and may play an important role in synchronizing distant regions (Melzer et al., 2012).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Melzer et al., 2012 source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationCortical columns with structured connectivity are computationally more efficient than networks with the same number of neurons but random connectivity.
“Computational modelling studies indicate that cortical columns with structured connectivity are computationally more efficient than a network containing the same number of neurons but with random connectivity (Haeusler and Maass, 2007).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Haeusler and Maass, 2007 source
May combine two claims.
PremisecitationSupragranular sites have higher broadband gamma power than infragranular sites, whereas alpha and beta power is greater in infragranular and granular layers.
“Maier et al., found that supragranular sites had higher broadband gamma power than infragranular sites. This pattern was reversed in the alpha and beta range; with greater power in the infragranular and granular layers.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Maier et al. (2010)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationcitationAlthough superficial and deep cortical layers are anatomically interconnected, they form functionally distinct processing streams.
“Superficial and deep layers are anatomically interconnected, but mounting evidence suggests that they constitute functionally distinct processing streams”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Roopun et al. (2006) source
- cites Maier et al. (2010)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationWhen stimuli spanned the extra-classical receptive field, cooling V5/MT enhanced V1 responses on average, consistent with a suppressive role of feedback.
“However, when the authors used a stimulus that spanned the extra-classical RF the responses of V1 neurons were – on average – enhanced after cooling area V5, consistent with the suppressive role of feedback connections.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Hupé et al., 1998 source
May combine two claims.
PremisecitationMultilaminar recordings show strong LFP coherence among sites within superficial layers and within deep layers, but weak coherence between the two compartments.
“Maier and colleagues (2010) used multilaminar recordings to show strong LFP coherence amongst sites within the superficial layers (the superficial compartment), as well as strong coherence amongst sites in deep layers (the deep compartment), but weak inter-compartment coherence.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Maier et al. (2010)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationRao and Ballard trained a hierarchical predictive coding network on natural images and showed that higher levels learn to predict visual features extending across many lower-level CRFs.
“Rao and Ballard (1999) trained a hierarchical predictive coding network to recognise natural images. They showed that higher levels in the hierarchy learn to predict visual features that extend across many CRFs in the lower levels (e.g. tree trunks or horizons).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Rao and Ballard (1999) source
May combine two claims.
PremisecitationWhen V2 was cooled and V1 neurons recorded, cooling decreased V1 spiking for CRF-only stimuli but increased V1 activity when stimuli were in both CRF and surround.
“Similar effects were observed when area V2 was cooled and neurons were measured in V1: when stimuli were presented only to the CRF, cooling V2 decreased V1 spiking activity; however, when stimuli were present in the CRF and the surround, cooling V2 increased V1 activity (Bullier et al., 1996).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Bullier et al., 1996 source
May combine two claims.
PremisecitationIn macaque V1, a significant proportion of superficial L2/3 cells (about half) and deep L5 excitatory cells (about 80%) do not project outside the cortical column.
“This accommodates the fact that (in macaque V1) a significant percentage of superficial L2/3 cells (about half) and deep L5 excitatory cells (about 80%) do not project outside the cortical column (Callaway and Wiser, 1996; Briggs and Callaway, 2005).”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningThere are many variations on the mapping between computational and neuronal architectures; even if predictive coding is an appropriate implementation of Bayesian filtering, the arrangement in Figure 5 is only one of several possibilities.
“Having said this, there are many variations on the mapping between computational and neuronal architectures: Even if predictive coding is an appropriate implementation of Bayesian filtering, there are many variations on the arrangement shown in Figure 5.”
Quote in the paperNot a substantial claimMay combine two claims. The quote only partly states this claim.
InterpretationreasoningThe circuitry in Figure 5 is consistent with the broad scheme of ascending feedforward and descending feedback intrinsic connections and recapitulates the functional asymmetries between extrinsic feedforward and feedback connections.
“The circuitry in Figure 5 appears consistent with the broad scheme of ascending (feedforward) and descending (feedback) intrinsic connections: feedforward prediction errors from a lower cortical level arrive at granular layers and are passed forward to excitatory and inhibitory interneurons in supragranular layers, encoding expectations.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenExcitatory and inhibitory interneurons in the supragranular layer are assigned expectations about hidden causes and hidden states, respectively.
“The remaining populations are the excitatory and inhibitory interneurons in the supragranular layer, to which we assign expectations about hidden causes and states respectively.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe arrangement accommodates the fact that dependencies among hidden states are confined to each node, so their expectations and prediction errors should be encoded by interneurons; splitting upper-layer excitatory cells into two populations is sensible because there is a one-to-one mapping between expectations on hidden causes and their prediction errors.
“This arrangement accommodates the fact that the dependencies among hidden states are confined to each node (by the nature of graphical models), which means that their expectations and prediction errors should be encoded by interneurons. Furthermore, the splitting of excitatory cells in the upper layers into two populations (encoding expectations and prediction errors on hidden causes) is sensible,”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningPredictions are formed by a linear accumulation of prediction errors, while prediction errors are nonlinear functions of predictions.
“In summary, predictions are formed by a linear accumulation of prediction errors. Conversely, prediction errors are nonlinear functions of predictions.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningSuperficial pyramidal cells must encode and broadcast prediction errors on hidden causes, while deep pyramidal cells must encode conditional expectations so they can elaborate feedback predictions.
“This means that superficial pyramidal cells must encode and broadcast prediction errors on hidden causes ξv(i+1), while deep pyramidal cells must encode conditional expectations (μ~v(i),μ~x(i)) so that they can elaborate feedback predictions.”
Quote in the paperSubstantial claim- Rests on an assumption: Superficial pyramidal cells provide feedforward connections, while deep pyramidal cells provide feedback connections.
May combine two claims.
InterpretationreasoningThis smoothing leads to an asymmetry in frequency content: superficial cells should express more gamma relative to beta, and deep cells should express more beta relative to gamma.
“This smoothing – inherent in Bayesian filtering – leads to an asymmetry in frequency content of superficial and deep cells: for example, superficial cells should express more gamma relative to beta, and deep cells should express more beta relative to gamma (Roopun et al., 2006, 2008; Maier et al., 2010).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Roopun et al., 2006, 2008
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Maier et al., 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningConverting prediction errors into predictions necessarily entails a loss of high frequencies, but the nonlinearity in the mapping from predictions to prediction errors means high frequencies can be created.
“This means that the conversion of prediction errors into predictions (Bayesian filtering) necessarily entails a loss of high frequencies. However, the nonlinearity in the mapping from predictions to prediction errors means that high frequencies can be created (consider the effect of squaring a sine wave, which would convert beta into gamma).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningInhibitory interneurons in the granular layer are associated with prediction errors on hidden states.
“This leaves the inhibitory interneurons in the granular layer, which, for symmetry, we associate with prediction errors on the hidden states.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThere is a remarkable correspondence between the form of Equation (1) and the connectivity of the canonical microcircuit, and the resulting scheme corresponds almost exactly to the computational architecture proposed by Mumford (1992).
“Figure 5 illustrates a remarkable correspondence between the form of Equation (1) and the connectivity of the canonical microcircuit. Furthermore, the resulting scheme corresponds almost exactly to the computational architecture proposed by Mumford (1992).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Mumford (1992) source
- Rests on an assumption: A cortical column contains the circuitry needed for the relevant computations, and this circuitry is replicated with only minor variations across cortex.
May combine two claims.
InterpretationreasoningTop-down predictions produce prediction errors through subtraction, so feedback connections should exert inhibitory effects, as observed empirically.
“Another key feature of Equation (1) is that the top-down predictions produce prediction errors through subtraction. In other words, feedback connections should exert inhibitory effects, of the sort seen empirically.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningBy minimising surprise through selecting appropriate sensations, the brain implicitly maximises the evidence for its own existence, a process known as active inference.
“These arguments mean that by minimising surprise, through selecting appropriate sensations, the brain is implicitly maximising the evidence for its own existence – this is known as active inference.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThere are two exceptions involving connections to inhibitory cells in the granular layer; predictive coding requires interlaminar projections from supragranular inhibitory and infragranular excitatory cells, which should be of a feedback sort.
“However, there are two exceptions that both involve connections to the inhibitory cells in the granular layer (shown as dotted lines in Figure 5). Predictive coding requires that these cells (that encode prediction errors on hidden states) compare the expected changes in hidden states with the actual changes. This suggests that there should be interlaminar projections from supragranular (inhibitor”
Quote in the paperSubstantial claim
The paper relies on this without support.
In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
“In predictive coding, feedforward connections convey prediction errors, while feedback connections convey predictions from higher cortical areas to suppress prediction errors in lower areas.”
The paper relies on this without support.
Top-down predictions are context sensitive.
“This is because top-down predictions are necessarily context sensitive: e.g., the occlusion of one visual object by another.”
The authors state this as an assumption.
Each canonical microcircuit is distinct from its neighbours and can carry out computations in parallel.
“The notion of a canonical microcircuit implicitly assumes that each circuit is distinct from its neighbours; that could presumably carry out computations in parallel.”
The paper relies on this without support.
A cortical column contains the circuitry needed for the relevant computations, and this circuitry is replicated with only minor variations across cortex.
“Central to the idea of a canonical microcircuit is the notion that a cortical column contains the circuitry necessary to perform requisite computations, and that these circuits can be replicated with minor variations throughout the cortex.”
The paper relies on this without support.
Superficial pyramidal cells provide feedforward connections, while deep pyramidal cells provide feedback connections.
“Second, we know that the superficial and deep pyramidal cells provide feedforward and feedback connections respectively.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Descending activity, carried by alpha/beta-band activity, is strongest in layers 5/6.
“Consistent with the proposed laminar profile of PP dynamics, evidence from monkey and human studies indicate that gamma‐band activity predominantly emanates from forward projections in superficial layers, while descending activity, carried by alpha/beta‐band activity, is strongest in layers 5/6.”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [40]
The Introduction says deep layers prefer alpha/beta frequencies and that feedback connections originate predominantly there. In “Segregation of processing streams,” however, the paper calls low-frequency feedback signalling a proposal and says experimental support was available at least for feedforward connections. It therefore does not establish as firmly as the claim suggests that descending activity is carried by alpha/beta activity or is strongest specifically in layers 5/6. (LLM, conf=0.83)
Experimental — accuracy not yet measuredEvidence from monkey and human studies indicates that gamma-band activity predominantly emanates from forward projections in superficial layers.
“Consistent with the proposed laminar profile of PP dynamics, evidence from monkey and human studies indicate that gamma‐band activity predominantly emanates from forward projections in superficial layers, while descending activity, carried by alpha/beta‐band activity, is strongest in layers 5/6.”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [40]
The Introduction and “Segregation of processing streams” report predominant gamma activity in superficial layers, where feedforward connections originate, and cite experimental support for higher-frequency feedforward signaling. The passages describe monkey findings but do not establish that the claimed laminar and feedforward pattern was also shown in human studies. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAccumulating evidence suggests a role for superficial pyramidal cells in representation (prediction) and error coding, while L5 cells may function especially in representation coding.
“Accumulating evidence suggests a role for superficial pyramidal cells in representation (prediction) and error coding, while L5 cells may function especially in representation coding.23,118,135”
From An integrative, multiscale view on neural theories of consciousness (2024) · cites this paper as [23]
In “The cortical microcircuit and predictive coding” (page unavailable), the paper assigns prediction-error coding to superficial pyramidal cells and conditional-expectation coding to deep pyramidal cells, consistent with the claim about L5 representation coding. It assigns expectation coding in superficial layers to excitatory interneurons, not superficial pyramidal cells, so the claimed dual role for superficial pyramidal cells is not supported. (LLM, conf=0.88)
Experimental — accuracy not yet measuredPP-based accounts, including NREP, propose that conscious vision begins with the interaction between the feedforward signaling stream (mainly conveying error information) and the recurrent stream (conveying top-down predictions) within the visual cortical hierarchy, although alternative schemes are possible.
“PP-based accounts, including NREP, propose that conscious vision begins with the interaction between the feedforward signaling stream (mainly conveying error information) and the recurrent stream (conveying top-down predictions) within the visual cortical hierarchy, 23,141,135 although alternative schemes are possible (e.g., Heeger 162 ).”
From An integrative, multiscale view on neural theories of consciousness (2024) · cites this paper as [23]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredPredictions and incoming sensory input each have an associated precision (inverse variance) reflecting their confidence or reliability.
“According to these theories, predictions and incoming sensory input each have an associated precision (inverse variance) reflecting their confidence or reliability.”
From Precision weighting of cortical unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis (2021) · cites this paper as [6]
In “Predictive coding and message passing” (page unavailable), the paper says prediction errors are weighted by precision, defined as inverse variance, when top-down predictions are compared with sensory input. It does not explicitly assign a separate precision to each prediction and each incoming sensory input, or define those precisions as their respective confidence or reliability. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Oscillatory signals have been related to predictive coding, with feedback signaling of predictions mediated predominantly by alpha/beta frequency bands and feedforward prediction error signaling by gamma-band activity.
“In the time-frequency domain, oscillatory signals have been related to predictive coding, with feedback signaling of predictions being mediated predominantly by the alpha/beta frequency bands and feedforward PE signaling by gamma-band activity (14, 35–37).”
From The Predictive Coding Account of Psychosis (2018) · cites this paper as [14]
The Introduction and “Functional asymmetries in the microcircuit” (page unavailable) link feedforward prediction errors to relatively high frequencies and feedback predictions to lower frequencies, with gamma and alpha/beta as examples. But the paper presents frequency-specific mediation as a suggestion, not an established predominant mechanism, and explicitly says predictive coding itself does not specify characteristic gamma or beta frequencies. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPredictive signals may be sent from higher hierarchical levels predominantly via glutamatergic NMDA receptor signaling, and disparity between prior belief and sensory data is signaled as a prediction error to higher levels mostly via glutamatergic AMPA receptors.
“In terms of neural implementation (14, 15), predictive signals may be sent from higher hierarchical levels predominantly via glutamatergic NMDA receptor signaling; any disparity between prior belief and sensory data is then signaled as a PE to the higher levels, mostly via glutamatergic AMPA receptors.”
From The Predictive Coding Account of Psychosis (2018) · cites this paper as [14]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe neurophysiological validity of variational free energy minimization, in terms of predictive coding and associative plasticity, has already been established in terms of neuronally plausible mechanisms.
“The neurophysiological validity of variational free energy minimization – in terms of predictive coding and associative plasticity – has already been established in terms of neuronally plausible mechanisms (Friston et al., 2006; Bastos et al., 2012).”
From Virtual reality and consciousness inference in dreaming (2014) · cites this paper as Bastos et al., 2012
The ‘Predictive coding and message passing’ section says its neuronal-network equations minimize variational free energy, and the Conclusion describes a correspondence between predictive coding and cortical microcircuit anatomy. But the Introduction calls the proposed neuronal roles speculative and the Conclusion discusses ways to test competing models; this does not establish neurophysiological validity. The passages also do not establish associative plasticity as a mechanism. Page numbers are unavailable in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredRecent theories have hypothesised that precision weighting of cortical unsigned prediction errors is mediated by neuromodulators (including dopamine), and link a malfunctioning dopamine system to psychosis through aberrant precision-weighting of these prediction errors.
“Recent theories [3, 4, 6, 9, 28] have hypothesised that precision weighting of cortical unsigned prediction errors is mediated by neuromodulators (including dopamine), and link a malfunctioning dopamine system to psychosis through aberrant precision-weighting of these prediction errors.”
From Precision weighting of cortical unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis (2021) · cites this paper as [6]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredHierarchical predictive coding suggests that hallucinations arise from disrupted balance between top-down expectations and bottom-up sensory inputs, with a stronger influence of expectations in people with hallucinations.
