The DOI matches its Crossref record; 160 of the 166 references it cites were found in a registry or an index; 36 of the 125 sources its claims cite were checked for support.
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
Some cited sources support this claim and some don’t — read each source’s verdict below.
In the paper
“in predictive coding, attention is mediated by increasing the gain of populations encoding prediction error (Spratling, 2008; Feldman and Friston, 2010).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Disputedcites Spratling, 2008 sourceExperimental — accuracy not yet measured
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.
- Supportedcites Feldman and Friston, 2010 source
The Methods section says attention is mediated by modulation of the synaptic gain of cells conveying prediction error. The Discussion likewise identifies gain modulation of prediction-error units as the proposed attentional mechanism. These passages support the citing sentence’s paraphrase. (LLM, conf=0.91)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
- Disputed
Every source the paper cites for this claim was checked and supports it.
In the paper
“In both cases, driving-like responses as well as modulatory-like responses were observed (Covic and Sherman, 2011; De Pasquale and Sherman, 2011).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Supportedcites Covic and Sherman, 2011 source
The Discussion reports a mix of Class 1B (driver-like) and Class 2 (modulator-like) inputs in both directions between mouse A1 and A2v. This supports the auditory-cortex part attributed to Covic and Sherman; the V1/V2 case is covered by the other citation, not this one. (LLM, conf=0.93)
- Supportedcites De Pasquale and Sherman, 2011 source
The Terminology section identifies Class 1B as driving-like and Class 2 as modulatory-like. The Glutamatergic response classes section reports both classes in the V1-to-V2 and V2-to-V1 pathways. The Comparison section says a similar relationship was found in auditory cortex, which the citing sentence separately attributes to Covic and Sherman. (LLM, conf=0.92)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Partly supportedcites Markov et al., 2011 sourceExperimental — accuracy not yet measured
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)
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Supportedcites Harrison et al. 2007 source
The Discussion (page unavailable) reports that coherent motion suppressed striate-cortex responses even though each V1 classical receptive field received only one moving dot. It says the stimuli were indistinguishable when viewed one component at a time and attributes the response difference to input from higher areas, supporting the claim for the global-motion stimuli studied. (LLM, conf=0.87)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Supportedcites Garrido et al., 2009 source
The brief introduction defines MMN as the difference between electrophysiological responses to deviant and repeated standard events. The predictive-coding section explains that repetition reduces evoked responses, while an unlearned deviant produces a mismatch response because its input was not predicted. (LLM, conf=0.93)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Supportedcites Meyer and Olson, 2011 source
The Results report a significantly stronger population response to an unpredicted trailing image than to a predicted one (81 neurons, P = 1.5e-11). The Methods describe the stimuli as digitized images of natural and manmade objects. The finding is a population-level effect, not a claim that every neuron showed it. (LLM, conf=0.91)
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These studies show that predictable stimuli evoke smaller responses in early cortical areas.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Supportedcites Murray et al., 2002 source
The study reports reduced fMRI activity in primary visual cortex (V1) when visual elements form coherent shapes rather than random arrangements. The Discussion interprets this finding in predictive-coding terms: activity in lower areas is reduced when higher-level predictions match the incoming sensory information. (LLM, conf=0.86)
- Supportedcites Harrison et al., 2007 source
The Discussion reports reduced striate-cortex responses to coherent versus incoherent motion and explicitly describes the coherent stimuli as predictable. It also reports reduced V1 responses in a related fMRI study of coherent shapes. (LLM, conf=0.91)
- Not matchedcites Summerfield et al., 2008, 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Partly supportedcites Garrido et al., 2007 sourceExperimental — accuracy not yet measured
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)
- Supportedcites Wacongne et al., 2011 source
The Abstract describes mismatch responses as prediction errors and says a higher-order prediction should cancel an expected mismatch response. It also predicts a larger response when the mismatch is unexpected. This supports the citing sentence’s account of enhanced responses to deviants as a failure of higher-level prediction to suppress prediction-error activity. (LLM, conf=0.90)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: In predictive coding, feedforward connections carry prediction errors and feedback connections carry predictions that suppress lower-level prediction errors.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, recent studies show that cortical feedback connections express not only modulatory but also driving characteristics:”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Supportedcites Covic and Sherman, 2011 source
The Discussion reports a driver/modulator mix in both directions between mouse auditory areas A1 and A2v, with most A2v-to-A1 inputs classified as Class 1B (driver). This supports the claim that a cortical feedback connection can have driving as well as modulatory characteristics, though the paper cautions that more pathways must be studied to establish how typical this is. (LLM, conf=0.89)
- Supportedcites De Pasquale and Sherman, 2011 source
The Abstract reports both Class 1B (formerly “driver”) and Class 2 (formerly “modulator”) inputs in connections between V1 and V2 in both directions. The Comparison of the V1-to-V2 and V2-to-V1 pathways section specifically identifies a strong representation of Class 1B inputs in the presumed feedback pathway. Page numbers are not provided in our copy. (LLM, conf=0.93)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Similarly, violating expectations of auditory repetition causes enhanced gamma-band responses in early auditory cortex (Todorovic et al., 2011).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Supportedcites Todorovic et al., 2011 source
