Contrast gain control and horizontal interactions in V1: A DCM study
Open accessAbstract
Using high-density electrocorticographic recordings - from awake-behaving monkeys - and dynamic causal modelling, we characterised contrast dependent gain control in visual cortex, in terms of synaptic rate constants and intrinsic connectivity. Specifically, we used neural field models to quantify the balance of excitatory and inhibitory influences; both in terms of the strength and spatial dispersion of horizontal intrinsic connections. Our results allow us to infer that increasing contrast increases the sensitivity or gain of superficial pyramidal cells to inputs from spiny stellate populations. Furthermore, changes in the effective spatial extent of horizontal coupling nuance the spatiotemporal filtering properties of cortical laminae in V1 - effectively preserving higher spatial frequencies. These results are consistent with recent non-invasive human studies of contrast dependent changes in the gain of pyramidal cells elaborating forward connections - studies designed to test specific hypotheses about precision and gain control based on predictive coding. Furthermore, they are consistent with established results showing that the receptive fields of V1 units shrink with increasing visual contrast.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Computational modeling studies have shown the plausibility of superficial gamma circuits to engage in prediction error computations.
“In addition, computational modeling studies have shown the plausibility of superficial gamma circuits to engage in prediction error computations (38, 41, 42).”
From Layer and rhythm specificity for predictive routing (2020) · cites this paper as [42]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783 · cited 1× · see citing works
“For the analyses of this paper, we use those sites in the primary visual cortex that showed high sensitivity to the stimuli.”
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583 · cited 1× · see citing works
“To address this relationship, we used dynamic causal modelling (DCM; Friston et al., 2003) to explain visually induced responses to stimuli of different contrast levels.”
Self-citationCanonical Microcircuits for Predictive Coding
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.038 · Cited by 2,421 · cited 5× · see citing works
“Bastos et al. (2012) compared the canonical microcircuit model from the Haeusler and Maass model to the theoretically-predicted circuit based on predictive coding(Friston,2008).”
InfluentialSelf-citationNEURONAL CIRCUITS OF THE NEOCORTEX
Douglas, R. J., & Martin, K. A. C. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144152 · Cited by 1,886 · cited 3× · see citing works
“Lastly, in their revised model, dense (lateral) interconnections amongst the superficial pyramidal cells allow these cells to sample their diverse inputs on the dendritic tree and implement a version of a winner-take-all algorithm (Douglas and Martin, 2004).”
InfluentialAttention, uncertainty, and free-energy
Feldman, H., & Friston, K. J. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00215 · Cited by 1,365 · cited 4× · see citing works
“These changes have been interpreted – in the context of predictive coding – as changes in the gain of superficial pyramidal cells reporting prediction error (Feldman and Friston, 2010).”
InfluentialSelf-citationThe dynamic brain: from spiking neurons to neural masses and cortical fields
Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M., & Friston, K. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000092 · Cited by 1,084 · cited 1× · see citing works
“Neural fi elds have a long and illustrious history in mathematical neuroscience, see Deco et al. (2008) and Nunez (1995) for a review.”
Self-citationNeocortical Dynamics and Human EEG Rhythms
Nunez, P. L., & Williamson, S. J. (1996) · Physics Today · doi:10.1063/1.2807585 · Cited by 1,005 · see citing works
Haider, B., Duque, A., Hasenstaub, A. R., & McCormick, D. A. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.5297-05.2006 · Cited by 962 · see citing works
Hierarchical Models in the Brain
Friston, K. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000211 · Cited by 952 · cited 2× · see citing works
“Bastos et al. (2012) compared the canonical microcircuit model from the Haeusler and Maass model to the theoretically-predicted circuit based on predictive coding(Friston,2008).”
Self-citationNeocortical Dynamics and Human EEG Rhythms
Collura, T. F. (1996) · Journal of Clinical Neurophysiology · doi:10.1097/00004691-199603000-00010 · Cited by 896 · see citing works
Variational free energy and the Laplace approximation
Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J., & Penny, W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.08.035 · Cited by 880 · cited 3× · see citing works
“A full description of these schemes can be found in Friston et al. (2007).”