“hierarchical predictive coding, a cognitive model of psychosis, suggests that hallucinations arise from disrupted balance between top-down expectations and bottom-up sensory inputs, with a stronger influence of expectations observed in people with hallucinations11–13”
From Deviations in effective connectivity explain different hallucination subtypes in Parkinson's disease psychosis (2026) · cites this paper as [12]
The “Predictive coding and message passing” section (page unavailable) describes top-down expectations generating predictions and bottom-up prediction errors updating those expectations. The passages do not connect this mechanism to hallucinations or psychosis, or report a stronger influence of expectations in people with hallucinations. (LLM, conf=0.86)
Experimental — accuracy not yet measuredOscillatory dynamics play a role in regulating cortical processing, especially the gamma (40 to 90 Hz) and alpha/beta (10 to 30 Hz) bands.
“But there is mounting evidence that oscillatory dynamics play a role in regulating cortical processing and thus can also play a role, especially the gamma (40 to 90 Hz) and alpha/beta (10 to 30 Hz) bands (1, 18–25).”
From Layer and rhythm specificity for predictive routing (2020) · cites this paper as [19]
The Introduction and ‘Segregation of processing streams’ describe gamma oscillations in superficial cortical layers and lower alpha/beta frequencies in deep layers, and suggest that these frequencies contribute differently to cortical signalling. Our copy does not specify the claimed 40–90 Hz and 10–30 Hz ranges, so it supports the general point but not those numerical bounds. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIntrinsic connectivity controls the gain of neuronal populations broadcasting prediction errors.
“This is thought to be implemented by intrinsic connectivity that controls the gain of neuronal populations broadcasting prediction errors (Friston, 2008, Bastos et al., 2012).”
From The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding (2014) · cites this paper as Bastos et al., 2012
In 'The cortical microcircuit and predictive coding' (page unavailable), the paper says superficial pyramidal cells broadcast prediction errors. In 'The anatomy and physiology of extrinsic connections' (page unavailable), it says attention increases the gain of populations encoding prediction error. Our copy does not establish the further claim that intrinsic connectivity controls that gain. (LLM, conf=0.76)
Experimental — accuracy not yet measuredSuperficial pyramidal cells convey prediction errors via extrinsic forward ascending connections targeting spiny stellate cells, while deep pyramidal cells convey predictions via extrinsic backward descending connections that target superficial pyramidal cells.
“Superficial pyramidal cells convey prediction errors via extrinsic forward ascending connections (targeting spiny stellate cells), while deep pyramidal cells are thought to convey predictions, via extrinsic backward descending connections that target superficial pyramidal cells (Mumford, 1992, Friston, 2008, Bastos et al., 2012).”
From The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding (2014) · cites this paper as Bastos et al., 2012
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredPrecision is encoded by the post-synaptic gain of neurons that report prediction errors, and sensory processing channels that convey precise information are selectively enabled by an increase in their precision.
“Precision is thought to be encoded by the post-synaptic gain of neurons that report prediction errors and has been used to explain both the psychophysical and electrophysiological correlates of attention, so that sensory processing channels that convey precise information are selectively enabled by an increase in their precision (Friston, 2008, Feldman and Friston, 2010, Bastos et al., 2012).”
From The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding (2014) · cites this paper as Bastos et al., 2012
In “Predictive coding and message passing” (page unavailable), the paper says prediction errors are weighted by precision. In “The anatomy and physiology of extrinsic connections” (page unavailable), it says attention increases the gain of populations encoding prediction error. These passages support the proposed mechanism, but do not explicitly say that precision is encoded by postsynaptic gain or that increasing precision selectively enables channels carrying precise information. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA detailed model by Bastos and colleagues formalised this, assigning biophysical neuron models to the roles of error units and prediction units and demonstrating consistency with observed cortical beta and gamma rhythms.
“A detailed model by Bastos and colleagues (20) formalised this, assigning biophysical neuron models to the roles of error units and prediction units and demonstrating consistency with observed cortical beta and gamma rhythms.”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [20]
In “The cortical microcircuit and predictive coding” (page unavailable), the paper assigns prediction-error and prediction-related roles to cortical cell populations. In “Functional asymmetries in the microcircuit” (page unavailable), it derives a relative high-versus-low-frequency prediction consistent with reported gamma and beta patterns. It does not assign biophysical neuron models to those roles or demonstrate that a biophysical model reproduces the observed rhythms. (LLM, conf=0.88)
Experimental — accuracy not yet measuredPredictions are conveyed from deep pyramidal cells by backward connections to target the superficial pyramidal cells encoding prediction error.
“predictions are conveyed from deep pyramidal cells, by backward (descending) connections, to target the superficial pyramidal cells encoding prediction error (Mumford, 1992; Bastos et al., 2012)”
From The Computational Anatomy of Psychosis (2013) · cites this paper as Bastos et al., 2012
A central postulate of PP is that prediction error minimization is coordinated between two functionally distinct prediction and error unit subpopulations, which propagate signals across different frequency bands and cortical layers.
“A central postulate of PP is that prediction error minimization is coordinated between two functionally distinct prediction and error unit subpopulations, which propagate signals across different frequency bands and cortical layers.”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [40]
PP associates the oscillatory segregation between forward and backward processing with the distinct subpopulations housing error and expectation units, suggesting that oscillatory signatures of error and prediction message passing should be dissociable.
“PP associates this oscillatory segregation between forward and backward processing 171 with the distinct subpopulations housing error and expectation units, suggesting that oscillatory signatures of error and prediction message passing should be dissociable.”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [40]
Superficial pyramidal cells are thought to encode prediction error.
“This is potentially important because – as noted above – superficial pyramidal cells are thought to encode prediction error (Bastos et al., 2012).”
From Virtual reality and consciousness inference in dreaming (2014) · cites this paper as Bastos et al., 2012
These descending predictions are compared with lower-level representations to form a prediction error.
“These descending predictions are compared with lower-level representations to form a prediction error (usually associated with the activity of superficial pyramidal cells).”
From Active interoceptive inference and the emotional brain (2016) · cites this paper as [23]
This mismatch or difference signal is passed back up the hierarchy, to update higher representations.
“This mismatch or difference signal is passed back up the hierarchy, to update higher representations (usually associated with the activity of deep pyramidal cells).”
From Active interoceptive inference and the emotional brain (2016) · cites this paper as [23]
Specific functional roles have been proposed for different frequency bands and cell types, with beta and gamma corresponding to respective predictions and prediction-errors from deep and superficial pyramidal neurons.
“Notably with respect to the present discussion—and as a source of empirical support for HPP—specific functional roles have been proposed for different frequency bands and cell types, with beta and gamma corresponding to respective predictions and prediction-errors from deep and superficial pyramidal neurons (Bastos et al., 2012; Chao et al., 2018; Scheeringa and Fries, 2019).”
From An Integrated World Modeling Theory (IWMT) of Consciousness (2020) · cites this paper as Bastos et al., 2012
According to some more recent formulations of predictive coding, the propagated error is weighted by the sensory reliability or precision.
“According to some more recent formulations of predictive coding [111,112], the propagated error is weighted by the sensory reliability or precision.”
From How Do Expectations Shape Perception? (2018) · cites this paper as [112]
When sensory precision is low, prediction errors are downweighted and observers rely more on predictions; conversely, when it is high, prediction errors are up-weighted and observers rely more strongly on their input.
“When sensory precision is low, prediction errors are downweighted and observers will rely more on predictions.Conversely, when it is high, prediction errors are up-weighted and observers will rely more strongly on their input.”
From How Do Expectations Shape Perception? (2018) · cites this paper as [112]
The most neurobiological plausible instance of predictive sampling is predictive coding, particularly of a hierarchical sort.
“The most neurobiological plausible instance of this predictive sampling is known as predictive coding; particularly of a hierarchical sort (Srinivasan, Laughlin and Dubs, 1982; Rao and Ballard, 1999; Bastos et al., 2012; Clark, 2013; Hohwy, 2013).”
From Virtual reality and consciousness inference in dreaming (2014) · cites this paper as Bastos et al., 2012
Hierarchical predictive coding provides a metaphor for recurrent message passing between levels of the cortical hierarchy and accounts for many anatomical and physiological aspects of extrinsic connections in the brain.
“This provides a nice metaphor for the recurrent message passing between levels of the cortical hierarchy – and accounts for many anatomical and physiological aspects of extrinsic (long-range) connections in the brain (Lee and Mumford, 2003; Friston, 2008; Bastos et al., 2012).”
From Virtual reality and consciousness inference in dreaming (2014) · cites this paper as Bastos et al., 2012
There is now a large body of circumstantial evidence for predictive coding in the brain, both in terms of hierarchical cortical architectures and the canonical microcircuits required to construct predictions.
“There is now a large body of circumstantial evidence for predictive coding in the brain, both in terms of hierarchical cortical architectures and the canonical microcircuits required to construct predictions (Bastos et al., 2012; Adams et al., 2013a).”
From Virtual reality and consciousness inference in dreaming (2014) · cites this paper as Bastos et al., 2012
Prediction errors are encoded by firing rates of superficial pyramidal cells in the upper layers of the cortex.
“In brief, current thinking suggests that prediction errors are encoded by firing rates of superficial pyramidal cells in the upper layers of the cortex.”
From Virtual reality and consciousness inference in dreaming (2014) · cites this paper as Bastos et al., 2012
Predictions are conveyed by top-down or descending backward connections from deep pyramidal cells encoding expectations or beliefs about hidden states in the world causing sensory impressions.
“The predictions, in turn, are conveyed by top-down or descending backward connections from deep pyramidal cells encoding expectations or beliefs about (hidden) states in the world causing sensory impressions (Mumford, 1992; Bastos et al., 2012).”
From Virtual reality and consciousness inference in dreaming (2014) · cites this paper as Bastos et al., 2012
Prediction error signals update prior beliefs in a manner weighted by their associated precision, such that more is learned from precise and reliable prediction errors compared with noisy and unreliable prediction errors.
“Such prediction error signals update prior beliefs in a manner that is weighted by their associated precision, such that more is learned from precise and reliable prediction errors compared with noisy and unreliable prediction errors [2, 4, 6].”
From Precision weighting of cortical unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis (2021) · cites this paper as [6]
Bastos et al. (2012) addressed the issue of specifying how proposed neural computation is accomplished using more realistic cortical neurons and circuitry by presenting equations that implement predictive coding dynamics and matching terms in the equations to neural sub-types in different layers.
“A paper by Bastos et al. (2012) addressed this very issue by first presenting the set of equations that implement the dynamics of predictive coding and then matching the terms in the equations to the neural sub-types in the different layers.”
From Is predictive coding theory articulated enough to be testable? (2015) · cites this paper as Bastos et al. (2012)
Bastos et al. pointed out that feedback connections can both facilitate and suppress firing in lower hierarchical areas.
“Moreover, they also pointed out that (page 699) “feedback connections can both facilitate and suppress firing in lower hierarchical areas.””
From Is predictive coding theory articulated enough to be testable? (2015) · cites this paper as Bastos et al. (2012)
Using Bayesian logic, a brain uses pattern completion to decide among simulations and implement one based on predicted maintenance of physiological efficiency across multiple body systems.
“Using Bayesian logic (Deneve, 2008; Bastos et al., 2012), a brain uses pattern completion to decide among simulations and implement one of them (Gallivan et al., 2016), based on predicted maintenance of physiological efficiency across multiple body systems”
From The theory of constructed emotion: an active inference account of interoception and categorization (2016) · cites this paper as Bastos et al., 2012
Rhythm-based models suggest that deep-layer cortex (layers 5 and 6) feedback predictions using alpha/beta.
“Deep-layer cortex (layers 5 and 6) feedback predictions using alpha/beta (1, 19).”
From Layer and rhythm specificity for predictive routing (2020) · cites this paper as [19]
Rhythm-based models suggest that superficial cortical layers (layers 2 and 3) feedforward prediction errors using gamma.
““Rhythm-based” models suggest that superficial cortical layers (layers 2 and 3) feedforward prediction errors using gamma.”
From Layer and rhythm specificity for predictive routing (2020) · cites this paper as [19]
Extrapolating from rhythm-based models of predictive coding, the authors propose a framework called predictive routing (PR).
“Extrapolating from the rhythm-based models of predictive coding (1, 19), this suggests a framework we call predictive routing (PR) (Fig. 6).”
From Layer and rhythm specificity for predictive routing (2020) · cites this paper as [19]
Predictive coding is enabled by a complex wiring of cortical loops.
“Predictive coding is enabled by a complex wiring of cortical loops.”
From Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review. (2025) · cites this paper as Bastos et al., 2012
Feedforward projections send bottom-up sensory inputs and predictive errors from lower to higher brain regions, while feedback processing sends top-down inferences.
“While feedforward projections send bottom-up sensory inputs and pre dictive errors from lower to higher brain regions, feedback processing sends top-down inferences.”
From Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review. (2025) · cites this paper as Bastos et al., 2012
Superficial pyramidal cells encode prediction error.
“Superficial pyramidal cells are thought to encode prediction error (Friston, 2008, Bastos et al., 2012).”
From The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding (2014) · cites this paper as Bastos et al., 2012
In predictive coding, top-down predictions are generated and compared with bottom-up sensory inputs to produce prediction errors.
“In predictive coding top-down predictions (conditional expectations) are generated and compared with bottom-up sensory inputs to produce prediction errors.”
From The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding (2014) · cites this paper as Bastos et al., 2012
Prediction errors weighted in proportion to their expected precision are used to update higher level expectations, which in turn reduce lower-level prediction errors.
“Prediction errors that are weighted in proportion to their expected precision are used to update higher level expectations, which in turn reduce lower-level prediction errors (Friston, 2008, Bastos et al., 2012).”
From The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding (2014) · cites this paper as Bastos et al., 2012
An interesting difference to the implied message passing in predictive coding proposals is that the HGF architecture requires not only precision-weighted prediction errors to be sent bottom-up between nodes, but also estimates of prediction precision which serve to update belief precision in the higher-level node.
“An interesting difference to the implied message passing in predictive coding proposals (Bastos et al., 2012; Shipp, 2016) arises from the update step: The HGF architecture requires that not only (precision-weighted) prediction errors are being sent bottom-up between nodes, but also estimates of prediction precision ( or γa) which serve to update belief precision in the higher-level node.”
From The generalized Hierarchical Gaussian Filter (2026) · cites this paper as Bastos et al., 2012
By considering nonlinear value coupling and deriving its message passing scheme, the paper enables formal comparison to other hierarchical Bayesian inference architectures, including predictive coding and active inference belief propagation.
“Finally, by considering the case of nonlinear value coupling and deriving the message passing scheme implied by this, we enable a formal comparison to other proposed architectures for hierarchical Bayesian inference, most prominently Bayesian (or generalized) predictive coding (Bastos et al., 2012; Shipp, 2016) and belief propagation in active inference (e.g., Fig. 5.1 in Parr et al., 2022).”
From The generalized Hierarchical Gaussian Filter (2026) · cites this paper as Bastos et al., 2012
It is currently not clear whether the inference networks of predictive coding and hierarchical filtering would place distinct requirements on implementation or make distinct predictions about neural readouts.
“While the message passing architecture implied by different predictive coding models has been examined in detail and partly matched with neuroanatomy and -physiology (for overviews, see Spratling, 2019; Keller & Mrsic-Flogel, 2018; Bastos et al., 2012; Shipp, 2016), and hierarchical filtering models share many similarities with predictive coding models, it is currently not clear whether the”
From The generalized Hierarchical Gaussian Filter (2026) · cites this paper as Bastos et al., 2012
The laminar asymmetry of forward versus backward connectivity is based on the canonical microcircuit model described by Bastos et al.