The Results section ('Expectation of repetition reduces auditory activity for repeated tones'; article pp. 9118–9123) reports significantly greater gamma-band power for unexpected than expected repetitions. The Discussion describes the larger gamma-band activity in auditory cortex as a response to unexpected sensory stimulation. (LLM, conf=0.94)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
- It may state two separate claims as one. 47
- Probably not a substantial claim. 14
- The quote only partly states this claim. 2
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > Introduction
What the sources say
- Not matchedcites Roopun et al., 2006, 2008
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Maier et al., 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites Buffalo et al., 2011 source
The Results (Coherence Differences Across Layers) report strong gamma-band spike-field coherence in superficial layers of V1, V2, and V4. Deep-layer coherence instead peaked around 10 Hz, within a measured band extending from alpha into low beta. (LLM, conf=0.91)
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
Every source the paper cites for this claim was checked and supports it.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Supportedcites Olsen et al., 2012 source
The paper reports that photo-stimulation of excitatory L6 neurons in V1 suppressed visually evoked responses in layers 2/3, 4 and 5. It also reports that photo-stimulating L6 in V1 reduced visually evoked activity in dLGN relay neurons. Both passages are labelled “Abstract” in our copy; page numbers are not provided. (LLM, conf=0.94)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Partly supportedcites Markov et al., 2011 sourceExperimental — accuracy not yet measured
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)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Supportedcites Olsen et al., 2012 source
The paper reports a 76±4% reduction in visually evoked dLGN activity and reductions of 81±5%, 84±3%, and 84±3% in V1 layers 2/3, 4, and 5, respectively. Both results followed L6 photo-stimulation at the strongest illumination (passages under “Gain control by layer six in cortical circuits of vision > Abstract”; page not provided). (LLM, conf=0.94)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Partly supportedcites Sherman and Guillery, 1998 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Not checked — no full text of the sourcecites Sherman and Guillery, 2011 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The remaining 5% represent cells giving rise to extrinsic connections, which – although sparse – can be extremely effective in driving their targets.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Partly supportedcites Markov et al., 2011 sourceExperimental — accuracy not yet measured
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)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > Microcircuits in the sensorimotor cortex
What the sources say
- Supportedcites Lefort et al., 2009 source
The Discussion lists the five most significant connections in the mouse C2 barrel column: three intralaminar connections, followed by ascending L4→L2 and descending L3→L5B. The claim's L3→L5 refers to the L5B connection identified in the paper. (LLM, conf=0.92)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Partly supportedcites Sherman and Guillery, 1998 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These L1 cells are almost all inhibitory and interconnect strongly with each other, via electrical connections and chemical synapses (Chu et al., 2003).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Partly supportedcites Chu et al., 2003 sourceExperimental — accuracy not yet measured
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)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > Feedforward and transthalamic connections
What the sources say
- Not checked — no full text of the sourcecites Yamaguchi and Knight, 1990 source
- Not checked — no full text of the sourcecites Crick, 1984 source
- Partly supportedcites Wurtz et al., 2011 sourceExperimental — accuracy not yet measured
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)
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
Every source the paper cites for this claim was checked and supports it.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The canonical microcircuit > Segregation of processing streams
What the sources say
- Supportedcites Buffalo et al., 2011 source
The Results report that spike-field coherence in superficial layers of visual areas V1, V2, and V4 peaked in the gamma band (about 40–60 Hz). In deep layers, gamma coherence was minimal and coherence peaked around 10 Hz in the alpha range in V1 and V2. No page numbers are supplied for these passages. (LLM, conf=0.94)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The canonical microcircuit > Segregation of processing streams
What the sources say
- Not checked — no full text of the sourcecites Fitzpatrick et al., 1994 source
- Supportedcites Briggs and Usrey, 2009 source
The Discussion (under “Feedforward and feedback pathways in the primate visual system”; page not provided) says upper-tier layer 6 corticogeniculate neurons project to parvocellular LGN layers, while lower-tier neurons project primarily to magnocellular layers. It describes these as anatomically segregated, stream-specific inputs, supporting the cited paraphrase. (LLM, conf=0.92)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > Conclusion
What the sources say
- Could not verifycites Markov et al 2011 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > Functional asymmetries in the microcircuit
What the sources say
- Partly supportedcites Friston 2008 sourceExperimental — accuracy not yet measured
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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > Introduction
What the sources say
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Since feedforward connections originate predominately from superficial layers and feedback connections from deep layers”
§ Canonical microcircuits for predictive coding > Introduction
What the sources say
- Not checked — no full text of the sourcecites Felleman and Van Essen, 1991 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > Intrinsic connections and the canonical microcircuit
What the sources say
- Not checked — no full text of the sourcecites Douglas and Martin, 1991 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This segregation can only arise from local neuronal computations that are structured and precisely interconnected.”