“The laminar asymmetry of forward versus backward connectivity is based on the canonical microcircuit model described by Bastos et al. 23”
From Alzheimer's disease and its progression reduce pyramidal cell gain and connectivity (2025) · cites this paper as [23]
Predictive coding depends on the hierarchical structure of neurons.
“Therefore, predictive coding is thought to depend on the hierarchical structure of neurons (39).”
From A Predictive Coding Perspective on Mismatch Negativity Impairment in Schizophrenia (2020) · cites this paper as [39]
The hierarchical organisation of populations in layers and areas naturally lends itself to the hierarchical Bayesian structure of predictive coding.
“Importantly, the hierarchical organisation – populations organised in layers and areas – naturally lends itself to the hierarchical Bayesian structure of predictive coding (20).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [20]
Neuroanatomical studies have found a remarkable correspondence between the connectivity of cortical microcircuits and the connections implied by predictive coding theories.
“Supporting this model, neuroanatomical studies have found a remarkable correspondence between the connectivity of cortical microcircuits and the connections implied by predictive coding theories (20).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [20]
The mismatch signal is passed back up the hierarchy to update higher representations associated with deep pyramidal cells.
“The ensuing mismatch signal is passed back up the hierarchy, to update higher representations (associated with the activity of deep pyramidal cells).”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [17]
Hierarchical predictive coding explains the hierarchical nature of cortical cartography, the prevalence of backward connections, and functional and structural asymmetries in extrinsic connections linking hierarchical levels.
“In brief, it explains the hierarchical nature of cortical cartography; the prevalence of backward connections and many of the functional and structural asymmetries in the extrinsic connections that link hierarchical levels.”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [17]
The recursive exchange of signals suppresses prediction error at each and every level to provide a hierarchical explanation for sensory inputs.
“This recursive exchange of signals suppresses prediction error at each and every level to provide a hierarchical explanation for sensory inputs that enter at the lowest (sensory) level.”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [17]
Top-down predictions are compared with representations at the lower level to form a prediction error associated with superficial pyramidal cells.
“These top-down predictions are compared with representations at the lower level to form a prediction error (associated with the activity of superficial pyramidal cells).”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [17]
In predictive coding, neuronal representations in higher levels of cortical hierarchies generate predictions of representations in lower levels.
“In these schemes, neuronal representations in higher levels of cortical hierarchies generate predictions of representations in lower levels.”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [17]
The descending predictions are effectively inhibitory, consistent with targeting of inhibitory interneurons (particularly in layer one) by backward connections in visual cortex.
“Note also that the descending predictions are effectively inhibitory, which is consistent with the targeting of inhibitory interneurons (particularly in layer one) by backward connections in the visual cortex [17].”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [17]
The brain performs hierarchical predictive coding, in which inferences about first-order content and second-order context are orchestrated.
“In this paper, we have considered how inferences about first-order content and second-order context are orchestrated in hierarchical predictive coding, highlighting the importance of modulatory effects by second-order representations—such as precision and saliency—in optimizing inference.”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [17]
All current analyses of canonical microcircuits place prediction error units in superficial layers.
“All current analyses of canonical microcircuits place prediction error units in superficial layers [17–19].”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [17]
Conditional expectations are encoded by deep pyramidal cells (representation units) at each cortical level, which convey predictions downward to suppress errors at the level below.
“Conditional expectations are thought to be encoded by deep pyramidal cells (i.e., representation units) at each level of the cortical hierarchy that convey predictions downward to suppress errors at the level below”
From The depressed brain: an evolutionary systems theory (2017) · cites this paper as [10]
Prediction errors are encoded by superficial pyramidal cells (error units) that convey errors forward to revise expectations at the level above.
“while prediction errors are encoded by superficial pyramidal cells (i.e., error units) that convey errors forward to revise expectations at the level above [10].”
From The depressed brain: an evolutionary systems theory (2017) · cites this paper as [10]
The brain's functional organization is hierarchical.
“The hierarchical nature of the brain’s functional organization has long been recognized113, 114”
From Imaging structural and functional brain development in early childhood (2018) · cites this paper as [113]
At the single-cell spiking level, a subset of neurons increases firing rate just prior (~1 s) to locomotion onset.
“At the level of single-cell spiking, an increase in firing rate can be observed in a subset of neurons just prior (~1 s) to the onset of locomotion”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Bastos et al., 2012
Radical predictive processing posits that cognition is accomplished by a canonical, ubiquitous microcircuit motif replicated across all sensory and cognitive domains, in which specific classes of neurons reciprocally pass predictions and prediction errors across the global neuronal hierarchy.
“Instead, cognition is accomplished by a canonical, ubiquitous microcircuit motif replicated across all sensory and cognitive domains in which specific classes of neurons reciprocally pass predictions and prediction errors across the global neuronal hierarchy (Bastos et al. 2012; Douglas et al. 1989).”
From From cognitivism to autopoiesis: towards a computational framework for the embodied mind (2018) · cites this paper as Bastos et al. 2012
The findings are compatible with feedback of predictive information to the earliest visual input level, in line with predictive coding models.
“Our findings are compatible with feedback of predictive information to the earliest visual input level (e.g., [6]), in line with predictive coding models [7–10].”
From Decoding sound and imagery content in early visual cortex (2014) · cites this paper as [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Stated column widths do not span three orders of magnitude
MinorThe stated widths run from roughly 50 micrometers for minicolumns to 1 mm (1,000 micrometers) for hypercolumns-a factor of about 20, not 1,000.
“The most likely candidate for this spatial scale is the cortical column – that can vary over three orders of magnitude between minicolumns, columns, and hypercolumns. Minicolumns are only a few cells wide, estimated to be about 50-60 micrometers in diameter by Mountcastle (1997)”
“Finally, Hubel and Wiesel defined a hypercolumn to be the unit of cortex necessary to traverse all possible values of a particular receptive field property, such as orientation or eye dominance – estimated to be between 0.5 to 1 mm wide (Hubel and Wiesel, 1974).”
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Self-citationThe Bayesian brain: The role of uncertainty in neural coding and computation
Knill, D. C., & Pouget, A. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.10.007 · Cited by 2,588 · cited 2× · see citing works
“…maximization of Bayesian model evidence provides an important link to the Bayesian brain hypothesis (Hinton and van Camp, 1993; Dayan et al., 1995; Knill and Pouget, 2004) and many other compelling proposals about perceptual synthesis, including analysis by synthesis (Neisser, 1967; Yuille and…”
Neuronal Synchrony: A Versatile Code for the Definition of Relations?
Singer, W. (1999) · Neuron · doi:10.1016/s0896-6273(00)80821-1 · Cited by 2,581 · cited 2× · see citing works
“anatomical segregation into parallel streams, the question naturally arises, how are these streams integrated? It has been previously suggested that integration occurs through the synchronized firing of multiple neurons that form a neural ensemble (Gray et al., 1989; Singer, 1999), while others have emphasized inter-a…”
BackgroundMODALITY AND TOPOGRAPHIC PROPERTIES OF SINGLE NEURONS OF CAT'S SOMATIC SENSORY CORTEX
Mountcastle, V. B. (1957) · Journal of Neurophysiology · doi:10.1152/jn.1957.20.4.408 · Cited by 2,411 · cited 1× · see citing works
“A cortical column was classically defined as a vertical alignment of cells containing neurons with similar receptive field properties, such as orientation preference and ocular dominance in V1 or touch in somatosensory cortex (Mountcastle, 1957; Hubel and Wiesel, 1972).”
Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices
Buschman, T. J., & Miller, E. K. (2007) · Science · doi:10.1126/science.1138071 · Cited by 2,380 · cited 2× · see citing works
“See Buschman and Miller (2007), Saalmann et al. (2007), Anderson et al. (2011), and Armstrong et al. (2012) for further discussion of top-down connections in attention.”
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition
Wang, X.-J. (2010) · Physiological Reviews · doi:10.1152/physrev.00035.2008 · Cited by 1,939 · see citing works
NEURONAL CIRCUITS OF THE NEOCORTEX
Douglas, R. J., & Martin, K. A. C. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144152 · Cited by 1,886 · cited 1× · see citing works
“This section reviews laminar-specific connections that underlie the notion of a canonical microcircuit (Douglas et al., 1989; Douglas and Martin, 1991, 2004).”
The neural mechanisms of top-down attentional control
Hopfinger, J. B., Buonocore, M. H., & Mangun, G. R. (2000) · Nature Neuroscience · doi:10.1038/72999 · Cited by 1,876 · cited 2× · see citing works
“Hupé et al. (1998) cooled area V5/MT while recording from areas V1, V2, and V3 in the monkey.”
Self-citationEntrainment of Neuronal Oscillations as a Mechanism of Attentional Selection
Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I., & Schroeder, C. E. (2008) · Science · doi:10.1126/science.1154735 · Cited by 1,719 · cited 1× · see citing works
“Indeed, there is empirical evidence to support this notion in the visual (Lakatos et al., 2008; Meirovithz et al., 2012; Bosman et al., 2012) and motor (Gwin and Ferris, 2012) domain.”
Function of the thalamic reticular complex: the searchlight hypothesis
Crick, F. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.14.4586 · Cited by 1,651 · cited 2× · see citing works
“In addition, the thalamic reticular nucleus has been proposed to mediate the inhibition that might underlie cross-modal attention or top-down predictions (Yamaguchi and Knight, 1990; Crick, 1984; Wurtz et al., 2011).”
The mismatch negativity: a review of underlying mechanisms
Garrido, M. I., Kilner, J. M., Stephan, K. E., & Friston, K. J. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.11.029 · Cited by 1,441 · cited 2× · see citing works
“are enhanced relative to predicted stimuli (Garrido et al., 2009).”
Self-citationAttention, uncertainty, and free-energy
Feldman, H., & Friston, K. J. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00215 · Cited by 1,365 · cited 4× · see citing works
“…suppression of responses to predictable stimuli can coexist with (top-down) attentional enhancement of evoked processing (Wyart et al., 2012): in predictive coding, attention is mediated by increasing the gain of populations encoding prediction error (Spratling, 2008; Feldman and Friston, 2010).”
InfluentialSelf-citationPossible Principles Underlying the Transformations of Sensory Messages
Barlow, H. B. (2012) · The MIT Press eBooks · doi:10.7551/mitpress/9780262518420.003.0013 · Cited by 1,318 · see citing works
Neural syntax: cell assemblies, synapsembles, and readers
Buzsáki, G. (2010) · Neuron · doi:10.1016/j.neuron.2010.09.023 · Cited by 1,289 · cited 2× · see citing works
“This view has re-emerged in recent years, with the development of the requisite recording and analytic techniques for evaluating this proposal (Buzsáki, 2010; Canolty et al., 2010; Singer et al., 1997; Lopes-dos-Santos et al., 2011).”
Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995) · Neural Computation · doi:10.1162/neco.1995.7.5.889 · Cited by 1,207 · cited 5× · see citing works
“Indeed, this led Geoffrey Hinton and colleagues to propose that the brain is an inference (Helmholtz) machine (Hinton and Zemel, 1994; Dayan et al., 1995).”
InfluentialHigh-frequency, long-range coupling between prefrontal and visual cortex during attention
Gregoriou, G. G., Gotts, S. J., Zhou, H., & Desimone, R. (2009) · Science · doi:10.1126/science.1171402 · Cited by 1,194 · cited 1× · see citing works
“…and feedback signaling may be mediated by distinct (high and low) frequencies (reviewed in Wang, 2010; see also Buschman and Miller, 2007), a proposal that has recently received experimental support, at least for the feedforward connections (Bosman et al., 2012; see also Gregoriou et al., 2009).”
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London, M., & Häusser, M. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135703 · Cited by 1,149 · cited 3× · see citing works
“Some authors emphasize that even within a dendrite, there are all the necessary biophysical mechanisms for performing surprisingly advanced computations, such as direction selectivity, coincidence detection, or temporal integration (Häusser and Mel, 2003; London and Häusser, 2005).”
BackgroundOn the computational architecture of the neocortex: II The role of cortico-cortical loops
Mumford, D. (1992) · Biological Cybernetics · doi:10.1007/bf00198477 · Cited by 1,074 · cited 6× · see citing works
“Furthermore, the resulting scheme corresponds almost exactly to the computational architecture proposed by Mumford (1992).”
InfluentialThe functional logic of cortical connections
Zeki, S., & Shipp, S. (1988) · Nature · doi:10.1038/335311a0 · Cited by 1,067 · cited 8× · see citing works
“Distinct (laminar-specific) extrinsic connections (Zeki and Shipp, 1988; Felleman and Van Essen, 1991; Barone et al., 2000; Vezoli et al., 2004; Markov et al., 2011).”
InfluentialPredictive coding: a fresh view of inhibition in the retina
Srinivasan, M. V., Laughlin, S. B., & Dubs, A. (1982) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1982.0085 · Cited by 1,042 · cited 2× · see citing works
“In terms of implementation, predictive coding is, arguably, the most plausible neurobiological candidate for making these inferences (Srinivasan et al., 1982; Mumford, 1992; Rao and Ballard, 1999).”
Neural mechanisms for visual memory and their role in attention
Desimone, R. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.24.13494 · Cited by 1,028 · cited 2× · see citing works
“The suppression of predictable responses can also be regarded as repetition suppression, observed in single-unit recordings from the inferior temporal cortex of macaque monkeys (Desimone, 1996).”
Sequence regularity and geometry of orientation columns in the monkey striate cortex
Hubel, D. H., & Wiesel, T. N. (1974) · The Journal of Comparative Neurology · doi:10.1002/cne.901580304 · Cited by 1,027 · cited 2× · see citing works
“Finally, Hubel and Wiesel defined a hypercolumn to be the unit of cortex necessary to traverse all possible values of a particular receptive field property, such as orientation or eye dominance, estimated to be between 0.5 and 1 mm wide (Hubel and Wiesel, 1974).”
InfluentialThe pulvinar regulates information transmission between cortical areas based on attention demands
Saalmann, Y. B., Pinsk, M. A., Wang, L., Li, X., & Kastner, S. (2012) · Science · doi:10.1126/science.1223082 · Cited by 1,016 · cited 1× · see citing works
“Furthermore, computational considerations and recent experimental findings point to a potentially important role for higher-order thalamic nuclei in coordinating and synchronizing cortical responses (Vicente et al., 2008; Saalmann et al., 2012).”
Keeping the neural networks simple by minimizing the description length of the weights
Hinton, G. E., & van Camp, D. (1993) · Proceedings of the sixth annual conference on Computational learning theory - COLT '93 · doi:10.1145/168304.168306 · Cited by 994 · see citing works
Hierarchical Models in the Brain
Friston, K. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000211 · Cited by 952 · cited 11× · see citing works
“Forward (driving) and backward (driving and modulatory) connections mediate the (linear) influence of prediction errors and the (linear and nonlinear) construction of predictions (Friston, 2008, 2010).”
InfluentialSelf-citationLaminar and columnar distribution of geniculo‐cortical fibers in the macaque monkey
Hubel, D. H., & Wiesel, T. N. (1972) · The Journal of Comparative Neurology · doi:10.1002/cne.901460402 · Cited by 945 · cited 2× · see citing works
“A cortical column was classically defined as a vertical alignment of cells containing neurons with similar receptive field properties, such as orientation preference and ocular dominance in V1 or touch in somatosensory cortex (Mountcastle, 1957; Hubel and Wiesel, 1972).”
The columnar organization of the neocortex.