§ Canonical microcircuits for predictive coding > Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > Introduction
What the sources say
- Not matchedcites Helmholtz, 1860
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Gregory 1968, 1980
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Dayan et al., 1995 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“The idea that the brain actively constructs explanations for its sensory inputs is now generally accepted.”
§ Canonical microcircuits for predictive coding > Introduction
What the sources say
- Not matchedcites Helmholtz, 1860
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Gregory 1968, 1980
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Dayan et al., 1995 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Not matched
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > Intrinsic connections and the canonical microcircuit
What the sources say
- Not checked — no full text of the sourcecites Douglas et al., 1989 source
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“first, L4 inhibitory connections are more restricted in their lateral extent, relative to other layers.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > Intrinsic connections and the canonical microcircuit
What the sources say
- Not matchedcites Kätzel et al., 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Not checked — no full text of the sourcecites Felleman and van Essen 1991 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, normal subjects show a rapid adaptation to deviant stimuli as they become predictable – an effect not seen in prefrontal patients.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- 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.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > Intrinsic connections and the canonical microcircuit
What the sources say
- Not checked — no full text of the sourcecites Thomson et al 2002 source
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In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Not checked — no full text of the sourcecites Knight, 1984 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > Intrinsic connections and the canonical microcircuit
What the sources say
- Not checked — no full text of the sourcecites Thomson et al 2002 source
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > Intrinsic connections and the canonical microcircuit
What the sources say
- Not matchedcites Kätzel et al., 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“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.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > Microcircuits in the sensorimotor cortex
What the sources say
- Not checked — no full text of the sourcecites Weiler et al., 2008 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Current dogma holds that the cortex is hierarchically organized.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Not checked — no full text of the sourcecites Felleman and Van Essen, 1991 source
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Not checked — no full text of the sourcecites Felleman and Van Essen, 1991 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“in primates, parvocellular input enters the cortex primarily in layer 4Cβ, whereas magnocellular inputs enter in 4Cα.”
§ Canonical microcircuits for predictive coding > The canonical microcircuit > Segregation of processing streams
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > The canonical microcircuit > Segregation of processing streams
What the sources say
- Not checked — no full text of the sourcecites Roopun et al. (2006) source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > Predictive coding and the free energy principle
What the sources say
- Not checked — no full text of the sourcecites Friston, 2010 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Not checked — no full text of the sourcecites Melzer et al., 2012 source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The canonical microcircuit > Spatial segregation and cortical columns
What the sources say
- Not checked — no full text of the sourcecites Haeusler and Maass, 2007 source
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > The canonical microcircuit > Segregation of processing streams
What the sources say
- Not matchedcites Maier et al. (2010)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Superficial and deep layers are anatomically interconnected, but mounting evidence suggests that they constitute functionally distinct processing streams”
§ Canonical microcircuits for predictive coding > The canonical microcircuit > Segregation of processing streams
What the sources say
- Not checked — no full text of the sourcecites Roopun et al. (2006) source
- Not matchedcites Maier et al. (2010)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Not checked — no full text of the sourcecites Hupé et al., 1998 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > The canonical microcircuit > Segregation of processing streams
What the sources say
- Not matchedcites Maier et al. (2010)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Not checked — no full text of the sourcecites Rao and Ballard (1999) source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > The anatomy and physiology of cortical connections > The anatomy and physiology of extrinsic connections
What the sources say
- Not checked — no full text of the sourcecites Bullier et al., 1996 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > The cortical microcircuit and predictive coding
What the sources say
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > Conclusion
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > Functional asymmetries in the microcircuit
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The remaining populations are the excitatory and inhibitory interneurons in the supragranular layer, to which we assign expectations about hidden causes and states respectively.”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > The cortical microcircuit and predictive coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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,”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > The cortical microcircuit and predictive coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In summary, predictions are formed by a linear accumulation of prediction errors. Conversely, prediction errors are nonlinear functions of predictions.”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > Functional asymmetries in the microcircuit
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > The cortical microcircuit and predictive coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Superficial pyramidal cells provide feedforward connections, while deep pyramidal cells provide feedback connections.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > Functional asymmetries in the microcircuit
What the sources say
- Not matchedcites Roopun et al., 2006, 2008
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Maier et al., 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > Functional asymmetries in the microcircuit
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This leaves the inhibitory interneurons in the granular layer, which, for symmetry, we associate with prediction errors on the hidden states.”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > The cortical microcircuit and predictive coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“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).”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > The cortical microcircuit and predictive coding
What the sources say
- Not checked — no full text of the sourcecites Mumford (1992) source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- 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.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > Predictive coding and message passing
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > Predictive coding and the free energy principle
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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”
§ Canonical microcircuits for predictive coding > A canonical microcircuit for predictive coding > The cortical microcircuit and predictive coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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 cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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)
The cited source says what the claim attributes to it.