Mountcastle, V. B. (1997) · Brain : a journal of neurology · doi:10.1093/brain/120.4.701 · Cited by 941 · see citing works
On the computational architecture of the neocortex: I. The role of the thalamo-cortical loop
Mumford, D. (1991) · Biological Cybernetics · doi:10.1007/bf00202389 · Cited by 929 · cited 7× · see citing works
“prediction errors (deep pyramidal cells) (Mumford, 1992; Friston, 2008).”
InfluentialThe excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex
Lefort, S., Tomm, C., Floyd Sarria, J.-C., & Petersen, C. C. H. (2009) · Neuron · doi:10.1016/j.neuron.2008.12.020 · Cited by 875 · cited 2× · see citing works
“Recently, two studies have mapped the intrinsic connectivity of mouse sensory and motor cortices: Lefort et al. (2009) used multiple whole-cell recordings in mouse barrel cortex to determine the probability of monosynaptic connections and the corresponding connection strength.”
Advances in Neural Information Processing Systems 7
Tesauro, G., Touretzky, D. S., & Leen, T. K. (1995) · Cited by 869 · see citing works
Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons
Hupé, J. M., James, A. C., Payne, B. R., Lomber, S. G., Girard, P., & Bullier, J. (1998) · Nature · doi:10.1038/29537 · Cited by 864 · cited 4× · see citing works
“When visual stimuli were presented in the classical receptive field (CRF), cooling of area V5/MT decreased unit activity in earlier areas, suggesting an excitatory effect of extrinsic feedback (Hupé et al., 1998).”
InfluentialVision as Bayesian inference: analysis by synthesis?
Yuille, A., & Kersten, D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.002 · Cited by 849 · see citing works
Decreased response to novel stimuli after prefrontal lesions in man
Knight, R. T. (1984) · Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section · doi:10.1016/0168-5597(84)90016-9 · Cited by 828 · cited 2× · see citing works
“P300 ERPs in response to novel stimuli—as if there were a failure to communicate top-down predictions to sensory cortex (Knight, 1984).”
Gregory, R. L. (1980) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1980.0090 · Cited by 789 · cited 2× · see citing works
“This notion builds on a long history of proposals that the brain uses internal or generative models to make inferences about the causes of its sensorium (Helmholtz, 1860; Gregory, 1968, 1980; Dayan et al., 1995).”
Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment
Berkes, P., Orbán, G., Lengyel, M., & Fiser, J. (2011) · Science · doi:10.1126/science.1195870 · Cited by 782 · see citing works
On the actions that one nerve cell can have on another: distinguishing “drivers” from “modulators”
Sherman, S. M., & Guillery, R. W. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.12.7121 · Cited by 746 · cited 8× · see citing works
“Modulatory connections produce metabotropic and ionotropic responses when stimulated, evoke weak EPSPs, and show paired-pulse facilitation (Sherman and Guillery, 1998; 2011).”
InfluentialA functional microcircuit for cat visual cortex
Douglas, R. J., & Martin, K. A. (1991) · The Journal of Physiology · doi:10.1113/jphysiol.1991.sp018733 · Cited by 736 · cited 4× · see citing works
“Distinct (laminar-specific) neuronal responses (Douglas et al., 1989; Douglas and Martin, 1991).”
InfluentialAttentional stimulus selection through selective synchronization between monkey visual areas
Bosman, C. A., Schoffelen, J.-M., Brunet, N., Oostenveld, R., Bastos, A. M., Womelsdorf, T., Rubehn, B., Stieglitz, T., De Weerd, P., & Fries, P. (2012) · Neuron · doi:10.1016/j.neuron.2012.06.037 · Cited by 720 · see citing works
Self-citationNeural repetition suppression reflects fulfilled perceptual expectations
Summerfield, C., Trittschuh, E. H., Monti, J. M., Mesulam, M.-M., & Egner, T. (2008) · Nature Neuroscience · doi:10.1038/nn.2163 · Cited by 692 · cited 1× · see citing works
“The inhibitory effect of feedback connections is further supported by neuroimaging studies (Murray et al., 2002, 2006; Harrison et al., 2007; Summerfield et al., 2008, 2011; Alink et al., 2010).”
Could information theory provide an ecological theory of sensory processing?
Atick, J. J. (2011) · Network: Computation in Neural Systems · doi:10.3109/0954898x.2011.638888 · Cited by 679 · see citing works
Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: a new unifying principle
Larkum, M. E., Nevian, T., Sandler, M., Polsky, A., & Schiller, J. (2009) · Science · doi:10.1126/science.1171958 · Cited by 677 · cited 1× · see citing works
“In terms of the excitatory and modulatory effect of feedback connections, predictive input from higher cortical areas might have an important impact via the distal dendrites of pyramidal neurons (Larkum et al., 2009).”
Corticocortical connections in the visual system: structure and function
Salin, P. A., & Bullier, J. (1995) · Physiological Reviews · doi:10.1152/physrev.1995.75.1.107 · Cited by 639 · cited 1× · see citing works
“These are more difficult to study, because inhibitory cells are less common than excitatory cells, and because there are at least seven distinct morphological classes (Salin and Bullier, 1995).”
Structure and function of the cerebral cortex
Shipp, S. (2007) · Current Biology · doi:10.1016/j.cub.2007.03.044 · Cited by 638 · cited 2× · see citing works
“…one important route by which feedback connections could mediate selective inhibition is via their termination in L1 (Anderson and Martin, 2006; Shipp, 2007): layer 1 is sometimes referred to as acellular due to its pale appearance with Nissl staining (the classical method for separating…”
Dynamic causal modeling of evoked responses in EEG and MEG
David, O., Kiebel, S. J., Harrison, L. M., Mattout, J., Kilner, J. M., & Friston, K. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.10.045 · Cited by 620 · see citing works
Self-citationLaminar differences in gamma and alpha coherence in the ventral stream
Buffalo, E. A., Fries, P., Landman, R., Buschman, T. J., & Desimone, R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1011284108 · Cited by 606 · cited 3× · see citing works
“Finally, the spiking activity of neurons in the superficial layers of visual cortex are more coherent with gamma-frequency oscillations in the local field potential, while neurons in deep layers are more coherent with alpha-frequency oscillations (Buffalo et al., 2011).”
Self-citationLOCAL CIRCUITS IN PRIMARY VISUAL CORTEX OF THE MACAQUE MONKEY
Callaway, E. M. (1998) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.21.1.47 · Cited by 603 · cited 1× · see citing works
“Anatomical and functional data suggest that afferent input enters primarily into L4 and is conveyed to superficial layers L2/3 that are rich in pyramidal cells, which project forward to the next cortical area, forming a disynaptic route between thalamus and secondary cortical areas (Callaway, 1998).”
The cortical column: a structure without a function
Horton, J. C., & Adams, D. L. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1623 · Cited by 597 · cited 2× · see citing works
“Minicolumns were originally proposed as elementary units of cortex by Lorente de No (1949) and appear to reflect the migration of cells from the ventricular zone to the cortical sheet during fetal development (reviewed in Horton and Adams, 2005).”
Laminar Structure of Spontaneous and Sensory-Evoked Population Activity in Auditory Cortex
Sakata, S., & Harris, K. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.09.020 · Cited by 580 · cited 2× · see citing works
“This issue is important as distinct neuronal dynamics in different cortical layers are becoming increasingly apparent (de Kock et al., 2007; Sakata and Harris, 2009; Maier et al., 2010; Bollimunta et al., 2011).”
Evidence for a hierarchy of predictions and prediction errors in human cortex
Wacongne, C., Labyt, E., van Wassenhove, V., Bekinschtein, T., Naccache, L., & Dehaene, S. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1117807108 · Cited by 560 · cited 1× · see citing works
“These enhanced responses are thought to reflect an inability of higher cortical areas to predict, and thereby suppress, the activity of populations encoding prediction error (Garrido et al., 2007; Wacongne et al., 2011).”
Gain control by layer six in cortical circuits of vision
Olsen, S. R., Bortone, D. S., Adesnik, H., & Scanziani, M. (2012) · Nature · doi:10.1038/nature10835 · Cited by 539 · cited 5× · see citing works
“Suppression was also high in V1, around 80%–84% (Olsen et al., 2012).”
InfluentialA canonical microcircuit for neocortex
Douglas, R. J., Martin, K. A. C., & Whitteridge, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.480 · Cited by 514 · cited 4× · see citing works
“Distinct (laminar-specific) neuronal responses (Douglas et al., 1989; Douglas and Martin, 1991).”
InfluentialTrichromacy, opponent colours coding and optimum colour information transmission in the retina
Buchsbaum, G., & Gottschalk, A. (1983) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1983.0090 · Cited by 513 · cited 1× · see citing works
“The most popular scheme—for Bayesian filtering in neuronal circuits—is predictive coding (Srinivasan et al., 1982; Buchsbaum and Gottschalk, 1983; Rao and Ballard, 1999).”
A 4 Hz oscillation adaptively synchronizes prefrontal, VTA, and hippocampal activities
Fujisawa, S., & Buzsáki, G. (2011) · Neuron · doi:10.1016/j.neuron.2011.08.018 · Cited by 501 · cited 2× · see citing works
“, 1989; Singer, 1999), while others have emphasized inter-areal phase-synchronization or coherence (Varela et al., 2001; Fries, 2005; Fujisawa and Buzsáki, 2011).”
BackgroundShape perception reduces activity in human primary visual cortex
Murray, S. O., Kersten, D., Olshausen, B. A., Schrater, P., & Woods, D. L. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.192579399 · Cited by 496 · cited 1× · see citing works
“The inhibitory effect of feedback connections is further supported by neuroimaging studies (Murray et al., 2002, 2006; Harrison et al., 2007; Summerfield et al., 2008, 2011; Alink et al., 2010).”
Physiology of the Nervous System
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“, 1995; Knill and Pouget, 2004) and many other compelling proposals about perceptual synthesis – including analysis by synthesis (Neisser, 1967; Yuille and Kersten, 2006) epistemological automata (MacKay, 1956), the principle of minimum redundancy (Attneave, 1954; Barlow, H.B., 1961; Dan et al., 1996), the Infomax pri…”
Interlaminar Connections in the Neocortex
Thomson, A. M. (2002) · Cerebral Cortex · doi:10.1093/cercor/13.1.5 · Cited by 468 · cited 2× · see citing works
“Feedback connections were typically seen when pyramidal cells in one layer targeted inhibitory cells in another (see Thomson and Bannister, 2003 for a review).”
Neuronal Mechanisms and Attentional Modulation of Corticothalamic Alpha Oscillations
Bollimunta, A., Mo, J., Schroeder, C. E., & Ding, M. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.5580-10.2011 · Cited by 441 · cited 2× · see citing works
“This issue is important as distinct neuronal dynamics in different cortical layers are becoming increasingly apparent (de Kock et al., 2007; Sakata and Harris, 2009; Maier et al., 2010; Bollimunta et al., 2011).”
BackgroundUnique functional properties of somatostatin-expressing GABAergic neurons in mouse barrel cortex
Gentet, L. J., Kremer, Y., Taniguchi, H., Huang, Z. J., Staiger, J. F., & Petersen, C. C. H. (2012) · Nature Neuroscience · doi:10.1038/nn.3051 · Cited by 438 · cited 2× · see citing works
“Furthermore, there is a specific type of GABAergic neuron that appears to control distal dendritic excitability, gating top-down excitatory signals differentially during behavior (Gentet et al., 2012).”
Stimulus Predictability Reduces Responses in Primary Visual Cortex
Alink, A., Schwiedrzik, C. M., Kohler, A., Singer, W., & Muckli, L. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.3730-10.2010 · Cited by 435 · cited 1× · see citing works
“The inhibitory effect of feedback connections is further supported by neuroimaging studies (Murray et al., 2002, 2006; Harrison et al., 2007; Summerfield et al., 2008, 2011; Alink et al., 2010).”
THE CIRCUITRY OF V1 AND V2: Integration of Color, Form, and Motion
Sincich, L. C., & Horton, J. C. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135731 · Cited by 433 · cited 7× · see citing works
“Distinct (laminar-specific) extrinsic connections (Zeki and Shipp, 1988; Felleman and Van Essen, 1991; Barone et al., 2000; Vezoli et al., 2004; Markov et al., 2011).”
InfluentialAttention reverses the effect of prediction in silencing sensory signals
Kok, P., Rahnev, D., Jehee, J. F. M., Lau, H. C., & de Lange, F. P. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr310 · Cited by 430 · cited 2× · see citing works
“The resulting attentional modulation (e.g., Hopfinger et al., 2000) can interact with top-down predictions to override their suppressive influence, as demonstrated empirically (Kok et al., 2012).”
Neural Mechanisms of Visual Attention: How Top-Down Feedback Highlights Relevant Locations
Saalmann, Y. B., Pigarev, I. N., & Vidyasagar, T. R. (2007) · Science · doi:10.1126/science.1139140 · Cited by 428 · see citing works
Thomson, A. M. (2002) · Cerebral Cortex · doi:10.1093/cercor/12.9.936 · Cited by 420 · cited 3× · see citing works
“The numbers denote connection strengths (mean amplitude of PSPs measured at soma in mV) and connection probabilities (in parentheses) according to Thomson et al. (2002).”
InfluentialTowards a mathematical theory of cortical micro-circuits
George, D., & Hawkins, J. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000532 · Cited by 404 · cited 2× · see citing works
“More Neuron 76, November 21, 2012 ª2012 Elsevier Inc. 701 recently, George and Hawkins have suggested that the canonical microcircuit implements a form of Bayesian processing (George and Hawkins, 2009).”
Häusser, M., & Mel, B. (2003) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(03)00075-8 · Cited by 392 · cited 3× · see citing works
“Columns, Connections, and Computations So is the cortical column the basic unit of cortical computation? Some authors emphasize that even within a dendrite, there are all the necessary biophysical mechanisms for performing surprisingly advanced computations, such as direction selectivity, coincidence detection, or tem…”
Prior expectation mediates neural adaptation to repeated sounds in the auditory cortex: an MEG study
Todorovic, A., van Ede, F., Maris, E., & de Lange, F. P. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.1425-11.2011 · Cited by 391 · cited 1× · see citing works
“Similarly, violating expectations of auditory repetition causes enhanced gamma-band responses in early auditory cortex (Todorovic et al., 2011).”
Fine-scale specificity of cortical networks depends on inhibitory cell type and connectivity
Yoshimura, Y., & Callaway, E. M. (2005) · Nature Neuroscience · doi:10.1038/nn1565 · Cited by 390 · cited 2× · see citing works
“For more information on inhibitory connections, see Yoshimura and Callaway (2005).”
Weight consistency specifies regularities of macaque cortical networks
Markov, N. T., Misery, P., Falchier, A., Lamy, C., Vezoli, J., Quilodran, R., Gariel, M. A., Giroud, P., Ercsey-Ravasz, M., Pilaz, L. J., et al. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq201 · Cited by 386 · see citing works
Prefrontal modulation of visual processing in humans
Barceló, F., Suwazono, S., & Knight, R. T. (2000) · Nature Neuroscience · doi:10.1038/73975 · Cited by 374 · cited 1× · see citing works
“…(suppressive) effect of feedback connections comes from neuropsychology: patients with damage to the prefrontal cortex (PFC) show disinhibition of event-related potential (ERP) responses to repeating stimuli (Knight et al., 1989; Yamaguchi and Knight, 1990; but see Barceló et al., 2000).”
The corticothalamocortical circuit drives higher-order cortex in the mouse
Theyel, B. B., Llano, D. A., & Sherman, S. M. (2010) · Nature Neuroscience · doi:10.1038/nn.2449 · Cited by 366 · cited 2× · see citing works
“The strongest evidence for this claim comes from the somatosensory system, where it was shown recently that the posterior medial nucleus of the thalamus (POm) – a higher-order thalamic nucleus that receives direct input from cortex – can relay information between S1 and S2 (Theyel et al., 2009).”
Transitions in neural oscillations reflect prediction errors generated in audiovisual speech
Arnal, L. H., Wyart, V., & Giraud, A.-L. (2011) · Nature Neuroscience · doi:10.1038/nn.2810 · Cited by 353 · see citing works
Constraints on cortical and thalamic projections: the no-strong-loops hypothesis
Crick, F., & Koch, C. (1998) · Nature · doi:10.1038/34584 · Cited by 353 · cited 3× · see citing works
“Although it is generally thought that feedback connections are weak and modulatory (Crick and Koch, 1998; Sherman and Guillery, 1998), recent evidence suggests that feedback connections do more than modulate lower-level responses: Sherman and colleagues recorded cells in mouse areas V1/V2 and…”
Time Course and Time-Distance Relationships for Surround Suppression in Macaque V1 Neurons
Bair, W., Cavanaugh, J. R., & Movshon, J. A. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-20-07690.2003 · Cited by 345 · cited 2× · see citing works
“Finally, others have argued for an inhibitory effect of feedback based on the timing and spatial extent of surround suppression in monkey V1 – concluding that the far surround suppression effects were most likely mediated by feedback (Bair et al., 2003).”
de Kock, C. P. J., Bruno, R. M., Spors, H., & Sakmann, B. J. (2007) · The Journal of Physiology · doi:10.1113/jphysiol.2006.124321 · Cited by 344 · cited 2× · see citing works
“This issue is important as distinct neuronal dynamics in different cortical layers are becoming increasingly apparent (de Kock et al., 2007; Sakata and Harris, 2009; Maier et al., 2010; Bollimunta et al., 2011).”
Dynamical relaying can yield zero time lag neuronal synchrony despite long conduction delays
Vicente, R., Gollo, L. L., Mirasso, C. R., Fischer, I., & Pipa, G. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0809353105 · Cited by 341 · cited 1× · see citing works
“Furthermore, computational considerations and recent experimental findings point to a potentially important role for higher-order thalamic nuclei in coordinating and synchronizing cortical responses (Vicente et al., 2008; Saalmann et al., 2012).”
A beta2-frequency (20-30 Hz) oscillation in nonsynaptic networks of somatosensory cortex
Roopun, A. K., Middleton, S. J., Cunningham, M. O., LeBeau, F. E. N., Bibbig, A., Whittington, M. A., & Traub, R. D. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0607443103 · Cited by 338 · cited 3× · see citing works
“…anatomically interconnected, but mounting evidence suggests that they constitute functionally distinct processing streams: in an elegant experiment, Roopun et al. (2006) showed that L2/3 of rat somatomotor cortex shows prominent gamma oscillations that are coexpressed with beta oscillations in L5.”
Distinct functions for direct and transthalamic corticocortical connections
Sherman, S. M., & Guillery, R. W. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00429.2011 · Cited by 336 · cited 2× · see citing works
“…while many studies have focused on extrinsic connections that project directly from one cortical area to the next, there is mounting evidence that feedforward driving connections (and perhaps feedback) in the cortex could be mediated by transthalamic pathways (Sherman and Guillery, 1998, 2011).”
Long-range-projecting GABAergic neurons modulate inhibition in hippocampus and entorhinal cortex
Melzer, S., Michael, M., Caputi, A., Eliava, M., Fuchs, E. C., Whittington, M. A., & Monyer, H. (2012) · Science · doi:10.1126/science.1217139 · Cited by 333 · cited 2× · see citing works
“…view is that extrinsic corticocortical connections are exclusively excitatory (using glutamate as their excitatory neurotransmitter), although recent evidence suggests that inhibitory extrinsic connections exist andmay play an important role in synchronizing distant regions (Melzer et al., 2012).”
BackgroundTop-down laminar organization of the excitatory network in motor cortex
Weiler, N., Wood, L., Yu, J., Solla, S. A., & Shepherd, G. M. G. (2008) · Nature Neuroscience · doi:10.1038/nn2049 · Cited by 330 · cited 2× · see citing works
“Weiler and colleagues combined whole-cell recordings in mouse motor cortex with photostimulation to uncage Glutamate (Weiler et al., 2008).”
Statistical learning of visual transitions in monkey inferotemporal cortex
Meyer, T., & Olson, C. R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1112895108 · Cited by 325 · cited 1× · see citing works
“Furthermore, neurons in monkey inferotemporal cortex respond significantly less to a predicted sequence of natural images, compared to an unpredicted sequence (Meyer and Olson, 2011).”
Barone, P., Batardiere, A., Knoblauch, K., & Kennedy, H. (2000) · The Journal of Neuroscience · doi:10.1523/jneurosci.20-09-03263.2000 · Cited by 300 · cited 1× · see citing works
“However, one might speculate 706 Neuron 76, November 21, 2012 ª2012 Elsevier Inc. that the characteristic frequencies of canonical microcircuits have evolved to model and—through active inference—create the sensorium (Berkes et al., 2011; Engbert et al., 2011; Friston, 2010).”
Evoked brain responses are generated by feedback loops
Garrido, M. I., Kilner, J. M., Kiebel, S. J., & Friston, K. J. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706274105 · Cited by 296 · cited 1× · see citing works
“These enhanced responses are thought to reflect an inability of higher cortical areas to predict, and thereby suppress, the activity of populations encoding prediction error (Garrido et al., 2007; Wacongne et al., 2011).”
Self-citationOscillatory phase coupling coordinates anatomically dispersed functional cell assemblies
Canolty, R. T., Ganguly, K., Kennerley, S. W., Cadieu, C. F., Koepsell, K., Wallis, J. D., & Carmena, J. M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008306107 · Cited by 292 · cited 2× · see citing works
“This view has re-emerged in recent years, with the development of the requisite recording and analytic techniques for evaluating this proposal (Buzsáki, 2010; Canolty et al., 2010; Singer et al., 1997; Lopes-dos-Santos et al., 2011).”
Neuronal assemblies: necessity, signature and detectability
Singer, W., Engel, A. K., Kreiter, A. K., Munk, M. H. J., Neuenschwander, S., & Roelfsema, P. R. (1997) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(97)01079-6 · Cited by 292 · cited 2× · see citing works
“This view has re-emerged in recent years, with the development of the requisite recording and analytic techniques for evaluating this proposal (Buzsáki, 2010; Canolty et al., 2010; Singer et al., 1997; Lopes-dos-Santos et al., 2011).”
Functional connectivity between simple cells and complex cells in cat striate cortex
Alonso, J.-M., & Martinez, L. M. (1998) · Nature Neuroscience · doi:10.1038/1609 · Cited by 278 · cited 1× · see citing works
“Furthermore, simple cells project monosynaptically onto complex cells, where they exert a strong feedforward influence (Alonso and Martinez, 1998; Alonso, 2002).”
BackgroundDissociable prior influences of signal probability and relevance on visual contrast sensitivity
Wyart, V., Nobre, A. C., & Summerfield, C. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1120118109 · Cited by 268 · cited 2× · see citing works
“It should be noted that the suppression of responses to predictable stimuli can coexist with (top-down) attentional enhancement of signal processing (Wyart et al., 2012): in predictive coding, attention is mediated by increasing the gain of populations encoding prediction error (Spratling, 2008; Feldman and Friston, 2…”
Maier (2010) · Frontiers in System Neuroscience · doi:10.3389/fnsys.2010.00031 · Cited by 266 · see citing works
Ullman, S. (1995) · Cerebral Cortex · doi:10.1093/cercor/5.1.1 · Cited by 264 · cited 1× · see citing works
“Others suggest that this circuitry allows the cortex to organize and integrate bottom-up, lateral, and top-down information (Ullman, 1995; Raizada and Grossberg, 2003).”
Raizada, R. D. S. (2002) · Cerebral Cortex · doi:10.1093/cercor/13.1.100 · Cited by 262 · cited 1× · see citing works
“Others suggest that this circuitry allows the cortex to organize and integrate bottom-up, lateral, and top-down information (Ullman, 1995; Raizada and Grossberg, 2003).”
Corticofugal feedback influences the generation of length tuning in the visual pathway
Murphy, P. C., & Sillito, A. M. (1987) · Nature · doi:10.1038/329727a0 · Cited by 257 · cited 3× · see citing works
“of feline LGN cells: without feedback, LGN cells are disinhibited and show weaker surround suppression (Murphy and Sillito, 1987; Sillito et al., 1993; but see Alitto and Usrey, 2008).”
Prefrontal cortex gating of auditory transmission in humans
Knight, R. T., Scabini, D., & Woods, D. L. (1989) · Brain Research · doi:10.1016/0006-8993(89)91381-4 · Cited by 256 · cited 2× · see citing works
“…(suppressive) effect of feedback connections comes from neuropsychology: patients with damage to the prefrontal cortex (PFC) show disinhibition of event-related potential (ERP) responses to repeating stimuli (Knight et al., 1989; Yamaguchi and Knight, 1990; but see Barceló et al., 2000).”
Inhibitory interneurons in a cortical column form hot zones of inhibition in layers 2 and 5A
Meyer, H. S., Schwarz, D., Wimmer, V. C., Schmitt, A. C., Kerr, J. N. D., Sakmann, B., & Helmstaedter, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1113648108 · Cited by 254 · cited 5× · see citing works
“The laminar location highlighted by these studies—the bottom of L1 and the top of L2/3—has recently been shown to be a ‘‘hotspot’’ of inhibition in the column (Meyer et al., 2011).”
InfluentialPerceptual Neural Organization: Some Approaches Based On Network Models And Information Theory
Linsker, R. (1990) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.13.030190.001353 · Cited by 248 · cited 2× · see citing works
“, 1996), the Infomax principle (Linsker, 1990; Atick, 2011; Kay and Phillips, 2011), andperceptionashypothesis testing (Gregory,1968,1980).”
The columnar and laminar organization of inhibitory connections to neocortical excitatory cells
Kätzel, D., Zemelman, B. V., Buetfering, C., Wölfel, M., & Miesenböck, G. (2011) · Nature Neuroscience · doi:10.1038/nn.2687 · Cited by 241 · cited 5× · see citing works
“Several conclusions can be drawn from this approach (Kätzel et al., 2011): first, L4 inhibitory connections are more restricted in their lateral extent, relative to other layers.”
InfluentialPerceptual illusions and brain models
Gregory, R. L. (1968) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1968.0071 · Cited by 237 · cited 4× · see citing works
“Andre M. Bastos,1,2,6 W. Martin Usrey,1,3,4 Rick A. Adams,8 George R. Mangun,2,3,5 Pascal Fries,6,7 and Karl J. Friston8,* 1Center for Neuroscience 2Center for Mind and Brain 3Department of Neurology 4Department of Neurobiology, Physiology and Behavior 5Department of Psychology University of California, Davis, Davis,…”
InfluentialHaeusler, S., & Maass, W. (2006) · Cerebral Cortex · doi:10.1093/cercor/bhj132 · Cited by 221 · cited 3× · see citing works
“The ensuing architecture bears a striking correspondence to the microcircuit in (Haeusler and Maass, 2007) in the left panel of Figure 5 – in the sense that nearly every connection required by the predictive coding scheme appears to be present in terms of quantitative measures of intrinsic connectivity.”
InfluentialGirard, P., Salin, P. A., & Bullier, J. (1992) · Journal of Neurophysiology · doi:10.1152/jn.1992.67.6.1437 · Cited by 216 · cited 1× · see citing works
“In all cases—when V1 is reversibly inactivated through cooling— single-cell activity in target areas is strongly suppressed (Girard and Bullier, 1989; Girard et al., 1991a, 1991b, 1992).”
The cortical projections of foveal striate cortex in the rhesus monkey
Zeki, S. M. (1978) · The Journal of Physiology · doi:10.1113/jphysiol.1978.sp012270 · Cited by 195 · cited 3× · see citing works
“When visual stimuli were presented in the classical receptive field (CRF), cooling of area V5/MT decreased unit activity in earlier areas, suggesting an excitatory effect of extrinsic feedback (Hupé et al., 1998).”
Oscillatory firing and interneuronal correlations in squirrel monkey striate cortex
Livingstone, M. S. (1996) · Journal of Neurophysiology · doi:10.1152/jn.1996.75.6.2467 · Cited by 194 · cited 1× · see citing works
“This finding is consistent with an earlier study by Livingstone (1996) showing that 50%of cells in L2/3 of squirrel monkey V1 expressed gamma oscillations, compared to less than 20% of cells in L4C and infragranular layers.”
Effect of cooling area 18 on striate cortex cells in the squirrel monkey.
Sandell, J. H., & Schiller, P. H. (1982) · Journal of Neurophysiology · doi:10.1152/jn.1982.48.1.38 · Cited by 189 · cited 1× · see citing works
“While these studies provide convincing evidence that cortical feedback to the LGN is inhibitory, the evidence is more complicated for corticocortical feedback connections (Sandell and Schiller, 1982; Johnson and Burkhalter, 1996, 1997).”
Beta- and gamma-range human lower limb corticomuscular coherence
Gwin, J. T., & Ferris, D. P. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00258 · Cited by 184 · cited 1× · see citing works
“Indeed, there is empirical evidence to support this notion in the visual (Lakatos et al., 2008; Meirovithz et al., 2012; Bosman et al., 2012) and motor (Gwin and Ferris, 2012) domain.”
Visual activity in area V2 during reversible inactivation of area 17 in the macaque monkey
Girard, P., & Bullier, J. (1989) · Journal of Neurophysiology · doi:10.1152/jn.1989.62.6.1287 · Cited by 179 · cited 2× · see citing works
“In all cases – when V1 is reversibly inactivated through cooling – single-cell activity in target areas is strongly suppressed (Girard and Bullier, 1989; Girard et al., 1991a, 1991b, 1992).”
Synaptic interactions of late-spiking neocortical neurons in layer 1
Chu, Z., Galarreta, M., & Hestrin, S. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-01-00096.2003 · Cited by 172 · cited 5× · see citing works
“These L1 cells are almost all inhibitory and interconnect strongly with each other, via electrical connections and chemical synapses (Chu et al., 2003).”
InfluentialPaths of information flow through visual cortex
Mignard, M., & Malpeli, J. G. (1991) · Science · doi:10.1126/science.1848727 · Cited by 164 · cited 2× · see citing works
“Feedback extrinsic connections are (driving and) modulatory (Mignard and Malpeli, 1991; Bullier et al., 1996; Sherman and Guillery, 1998; Covic and Sherman, 2011; De Pasquale and Sherman, 2011).”
Reconciling predictive coding and biased competition models of cortical function
Spratling, M. W. (2008) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.004.2008 · Cited by 153 · cited 2× · see citing works
“…suppression of responses to predictable stimuli can coexist with (top-down) attentional enhancement of evoked processing (Wyart et al., 2012): in predictive coding, attention is mediated by increasing the gain of populations encoding prediction error (Spratling, 2008; Feldman and Friston, 2010).”
Gating of somatosensory input by human prefrontal cortex
Yamaguchi, S., & Knight, R. T. (1990) · Brain Research · doi:10.1016/0006-8993(90)91553-s · Cited by 152 · cited 3× · see citing works
“Further evidence for the inhibitory (suppressive) effect of feedback connections comes from neuropsychology: patients with damage to the prefrontal cortex (PFC) show disinhibition of event-related potential (ERP) responses to repeating stimuli (Knight et al., 1989; Yamaguchi and Knight, 1990; but see Barceló et al., 2…”
Alitto, H. J., & Usrey, W. M. (2008) · Neuron · doi:10.1016/j.neuron.2007.11.019 · Cited by 151 · cited 1× · see citing works
“…contributions to length tuning in the LGN, showing that cortical feedback contributes to the surround suppression of feline LGN cells: without feedback, LGN cells are disinhibited and show weaker surround suppression (Murphy and Sillito, 1987; Sillito et al., 1993; but see Alitto and Usrey, 2008).”