In the citing paper
“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)
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
““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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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 cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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 cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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 the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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).”
Citations
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“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).”
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BackgroundPreface: Cerebral Cortex Has Come of Age
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“We have previously discussed this in terms of attentional modulation, whichmay be intimately linked to the synchronization of presynaptic inputs and ensuing postsynaptic responses (Feldman and Friston, 2010; Fries et al., 2001).”
Self-citationThe Bayesian brain: The role of uncertainty in neural coding and computation
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“…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…”
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BackgroundMODALITY AND TOPOGRAPHIC PROPERTIES OF SINGLE NEURONS OF CAT'S SOMATIC SENSORY CORTEX
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“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
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The neural mechanisms of top-down attentional control
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Self-citationEntrainment of Neuronal Oscillations as a Mechanism of Attentional Selection
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Function of the thalamic reticular complex: the searchlight hypothesis
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The mismatch negativity: a review of underlying mechanisms
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Self-citationAttention, uncertainty, and free-energy
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“…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
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Neural syntax: cell assemblies, synapsembles, and readers
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InfluentialHigh-frequency, long-range coupling between prefrontal and visual cortex during attention
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“…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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“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).”
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InfluentialSequence regularity and geometry of orientation columns in the monkey striate cortex
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InfluentialPredictive coding: a fresh view of inhibition in the retina
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The pulvinar regulates information transmission between cortical areas based on attention demands
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“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
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On the computational architecture of the neocortex: I. The role of the thalamo-cortical loop
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“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×
“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
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×
“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
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×
“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×
“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
On the actions that one nerve cell can have on another: distinguishing “drivers” from “modulators”
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“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×
“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
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×
“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
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×
“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).”
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×
“…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…”
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 636 · cited 1×
“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).”
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 628 · cited 2×
“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).”
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
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 614 · cited 1×
“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).”
Laminar 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×
“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-citationLaminar 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 604 · cited 2×
“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×
“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×
“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×
“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×
“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×
“, 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×
“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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Dan, Y., Atick, J. J., & Reid, R. C. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-10-03351.1996 · Cited by 476 · cited 1×
“, 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×
“Feedback connections were typically seen when pyramidal cells in one layer targeted inhibitory cells in another (see Thomson and Bannister, 2003 for a review).”
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 447 · cited 7×
“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).”
InfluentialNeuronal 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×
“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×
“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).”
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 437
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×
“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).”
Attention 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×
“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).”
Thomson, A. M. (2002) · Cerebral Cortex · doi:10.1093/cercor/12.9.936 · Cited by 420 · cited 3×
“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×
“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×
“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×
“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×
“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
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×
“…(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×
“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
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×
“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×
“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×
“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×
“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×
“…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×
“…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×
“…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×
“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×
“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×
“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×
“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×
“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×
“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×
“Furthermore, simple cells project monosynaptically onto complex cells, where they exert a strong feedforward influence (Alonso and Martinez, 1998; Alonso, 2002).”
BackgroundUllman, S. (1995) · Cerebral Cortex · doi:10.1093/cercor/5.1.1 · Cited by 269 · cited 1×
“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).”
Dissociable 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×
“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
Raizada, R. D. S. (2002) · Cerebral Cortex · doi:10.1093/cercor/13.1.100 · Cited by 263 · cited 1×
“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×
“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×
“…(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×
“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×
“, 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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“…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×
“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×
“…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×
“…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×
“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×
“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).”
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×
“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×
“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×
“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×
“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×
“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×
“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×
“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 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×
“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
Callaway, E. M., & Wiser, A. K. (1996) · Visual Neuroscience · doi:10.1017/s0952523800009159 · Cited by 112 · cited 2×
“%) 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×
“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×
“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×
“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×
“…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
Usrey, W., & Fitzpatrick, D. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-03-01203.1996 · Cited by 90
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×
“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 88 · cited 1×
“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×
“…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
Johnson, R. R., Jiang, X., & Burkhalter, A. (1996) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(19960506)368:3 · Cited by 62 · cited 1×
“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 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×
“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×
“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
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×
“%) 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 37 · cited 1×
“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×
“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×
“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
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×
“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×
“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
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
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 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
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
“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
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
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
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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