BackgroundSelf-citationconcatenation underlies interactions between gamma and beta rhythms in neocortex
Roopun, A. K. (2008) · Frontiers in Cellular Neuroscience · doi:10.3389/neuro.03.001.2008 · Cited by 147 · cited 3× · see citing works
“…smoothing—inherent in Bayesian filtering—leads to an asymmetry in frequency content of superficial and deep cells: for example, superficial cells should express more gamma relative to beta, and deep cells should express more beta relative to gamma (Roopun et al., 2006, 2008; Maier et al., 2010).”
Human Scalp Electroencephalography Reveals that Repetition Suppression Varies with Expectation
Summerfield, C., Wyart, V., Johnen, V. M., & de Gardelle, V. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00067 · Cited by 145 · cited 1× · see citing works
“The inhibitory effect of feedback connections is further supported by neuroimaging studies (Murray et al., 2002, 2006; Harrison et al., 2007; Summerfield et al., 2008, 2011; Alink et al., 2010).”
An integrated model of fixational eye movements and microsaccades
Engbert, R., Mergenthaler, K., Sinn, P., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1102730108 · Cited by 144 · cited 1× · see citing works
“However, one might speculate 706 Neuron 76, November 21, 2012 ª2012 Elsevier Inc. that the characteristic frequencies of canonical microcircuits have evolved to model and—through active inference—create the sensorium (Berkes et al., 2011; Engbert et al., 2011; Friston, 2010).”
Synapse restructuring associated with the maintenance phase of hippocampal long-term potentiation
Geinisman, Y., deToledo‐Morrell, L., Morrell, F., Persina, I. S., & Beatty, M. A. (1996) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(19960506)368:3 · Cited by 140 · cited 1× · see citing works
“While these studies provide convincing evidence that cortical feedback to the LGN is inhibitory, the evidence is more complicated for corticocortical feedback connections (Sandell and Schiller, 1982; Johnson and Burkhalter, 1996, 1997).”
Parallel processing in the corticogeniculate pathway of the macaque monkey
Briggs, F., & Usrey, W. M. (2009) · Neuron · doi:10.1016/j.neuron.2009.02.024 · Cited by 139 · cited 1× · see citing works
“The corticogeniculate feedback pathway from L6 maintains this segregation, as upper L6 cells preferentially synapse onto parvocellular cells in the LGN, while lower L6 cells target the magnocellular LGN layers (Fitzpatrick et al., 1994; Briggs and Usrey, 2009).”
BackgroundSelf-citationSillito, A. M., Cudeiro, J., & Murphy, P. C. (1993) · Experimental Brain Research · doi:10.1007/bf00227775 · Cited by 139 · cited 3× · see citing works
“This evidence is in line with classical studies of corticogeniculate contributions to length tuning in the LGN, showing that cortical feedback contributes to the surround suppression of feline LGN cells: without feedback, LGN cells are disinhibited and show weaker surround suppression (Murphy and Sillito, 1987; Sillit…”
An in vivo assay of synaptic function mediating human cognition
Moran, R. J., Symmonds, M., Stephan, K. E., Friston, K. J., & Dolan, R. J. (2011) · Current Biology · doi:10.1016/j.cub.2011.06.053 · Cited by 137 · cited 2× · see citing works
“Dynamic causal modeling enables one to compare different connectivity models, using empirical electrophysiological responses (David et al., 2006; Moran et al., 2008, 2011).”
Self-citationThe sublaminar organization of corticogeniculate neurons in layer 6 of macaque striate cortex
Fitzpatrick, D., Usrey, W. M., Schofield, B. R., & Einstein, G. (1994) · Visual Neuroscience · doi:10.1017/s0952523800001656 · Cited by 132 · cited 1× · see citing works
“The corticogeniculate feedback pathway from L6 maintains this segregation, as upper L6 cells preferentially synapse onto parvocellular cells in the LGN, while lower L6 cells target the magnocellular LGN layers (Fitzpatrick et al., 1994; Briggs and Usrey, 2009).”
Self-citationWozny, C., & Williams, S. R. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq257 · Cited by 129 · cited 2× · see citing works
“Feedback extrinsic connections are inhibitory (Murphy and Sillito, 1987; Sillito et al., 1993; Chu et al., 2003; Olsen et al., 2012; Meyer et al., 2011; Wozny and Williams, 2011).”
BackgroundThalamic pathways for active vision
Wurtz, R. H., McAlonan, K., Cavanaugh, J., & Berman, R. A. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.02.004 · Cited by 127 · cited 1× · see citing works
“In addition, the thalamic reticular nucleus has been proposed to mediate the inhibition that might underlie crossmodal attention or top-down predictions (Yamaguchi and Knight, 1990; Crick, 1984; Wurtz et al., 2011).”
Bayesian estimation of synaptic physiology from the spectral responses of neural masses
Moran, R. J., Stephan, K. E., Kiebel, S. J., Rombach, N., O'Connor, W. T., Murphy, K. J., Reilly, R. B., & Friston, K. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.01.025 · Cited by 126 · cited 2× · see citing works
“Dynamic causal modeling enables one to compare different connectivity models, using empirical electrophysiological responses (David et al., 2006; Moran et al., 2008, 2011).”
BackgroundMethodSelf-citationAnderson, J. C., Kennedy, H., & Martin, K. A. C. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.0622-11.2011 · Cited by 117 · cited 1× · see citing works
“See Buschman and Miller (2007), Saalmann et al. (2007), Anderson et al. (2011), and Armstrong et al. (2012) for further discussion of top-down connections in attention.”
Synaptic properties of connections between the primary and secondary auditory cortices in mice
Covic, E. N., & Sherman, S. M. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr029 · Cited by 116 · cited 2× · see citing works
“Feedback extrinsic connections are (driving and) modulatory (Mignard and Malpeli, 1991; Bullier et al., 1996; Sherman and Guillery, 1998; Covic and Sherman, 2011; De Pasquale and Sherman, 2011).”
De Pasquale, R., & Sherman, S. M. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3664-11.2011 · Cited by 114 · see citing works
Callaway, E. M., & Wiser, A. K. (1996) · Visual Neuroscience · doi:10.1017/s0952523800009159 · Cited by 112 · cited 2× · see citing works
“%) do not project outside the cortical column (Callaway and Wiser, 1996; Briggs and Callaway, 2005). d Second, we know that the superficial and deep pyramidal cells provide feedforward and feedback connections, respectively.”
Functional interactions between areas V1 and V2 in the monkey
Bullier, J., Hupé, J., James, A., & Girard, P. (1996) · Journal of Physiology-Paris · doi:10.1016/s0928-4257(97)81426-x · Cited by 111 · cited 3× · see citing works
“Similar effects were observed when area V2was cooled and neurons weremeasured in V1: when stimuli were presented only to the CRF, cooling V2 decreased V1 spiking activity; however, when stimuli were present in the CRF and the surround, cooling V2 increased V1 activity (Bullier et al., 1996).”
A Polysynaptic Feedback Circuit in Rat Visual Cortex
Johnson, R. R., & Burkhalter, A. (1997) · Journal of Neuroscience · doi:10.1523/jneurosci.17-18-07129.1997 · Cited by 107 · cited 1× · see citing works
“While these studies provide convincing evidence that cortical feedback to the LGN is inhibitory, the evidence is more complicated for corticocortical feedback connections (Sandell and Schiller, 1982; Johnson and Burkhalter, 1996, 1997).”
Visual activity in areas V3a and V3 during reversible inactivation of area V1 in the macaque monkey
Girard, P., Salin, P. A., & Bullier, J. (1991) · Journal of Neurophysiology · doi:10.1152/jn.1991.66.5.1493 · Cited by 107 · cited 1× · see citing works
“In all cases—when V1 is reversibly inactivated through cooling— single-cell activity in target areas is strongly suppressed (Girard and Bullier, 1989; Girard et al., 1991a, 1991b, 1992).”
Ordinal position and afferent input of neurons in monkey striate cortex
Bullier, J., & Henry, G. H. (1980) · Journal of Comparative Neurology · doi:10.1002/cne.901930407 · Cited by 99 · cited 1× · see citing works
“…supported these predictions, showing that input layers 4Ca and 4Cb contained the largest proportion of cells receiving monosynaptic geniculate input, while superficial and deep layer cells contain a larger number of cells receiving disynaptic or polysynaptic input (Bullier and Henry, 1980).”
Functional Organization of the Visual Cortex
Gilbert, C. D., & Wiesel, T. N. (1983) · Progress in brain research · doi:10.1016/s0079-6123(08)60022-9 · Cited by 93 · see citing works
Usrey, W., & Fitzpatrick, D. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-03-01203.1996 · Cited by 90 · see citing works
Self-citationExtra-classical receptive field effects measured in striate cortex with fMRI
Harrison, L. M., Stephan, K. E., Rees, G., & Friston, K. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.10.017 · Cited by 89 · cited 2× · see citing works
“The inhibitory effect of feedback connections is further supported by neuroimaging studies (Murray et al., 2002, 2006; Harrison et al., 2007; Summerfield et al., 2008, 2011; Alink et al., 2010).”
BackgroundResultSelf-citationNeuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis
Lopes‐dos‐Santos, V., Conde‐Ocazionez, S., Nicolelis, M. A. L., Ribeiro, S. T. G., & Tort, A. B. L. (2011) · PLoS ONE · doi:10.1371/journal.pone.0020996 · Cited by 86 · cited 1× · see citing works
“This view has re-emerged in recent years, with the development of the requisite recording and analytic techniques for evaluating this proposal (Buzsáki, 2010; Canolty et al., 2010; Singer et al., 1997; Lopes-dos-Santos et al., 2011).”
BackgroundCoherent Infomax as a Computational Goal for Neural Systems
Kay, J., & Phillips, W. A. (2010) · Bulletin of Mathematical Biology · doi:10.1007/s11538-010-9564-x · Cited by 80 · cited 1× · see citing works
“…automata (MacKay, 1956), the principle of minimum redun- 702 Neuron 76, November 21, 2012 ª2012 Elsevier Inc. dancy (Attneave, 1954; Barlow, 1961; Dan et al., 1996), the Infomax principle (Linsker, 1990; Atick, 2011; Kay and Phillips, 2011), andperceptionashypothesis testing (Gregory,1968,1980).”
Quantitative analysis of connectivity in the visual cortex: extracting function from structure
Vezoli, J., Falchier, A., Jouve, B., Knoblauch, K., Young, M., & Kennedy, H. (2004) · The Neuroscientist · doi:10.1177/1073858404268478 · Cited by 65 · see citing works
Visual activity in macaque area V4 depends on area 17 input
Girard, P., Salin, P. A., & Bullier, J. (1991) · Neuroreport · doi:10.1097/00001756-199102000-00004 · Cited by 57 · cited 1× · see citing works
“In all cases—when V1 is reversibly inactivated through cooling— single-cell activity in target areas is strongly suppressed (Girard and Bullier, 1989; Girard et al., 1991a, 1991b, 1992).”
BackgroundSynaptic connection from cortical area V4 to V2 in macaque monkey
Anderson, J. C., & Martin, K. A. C. (2006) · Journal of Comparative Neurology · doi:10.1002/cne.20914 · Cited by 56 · cited 2× · see citing works
“However, one important route by which feedback connections could mediate selective inhibition is via their termination in L1 (Anderson and Martin, 2006; Shipp, 2007): layer 1 is sometimes referred to as acellular due to its pale appearance with Nissl staining (the classical method for separating…”
The Neuroscience of AttentionAttentional Control and Selection
Mangun, G. R. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195334364.001.0001 · Cited by 50 · see citing works
Self-citationLaminar patterns of local excitatory input to layer 5 neurons in macaque primary visual cortex
Briggs, F., & Callaway, E. M. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhh154 · Cited by 44 · cited 1× · see citing works
“%) do not project outside the cortical column (Callaway and Wiser, 1996; Briggs and Callaway, 2005). d Second, we know that the superficial and deep pyramidal cells provide feedforward and feedback connections, respectively.”
Spatially Specific fMRI Repetition Effects in Human Visual Cortex
Murray, S. O., Olman, C. A., & Kersten, D. (2006) · Journal of Neurophysiology · doi:10.1152/jn.01236.2005 · Cited by 38 · cited 1× · see citing works
“The inhibitory effect of feedback connections is further supported by neuroimaging studies (Murray et al., 2002, 2006; Harrison et al., 2007; Summerfield et al., 2008, 2011; Alink et al., 2010).”
Time-dependent, layer-specific modulation of sensory responses mediated by neocortical layer 1
Shlosberg, D., Amitai, Y., & Azouz, R. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00520.2006 · Cited by 34 · cited 2× · see citing works
“Indeed, a study of rat barrel cortex, which stimulated (and inactivated) L1, showed that it exerts a powerful inhibitory effect on whisker-evoked responses (Shlosberg et al., 2006).”
BackgroundMeirovithz, E., Ayzenshtat, I., Werner-Reiss, U., Shamir, I., & Slovin, H. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr102 · Cited by 33 · cited 1× · see citing works
“Indeed, there is empirical evidence to support this notion in the visual (Lakatos et al., 2008; Meirovithz et al., 2012; Bosman et al., 2012) and motor (Gwin and Ferris, 2012) domain.”
Coordination in Behavior and Cognition
Engel, A. K., Friston, K. J., Kelso, J. A. S., Peter König, B., Kovács, I., MacDonald, A. W., Miller, E. K., Phillips, W. A., Silverstein, S. M., Tallon‐Baudry, C., et al. (2010) · The MIT Press eBooks · doi:10.7551/mitpress/8797.003.0021 · Cited by 33 · see citing works
Self-citationBook Review: Neural Connections and Receptive Field Properties in the Primary Visual Cortex
Alonso, J. (2002) · The Neuroscientist · doi:10.1177/107385802236967 · Cited by 28 · cited 1× · see citing works
“Furthermore, simple cells project monosynaptically onto complex cells, where they exert a strong feedforward influence (Alonso and Martinez, 1998; Alonso, 2002).”
BackgroundKlampfl, S., Legenstein, R. A., & Maass, W. (2008) · Neural Computation · doi:10.1162/neco.2008.01-07-432 · Cited by 23 · cited 1× · see citing works
“Others argue that single neurons can process their inputs at the dendrite, soma, and initial segment, such that the output spike trains of just two interconnected cells could mediate computations like independent components analysis (Klampfl et al., 2009).”
Guest editoria: Wireless mesh networks: Applications, architectures, and protocols
Yang, Y., Papagiannaki, K., Ci, S., et al. (2008) · IEEE Network · doi:10.1109/mnet.2008.4435896 · Cited by 13 · see citing works
Coordination in Behavior and Cognition
Engel, A. K., Friston, K. J., Kelso, J. A. S., et al. (2010) · The MIT Press eBooks · doi:10.7551/mitpress/9780262014717.003.0018 · Cited by 8 · see citing works
Self-citationAttention and Action in the Frontal Eye Field
Armstrong, K. M., Schafer, R. J., Chang, M. H., et al. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195334364.003.0007 · Cited by 5 · cited 1× · see citing works
“See Buschman and Miller (2007), Saalmann et al. (2007), Anderson et al. (2011), and Armstrong et al. (2012) for further discussion of top-down connections in attention.”
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Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550 · see citing works
Structural and Functional Brain Networks: From Connections to Cognition
Park, H.-J., & Friston, K. (2013) · Science · doi:10.1126/science.1238411 · Cited by 2,106 · see citing works
“A simplified canonical microcircuit (36) includes intrinsic connectivity (inhibitory and excitatory) and extrinsic forward and backward connectivity.”
BackgroundInfluentialSelf-citationInteroceptive predictions in the brain
Barrett, L. F., & Simmons, W. K. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3950 · Cited by 1,766 · see citing works
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557 · see citing works
Dorsal and Ventral Attention Systems: Distinct Neural Circuits but Collaborative Roles
Vossel, S., Geng, J. J., & Fink, G. R. (2014) · The Neuroscientist · doi:10.1177/1073858413494269 · Cited by 1,438 · see citing works
Visual areas exert feedforward and feedback influences through distinct frequency channels
Bastos, A. M., Vezoli, J., Bosman, C. A., Schoffelen, J.-M., Oostenveld, R., Dowdall, J. R., De Weerd, P., Kennedy, H., & Fries, P. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.018 · Cited by 1,320 · see citing works
Self-citationThe theory of constructed emotion: an active inference account of interoception and categorization
Barrett, L. F. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw154 · Cited by 1,426 · see citing works
“Using Bayesian logic (Deneve, 2008; Bastos et al., 2012), a brain uses pattern completion to decide among simulations and implement one of them (Gallivan et al.”
REBUS and the anarchic brain: Toward a unified model of the brain action of psychedelics
Carhart-Harris, R. L., & Friston, K. J. (2019) · Pharmacological Reviews · doi:10.1124/pr.118.017160 · Cited by 1,180 · see citing works
“This schematic uses the format in Bastos et al. (2012), to which the reader is referred for details.”
MethodActive inference: a process theory
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017) · Neural Computation · doi:10.1162/neco_a_00912 · Cited by 1,156 · see citing works
Self-citationHow Do Expectations Shape Perception?
de Lange, F. P., Heilbron, M., & Kok, P. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.06.002 · Cited by 975 · see citing works
Imaging structural and functional brain development in early childhood
Gilmore, J. H., Knickmeyer, R. C., & Gao, W. (2018) · Nature Reviews Neuroscience · doi:10.1038/nrn.2018.1 · Cited by 1,013 · see citing works
Active interoceptive inference and the emotional brain
Seth, A. K., & Friston, K. J. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2016.0007 · Cited by 945 · see citing works
“Current interpretations suggest that superficial pyramidal cells (red triangles) compare the expectations (at each level) with top-down predictions from deep pyramidal cells (black triangles) of higher levels (22, 23).”
BackgroundSelf-citationThe theory of constructed emotion: an active inference account of interoception and categorization
Barrett, L. F. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx060 · Cited by 950 · see citing works
“Using Bayesian logic (Deneve, 2008; Bastos et al., 2012), a brain uses pattern completion to decide among simulations and implement one of them (Gallivan et al., 2016), based on predicted maintenance of physiological efficiency across multiple body systems (e.g. need for glucose, oxygen, salt etc.).”
Expectation in perceptual decision making: neural and computational mechanisms
Summerfield, C., & de Lange, F. P. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3838 · Cited by 832 · see citing works
The Predictive Coding Account of Psychosis
Sterzer, P., Adams, R. A., Fletcher, P., Frith, C., Lawrie, S. M., Muckli, L., Petrovic, P., Uhlhaas, P., Voss, M., & Corlett, P. R. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.05.015 · Cited by 941 · see citing works
“Similarly, models that identify how genes relate to brain development (114) and changes the canonical microcircuits (14) involved in aberrant predictive coding are warranted.”
BackgroundSelf-citationWaking state: rapid variations modulate neural and behavioral responses
McGinley, M. J., Vinck, M., Reimer, J., Batista-Brito, R., Zagha, E., Cadwell, C. R., Tolias, A. S., Cardin, J. A., & McCormick, D. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.012 · Cited by 801 · see citing works
“In addition to arousal effects, this increase in activity may also represent pre-movement preparatory signals (Bastos et al., 2012; Bruce and Goldberg, 1985; Carvell et al., 1996; Georgopoulos et al., 1982).”
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0277-3 · Cited by 908 · see citing works
The Computational Anatomy of Psychosis
Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D., & Friston, K. J. (2013) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2013.00047 · Cited by 850 · see citing works
“See Bastos et al. (2012) for a review of this canonical circuitry from the point of view of predictive coding.”
BackgroundSelf-citationAnatomy of hierarchy: Feedforward and feedback pathways in macaque visual cortex
Markov, N. T., Vezoli, J., Chameau, P., Falchier, A., Quilodran, R., Huissoud, C., Lamy, C., Misery, P., Giroud, P., Ullman, S., et al. (2014) · Journal of Comparative Neurology · doi:10.1002/cne.23458 · Cited by 759 · see citing works
“…construct large-scale computational models, incorporating hierarchical integration of interareal connections into the local circuit of the cortex (Bastos et al., 2012), and thereby link up the concepts of cortical hierarchy, Bayesian inference, and the canonical circuit (Douglas and Martin,…”
BackgroundPredictive Processing: A Canonical Cortical Computation
Keller, G. B., & Mrsic-Flogel, T. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.003 · Cited by 836 · see citing works
“It is possible that internal representation neurons are intermixed with prediction error neurons in different cortical layers, or that they are enriched in deep layers of cortex (Bastos et al., 2012), which are the main source of topdown signals (Felleman and Van Essen, 1991; Markov et al.”
Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T., Cownden, D., Tweed, D., & Akerman, C. J. (2016) · Nature Communications · doi:10.1038/ncomms13276 · Cited by 879 · see citing works
Michalareas, G., Vezoli, J., van Pelt, S., Schoffelen, J.-M., Kennedy, H., & Fries, P. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.018 · Cited by 696 · see citing works
“…in essence that higher areas constantly derive predictions based on incoming evidence and prior experience, and feed those predictions back to lower areas, where they are subtracted from sensory evidence, such that only prediction errors need to be forwarded to higher areas (Bastos et al., 2012).”
Self-citationMiller, E. K., Lundqvist, M., & Bastos, A. M. (2018) · Neuron · doi:10.1016/j.neuron.2018.09.023 · Cited by 797 · see citing works
Self-citationWhere Does EEG Come From and What Does It Mean?
Cohen, M. X. (2017) · Trends in Neurosciences · doi:10.1016/j.tins.2017.02.004 · Cited by 722 · see citing works
An aberrant precision account of autism
Lawson, R. P., Rees, G., & Friston, K. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00302 · Cited by 641 · see citing works
Self-citationActive Inference, homeostatic regulation and adaptive behavioural control
Pezzulo, G., Rigoli, F., & Friston, K. (2015) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.09.001 · Cited by 650 · see citing works
“…specificity of forward and backward connections, the prevalence of nonlinear or modulatory backward connections (that embody interactions and nonlinearities inherent in the generation of sensory signals) and their spectral characteristics (Adams et al., 2013; Bastos et al., 2012; Friston, 2008).”
Self-citationCortical high-density counterstream architectures
Markov, N. T., Ercsey-Ravasz, M., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013) · Science · doi:10.1126/science.1238406 · Cited by 616 · see citing works
“dictive coding, and emerging concepts of inference in cortical function (18, 69, 70).”
BackgroundEvidence for a large-scale brain system supporting allostasis and interoception in humans
Kleckner, I. R., Zhang, J., Touroutoglou, A., Chanes, L., Xia, C., Simmons, W. K., Quigley, K. S., Dickerson, B. C., & Feldman Barrett, L. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0069 · Cited by 641 · see citing works
“…called the Embodied Predictive Interoception Coding (EPIC) model 11 , we integrated Friston’s active inference approach to predictive coding 38–40 with Barbas’s structural model to hypothesize that less-differentiated agranular and dysgranular visceromotor cortices in the cingulate cortex…”
Uncertainty and stress: Why it causes diseases and how it is mastered by the brain
Peters, A., McEwen, B. S., & Friston, K. (2017) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2017.05.004 · Cited by 652 · see citing works
“, 2000; Ramaswamy and Markram, 2015; Thomson and Lamy, 2007) and neurocomputational analyses (Bastos et al., 2012; Haeusler and Maass, 2007; Shipp, 2016; Shipp et al., 2013), we propose the following assignment of empirical prior beliefs, the likelihood, the sensory data, and the prediction errors to their anatomical…”
Self-citationMirror neurons: from origin to function
Cook, R., Bird, G., Catmur, C., Press, C., & Heyes, C. (2014) · Behavioral and Brain Sciences · doi:10.1017/s0140525x13000903 · Cited by 632 · see citing works
“Rather, the associative hypothesis implies that a new approach is required to find out what MNs contribute to social behavior.”
BackgroundBeyond the Status Quo: A Role for Beta Oscillations in Endogenous Content (Re)Activation
Spitzer, B., & Haegens, S. (2017) · eNeuro · doi:10.1523/eneuro.0170-17.2017 · Cited by 625 · see citing works
“2017 eNeuro.org nication of top-down predictions (Arnal and Giraud, 2012; Bastos et al., 2012; Bastos et al., 2015; Michalareas et al., 2016).”
BackgroundAn active inference theory of allostasis and interoception in depression
Barrett, L. F., Quigley, K. S., & Hamilton, P. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2016.0011 · Cited by 554 · see citing works
Noninvasive brain stimulation: from physiology to network dynamics and back
Dayan, E., Censor, N., Buch, E. R., Sandrini, M., & Cohen, L. G. (2013) · Nature Neuroscience · doi:10.1038/nn.3422 · Cited by 588 · see citing works
Computational psychiatry: the brain as a phantastic organ
Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)70275-5 · Cited by 531 · see citing works
Self-citationThe dysconnection hypothesis (2016)
Friston, K., Brown, H. R., Siemerkus, J., & Stephan, K. E. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.07.014 · Cited by 561 · see citing works
“Predictive coding describes how the brain processes sensory information by optimizing explanations for its sensations: see (Bastos et al., 2012) for a review of canonical microcircuits and hierarchical predictive coding in perception and (Adams et al., 2013a; Shipp et al., 2013) for corresponding…”
BackgroundInfluentialSelf-citationHohwy, J. (2016) · Noûs · doi:10.1111/nous.12062 · Cited by 578 · see citing works
The neural architecture of language: Integrative modeling converges on predictive processing
Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105646118 · Cited by 517 · see citing works
Friston, K. J., Kahan, J., Biswal, B., & Razi, A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.12.009 · Cited by 553 · see citing works
“Theoretically, this is consistent with predictive coding, where top-down predictions suppress prediction errors in lower levels of a hierarchy (Bastos et al., 2012).”
ResultSelf-citationHow prediction errors shape perception, attention, and motivation
den Ouden, H. E. M., Kok, P., & de Lange, F. P. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00548 · Cited by 512 · see citing works
“In short, feedback “prediction units” (e.g., deep layers in hierarchically higher visual areas, CA3, prefrontal cortex) set an activity pattern that is integrated with feedforward inputs in “PE units” (e.g., granular layer in hierarchically lower visual areas, CA1, VTA), to reflect the difference…”
BackgroundMethodThe hierarchical basis of neurovisceral integration
Smith, R., Thayer, J. F., Khalsa, S. S., & Lane, R. D. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.02.003 · Cited by 523 · see citing works
Cognitive computational neuroscience
Kriegeskorte, N., & Douglas, P. K. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0210-5 · Cited by 535 · see citing works
The free-energy self: a predictive coding account of self-recognition
Apps, M. A. J., & Tsakiris, M. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.01.029 · Cited by 514 · see citing works
“…type of neuron is, according to the theory (Friston and Kiebel, 2009b) and also recent evidence examining connectivity in cortical microcircuits (Bastos et al., 2012), found in separate cortical layers, the BOLD signal measured in these commonly used MRI scanners will likely be a function of…”
Great Expectations: Is there Evidence for Predictive Coding in Auditory Cortex?
Heilbron, M., & Chait, M. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2017.07.061 · Cited by 428 · see citing works
Broadband Cortical Desynchronization Underlies the Human Psychedelic State
Muthukumaraswamy, S. D., Carhart-Harris, R. L., Moran, R., Brookes, M. J., Williams, T. M., Errtizoe, D., Sessa, B., Papadopoulos, A., Bolstridge, M., Singh, K. D., et al. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2063-13.2013 · Cited by 465 · see citing works
“Moreover, excitation of deep-layer pyramidal cells is consistent with decreased brain activity (Carhart-Harris et al., 2012a) and oscillatory power because the net effect of feedback connections is thought to be inhibitory (Bastos et al., 2012).”
BackgroundMethodSelf-citationA review of predictive coding algorithms
Spratling, M. W. (2017) · Brain and Cognition · doi:10.1016/j.bandc.2015.11.003 · Cited by 437 · see citing works
“…error and prediction layers in the model implements an inference process that serves to update the estimate the mean and variance, and this version of predictive coding is therefore equivalent to Kalman filtering (Bastos et al., 2012; Kiebel & Friston, 2011; Perrinet, Adams, & Friston, 2014).”
BackgroundMethodContextual Feedback to Superficial Layers of V1
Muckli, L., De Martino, F., Vizioli, L., Petro, L. S., Smith, F. W., Ugurbil, K., Goebel, R., & Yacoub, E. (2015) · Current Biology · doi:10.1016/j.cub.2015.08.057 · Cited by 423 · see citing works
“The predictive coding framework describes neuronal computations in cortex, which integrate the predictions carried by cortical feedback with sensory inputs carried by feedforward projections [4].”
Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression
Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A. E., Paliwal, S., Gard, T., Tittgemeyer, M., Fleming, S. M., Haker, H., Seth, A. K., et al. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00550 · Cited by 437 · see citing works
Selective Activation of the Deep Layers of the Human Primary Visual Cortex by Top-Down Feedback
Kok, P., Bains, L. J., van Mourik, T., Norris, D. G., & de Lange, F. P. (2016) · Current Biology · doi:10.1016/j.cub.2015.12.038 · Cited by 397 · see citing works
Cerebral hierarchies: predictive processing, precision and the pulvinar
Kanai, R., Komura, Y., Shipp, S., & Friston, K. (2015) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2014.0169 · Cited by 432 · see citing works
Self-citationShared Neural Mechanisms of Visual Perception and Imagery
Dijkstra, N., Bosch, S., & van Gerven, M. A. J. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.02.004 · Cited by 445 · see citing works
The neurobiology of interoception in health and disease
Quadt, L., Critchley, H., & Garfinkel, S. N. (2018) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13915 · Cited by 447 · see citing works
“This hierarchy does not represent rigid step-by-step processing, but rather a highly context-sensitive, reconfigurable dynamical system whose patterns of effective connectivity change on a momentto-moment basis depending on task, and internal and external contexts.(61,65) to provide an embodied representational contex…”
BackgroundPloner, M., Sorg, C., & Groß, J. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.12.001 · Cited by 445 · see citing works
“In the cortex predictive coding processes are likely to be implemented by various neuronal populations [44].”
BackgroundInfluentialIntegration of visual motion and locomotion in mouse visual cortex
Saleem, A. B., Ayaz, A., Jeffery, K. J., Harris, K. D., & Carandini, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3567 · Cited by 402 · see citing works
Theories of Error Back-Propagation in the Brain
Whittington, J. C. R., & Bogacz, R. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.12.005 · Cited by 420 · see citing works
A guide to group effective connectivity analysis, part 2: Second level analysis with PEB
Zeidman, P., Jafarian, A., Seghier, M. L., Vladimir Litvak, A., Cagnan, H., Price, C. J., & Friston, K. J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.032 · Cited by 412 · see citing works
Self-citationA neural microcircuit for cognitive conflict detection and signaling
Cohen, M. X. (2014) · Trends in Neurosciences · doi:10.1016/j.tins.2014.06.004 · Cited by 407 · see citing works
The graphical brain: Belief propagation and active inference
Friston, K. J., Parr, T., & de Vries, B. (2017) · Network Neuroscience · doi:10.1162/netn_a_00018 · Cited by 415 · see citing works
“Fuller accounts of the anatomical and neurophysiological evidence—upon which these arguments are based—can be found in Adam, Shipp, and Friston (2013), Bastos et al. (2012), Friston (2008), Mumford (1992), Shipp (2005, 2016), and Shipp, Adam, and Friston (2013).”
BackgroundInfluentialSelf-citationDeep Predictive Coding Networks for Video Prediction and Unsupervised LearningPreprint
Lotter, W. E., Kreiman, G., & Cox, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.08104 · Cited by 805 · see citing works
“Our work here builds on previous work in next-frame video prediction [26, 28, 29, 38, 48], but we take particular inspiration from the concept of “predictive coding” from the neuroscience literature [2, 5, 7, 9, 11, 21, 25, 32, 39, 40, 47, 49].”
MethodBridging the Gap between Connectome and Transcriptome
Fornito, A., Arnatkevičiūtė, A., & Fulcher, B. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.10.005 · Cited by 380 · see citing works
Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing
Jensen, O., Gips, B., Bergmann, T. O., & Bonnefond, M. (2014) · Trends in Neurosciences · doi:10.1016/j.tins.2014.04.001 · Cited by 389 · see citing works
From cognitivism to autopoiesis: towards a computational framework for the embodied mind
Allen, M., & Friston, K. J. (2018) · Synthese · doi:10.1007/s11229-016-1288-5 · Cited by 386 · see citing works
“Instead, cognition is accomplished by a canonical, ubiquitous microcircuit motif replicated across all sensory and cognitive domains in which specific classes of neurons reciprocally pass predictions and prediction errors across the global neuronal hierarchy (Bastos et al. 2012; Douglas et al. 1989).”
BackgroundSelf-citationArtificial Neural Networks for Neuroscientists: A Primer
Yang, G. R., & Wang, X. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.005 · Cited by 386 · see citing works
“Predictive coding Another canonical computation proposed for the brain is to compute predictions [Rao and Ballard, 1999, Bastos et al., 2012, Heilbron and Chait, 2018].”
BackgroundPerceptual Decision Making in Rodents, Monkeys, and Humans
Hanks, T. D., & Summerfield, C. (2017) · Neuron · doi:10.1016/j.neuron.2016.12.003 · Cited by 388 · see citing works
“The theory makes specific predictions about various types of neural response that would be observed in the cortical microcircuitry (Bastos et al., 2012), but single-cell researchers are only just beginning to engage with this hypothesis (Bell et al., 2016).”
BackgroundA guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI
Zeidman, P., Jafarian, A., Corbin, N., Seghier, M. L., Razi, A., Price, C. J., & Friston, K. J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.031 · Cited by 369 · see citing works
Self-citationExperience-dependent spatial expectations in mouse visual cortex
Fiser, A., Mahringer, D., Oyibo, H. K., Petersen, A. V., Leinweber, M., & Keller, G. B. (2016) · Nature Neuroscience · doi:10.1038/nn.4385 · Cited by 332 · see citing works
Evaluating the neurophysiological evidence for predictive processing as a model of perception
Walsh, K. S., McGovern, D. P., Clark, A., & O'Connell, R. G. (2020) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14321 · Cited by 333 · see citing works
The free energy principle for action and perception: A mathematical review
Buckley, C. L., Kim, C. S., McGregor, S., & Seth, A. K. (2017) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2017.09.004 · Cited by 352 · see citing works
“Here, the simple idea is that top-down cortical signalling conveys predictions while bottom-up activity returns prediction errors (Bastos et al., 2012).”
BackgroundInfluentialDelta–Beta Coupled Oscillations Underlie Temporal Prediction Accuracy
Arnal, L. H., Doelling, K. B., & Poeppel, D. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu103 · Cited by 361 · see citing works
“Importantly, our hypothetical model also sits comfortably within current thinking about predictive coding in the brain (Friston 2005; Bastos et al. 2012).”
BackgroundA voxel-wise encoding model for early visual areas decodes mental images of remembered scenes
Naselaris, T., Olman, C. A., Stansbury, D. E., Uǧurbil, K., & Gallant, J. L. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.018 · Cited by 360 · see citing works
“…has been advanced by the predictive coding theory of vision (Gregory, 1980; Rao and Ballard, 1999; Lee and Mumford, 2003; Yuille and Kersten, 2006; Bastos et al., 2012). ing to this theory feedback corresponds to the outputs of an internal, hierarchical model of the visual environment (see Berkes…”
Neural elements for predictive coding
Shipp, S. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01792 · Cited by 340 · see citing works
Answering Schrödinger's question: A free-energy formulation
Ramstead, M. J. D., Badcock, P. B., & Friston, K. J. (2018) · Physics of Life Reviews · doi:10.1016/j.plrev.2017.09.001 · Cited by 354 · see citing works
Self-citationFriston, K., & Frith, C. (2015) · Consciousness and Cognition · doi:10.1016/j.concog.2014.12.003 · Cited by 359 · see citing works
“In a companion paper (Friston & Frith, 2015), we demonstrate how communication facilitates long-term changes in generative models that are trying to predict each other.”
BackgroundSelf-citationRepetition suppression and its contextual determinants in predictive coding
Auksztulewicz, R., & Friston, K. (2016) · Cortex · doi:10.1016/j.cortex.2015.11.024 · Cited by 307 · see citing works
“This is consistent with the implementation of predictive coding within a canonical microcircuit (Bastos et al., 2012), where descending predictions are typically modelled as targeting inhibitory interneurons that enable superficial pyramidal cells to compare incoming sensory input with the…”
BackgroundMethodInfluentialSelf-citationEmotion words, emotion concepts, and emotional development in children: A constructionist hypothesis
Hoemann, K., Xu, F., & Barrett, L. F. (2019) · Developmental Psychology · doi:10.1037/dev0000686 · Cited by 354 · see citing works
“Following Bayesian logic (Bastos et al., 2012; Deneve, 2008), the prediction with the strongest prior probability might be confirmed by sensory inputs from the world and the body (Gallivan, Logan, Wolpert, & Flanagan, 2016).”
BackgroundCellular Mechanisms of Conscious Processing
Aru, J., Suzuki, M., & Larkum, M. E. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.07.006 · Cited by 321 · see citing works
“According to the most prominent implementations of PC [67,68], predictions sent from higher to lower levels of processing try to explain away activity propagating from lower to higher levels by suppressing the sensory signals that match the predictions.”
BackgroundVisual mismatch negativity: a predictive coding view
Stefanics, G., Kremláček, J., & Czigler, I. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00666 · Cited by 338 · see citing works
“Furthermore, audiovisual training has been shown to enhance auditory cortical discrimination accuracy, as indexed by MMN, and concurrently improve reading skills in children with dyslexia (Kujala et al., 2001).”
BackgroundNeurons along the auditory pathway exhibit a hierarchical organization of prediction error
Parras, G. G., Nieto-Diego, J., Carbajal, G. V., Valdés-Baizabal, C., Escera, C., & Malmierca, M. S. (2017) · Nature Communications · doi:10.1038/s41467-017-02038-6 · Cited by 344 · see citing works
Prior expectations induce prestimulus sensory templates
Kok, P., Mostert, P., & de Lange, F. P. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705652114 · Cited by 340 · see citing works
“Previous studies have suggested, both on theoretical (31) and on empirical (32, 33) grounds, that top-down (prediction) and bottom-up (stimulus-driven or prediction error) signals are subserved by distinct frequency bands.”
BackgroundMeyer, L. (2017) · European Journal of Neuroscience · doi:10.1111/ejn.13748 · Cited by 326 · see citing works
“…with the physiology of beta- and gamma-band oscillations within the columnar hierarchies of cortical information transmission: Beta-band oscillations are strongest in superficial cortical layers, whereas gamma-band oscillations are strongest in deep layers of the cortex (Bastos et al., 2012).”
BackgroundThe anatomy of choice: active inference and agency
Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00598 · Cited by 340 · see citing works
“However, there is one comforting point of convergence between variational Bayes and more neurobiologi-cally plausible predictive coding schemes (Bastos et al., 2012): this is the fact that the solution for both is exactly the same.”
BackgroundSelf-citationExpectation and Attention in Hierarchical Auditory Prediction
Chennu, S., Noreika, V., Gueorguiev, D., Blenkmann, A., Kochen, S., Ibanez, A., Owen, A. M., & Bekinschtein, T. A. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0114-13.2013 · Cited by 299 · see citing works
Learning enhances the relative impact of top-down processing in the visual cortex
Makino, H., & Komiyama, T. (2015) · Nature Neuroscience · doi:10.1038/nn.4061 · Cited by 318 · see citing works
Decoding sound and imagery content in early visual cortex
Vetter, P., Smith, F. W., & Muckli, L. (2014) · Current Biology · doi:10.1016/j.cub.2014.04.020 · Cited by 323 · see citing works
“, [6]), in line with predictive coding models [7–10].”
Electrophysiological source imaging: A noninvasive window to brain dynamics
He, B., Sohrabpour, A., Brown, E., & Liu, Z. (2018) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-062117-120853 · Cited by 297 · see citing works
“Although the findings need further replication and validation, conceivable implications include using the frequency information to infer directional networks and to investigate the dynamics and roles of feedforward versus feedback pathways against theories in computational neuroscience, such as predictive coding (189,…”
BackgroundLayer and rhythm specificity for predictive routing
Bastos, A. M., Lundqvist, M., Waite, A. S., Kopell, N., & Miller, E. K. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2014868117 · Cited by 268 · see citing works
“Deep-layer cortex (layers 5 and 6) feedback predictions using alpha/beta (1, 19).”
Self-citationFree Energy, Precision and Learning: The Role of Cholinergic Neuromodulation
Moran, R. J., Campo, P., Symmonds, M., Stephan, K. E., Dolan, R. J., & Friston, K. J. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.4255-12.2013 · Cited by 312 · see citing works
Self-citationNeural Encoding and Decoding with Deep Learning for Dynamic Natural Vision
Wen, H., Shi, J., Zhang, Y., Lu, K., Cao, J., & Liu, Z. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx268 · Cited by 297 · see citing works
“…dynamic natural vision involves feedforward, recurrent, and feedback connections (Callaway 2004), the CNN only models feedforward processing and operates on instantaneous input, without any account for recurrent or feedback network interactions (Bastos et al. 2012; Polack and Contreras 2012).”
BackgroundThe impact of the human thalamus on brain-wide information processing
Shine, J. M., Lewis, L. D., Garrett, D. D., & Hwang, K. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00701-0 · Cited by 308 · see citing works
Vuust, P., & Witek, M. A. G. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01111 · Cited by 298 · see citing works
“…cortex, rich in pyramidal cells, are responsible for forwarding prediction error upward in the system (driving), whereas mainly modulatory feedback connections from deeper layers provide predictions from higher cortical areas to suppress prediction errors at the lower levels (Bastos et al., 2012).”
BackgroundConsciousness in humans and non-human animals: recent advances and future directions
Boly, M., Seth, A. K., Wilke, M., Ingmundson, P., Baars, B., Laureys, S., Edelman, D. B., & Tsuchiya, N. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00625 · Cited by 291 · see citing works
“In the last few years, the concept of predictive coding has gained accelerating influence in the neuroscience community (Friston, 2010; Bastos et al., 2012).”
BackgroundIlluminating dendritic function with computational models
Poirazi, P., & Papoutsi, A. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-020-0301-7 · Cited by 303 · see citing works
Bastos, A. M., Loonis, R., Kornblith, S., Lundqvist, M., & Miller, E. K. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710323115 · Cited by 290 · see citing works
“The consistency and the specificity of these physiological effects suggest an underlying pattern of recurring neuronal dynamics shared between visual and frontal cortices (16, 17).”
BackgroundSelf-citationA tutorial on the free-energy framework for modelling perception and learning
Bogacz, R. (2017) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2015.11.003 · Cited by 283 · see citing works
“For example, Bastos et al. (2012) compared a more recent version of the model (Friston, 2008) with the details of the cortical organization.”
A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs
Dileep George, A., Lehrach, W. P., Kansky, K., Lázaro-Gredilla, M., Laan, C., Marthi, B., Lou, X., Meng, Z., Liu, Y., Wang, H., et al. (2017) · Science · doi:10.1126/science.aag2612 · Cited by 276 · see citing works
Visuomotor Coupling Shapes the Functional Development of Mouse Visual Cortex
Attinger, A., Wang, B., & Keller, G. B. (2017) · Cell · doi:10.1016/j.cell.2017.05.023 · Cited by 252 · see citing works
Whittington, J. C. R., & Bogacz, R. (2017) · Neural Computation · doi:10.1162/neco_a_00949 · Cited by 278 · see citing works
“The proposed predictive coding models are still quite abstract, and it is important to investigate if different linear or nonlinear nodes can be mapped on particular anatomically defined neurons within a cortical microcircuit (Bastos et al., 2012).”
BackgroundFontolan, L., Morillon, B., Liégeois‐Chauvel, C., & Giraud, A. (2014) · Nature Communications · doi:10.1038/ncomms5694 · Cited by 265 · see citing works
“Our findings confirmed that top-down neural flow uses lower frequency ranges than bottom-up, but also point to a more complex picture than the previously hypothesized gamma-up beta-down scheme 6, 18, 33, 34, 35, 36, 37 .”
InfluentialDynamic causal modelling revisited
Friston, K. J., Preller, K. H., Mathys, C., Cagnan, H., Heinzle, J., Razi, A., & Zeidman, P. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.045 · Cited by 271 · see citing works
Self-citationWords as social tools: language, sociality and inner grounding in abstract concepts
Borghi, A. M., Barca, L., Binkofski, F., Castelfranchi, C., Pezzulo, G., & Tummolini, L. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.12.001 · Cited by 253 · see citing works
Large-scale cortical networks for hierarchical prediction and prediction error in the primate brain
Chao, Z. C., Takaura, K., Wang, L., Fujii, N., & Dehaene, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.004 · Cited by 238 · see citing works
“However, one core hypothesis derived from the predictive-coding model has not yet been directly evaluated: the existence of simultaneous and interdependent computations of predictions and prediction errors, carried out by distinct and hierarchically organized neuronal populations (as proposed by Friston, 2005), and tr…”
- Authors
- Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J.
- Affiliations
- University of California, Davis (US)
- Wellcome Centre for Human Neuroimaging (GB)
- Radboud University Nijmegen (NL)
- Corresponding author
- Friston, Karl J.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2012-11
- Volume
- 76
- Issue
- 4
- Pages
- 695-711
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC3777738
- OpenAlex
- W2120079537
- Cited by
- 2,421
- References
- 166
- Citations by year
- 2026: 222 · 2025: 233 · 2024: 199 · 2023: 238 · 2022: 186 · 2021: 217 · 2020: 235 · 2019: 175 · 2018: 165 · 2017: 147 · 2016: 117 · 2015: 114 · 2014: 121 · 2013: 48 · 2012: 3
- FWCI
- 7.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 385 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- National Institutes of Health (1228535, EY013588, MH055714)
- Wellcome Trust
- National Science Foundation (2009090358)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- predictive coding, cortical microcircuitry, Bayesian inference, intrinsic connections, cortical columns, cortical connectivity
- MeSH terms
- Animals, Cerebral Cortex, Computer Simulation, Humans, Functional Laterality, Models, Neurological, Nerve Net, Neural Pathways, Feedback, Physiological
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