InfluentialSelf-citationA Quantitative Map of the Circuit of Cat Primary Visual Cortex
Binzegger, T., Douglas, R. J., & Martin, K. A. C. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.1400-04.2004 · Cited by 879 · see citing works
The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex
Lefort, S., Tomm, C., Floyd Sarria, J.-C., & Petersen, C. C. H. (2009) · Neuron · doi:10.1016/j.neuron.2008.12.020 · Cited by 875 · cited 2× · see citing works
“Whilst this core circuit has been established mostly for primary visual cortex (which is also our focus here), recent studies have also demonstrated that other cortical areas such as primary somatosensory and primary motor cortex share similar circuit properties (Lefort et al., 2009; Weiler et al., 2008).”
Brunel, N., & Wang, X. (2003) · Journal of Neurophysiology · doi:10.1152/jn.01095.2002 · Cited by 854 · see citing works
Attentional 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 · cited 2× · see citing works
“…selective responses, we will see below that the use of a biophysically informed model allows us to infer that contrast dependent modulation of the effective spatial extent of neuronal connections contributes to the observed gamma response (a result that accords with single cell studies).”
Self-citationContrast's effect on spatial summation by macaque V1 neurons
Sceniak, M. P., Ringach, D. L., Hawken, M. J., & Shapley, R. M. (1999) · Nature Neuroscience · doi:10.1038/11197 · Cited by 608 · cited 1× · see citing works
“For example, Sceniak et al. (1999) recorded single units in V1 when monkeys fixated a point on the screen and were shown patches of drifting gratings at various contrasts and sizes.”
BackgroundLaminar differences in gamma and alpha coherence in the ventral stream
Buffalo, E. A., Fries, P., Landman, R., Buschman, T. J., & Desimone, R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1011284108 · Cited by 606 · cited 3× · see citing works
“This separates the sources of forward and backward connections in cortical hierarchies and has proved useful when trying to explain several aspects of distributed cortical computations in theoretical neurobiology (Bastos et al., 2012; Buffalo et al., 2011).”
Self-citationInterlaminar Connections in the Neocortex
Thomson, A. M. (2002) · Cerebral Cortex · doi:10.1093/cercor/13.1.5 · Cited by 468 · cited 1× · see citing works
“For example Thomson and Bannister (2003) combined multiple whole-cell recordings with histology to identify theprobabilityof fi ndinginterconnectedcellsbetweenparticularlayers, and their synaptic strengths.”
A Combined MRI and Histology Atlas of the Rhesus Monkey Brain: In Stereotaxic Coordinates
Saleem, K. S., & Logothetis, N. K. (2012) · Max Planck Digital Library · Cited by 446 · see citing works
Differences in Gamma Frequencies across Visual Cortex Restrict Their Possible Use in Computation
Ray, S., & Maunsell, J. H. R. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.004 · Cited by 439 · cited 1× · see citing works
“Gamma band oscillations (30–100 Hz) in V1 are also sensitive to stimulus properties like contrast (and stimulus size) (Ray and Maunsell, 2010).”
BackgroundA MEMS-based flexible multichannel ECoG-electrode array
Rubehn, B., Bosman, C., Oostenveld, R., Fries, P., & Stieglitz, T. (2009) · Journal of Neural Engineering · doi:10.1088/1741-2560/6/3/036003 · Cited by 387 · see citing works
Self-citationStimulus contrast modulates functional connectivity in visual cortex
Nauhaus, I., Busse, L., Carandini, M., & Ringach, D. L. (2008) · Nature Neuroscience · doi:10.1038/nn.2232 · Cited by 380 · cited 2× · see citing works
“Contents lists available at ScienceDirect NeuroImage j ourna l homepage: www.e lsev ie r .com/ locate /yn img Contrast gain control and horizontal interactions in V1: A DCM study D.A. Pinotsis a,⁎, N. Brunet c,e, A. Bastos b,f, C.A. Bosman c,d, V. Litvak a, P. Fries b,c,1, K.J. Friston a,1 a The Wellcome Trust Centre…”
BackgroundTraub, R. D., Kopell, N., Bibbig, A., Buhl, E. H., LeBeau, F. E. N., & Whittington, M. A. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-23-09478.2001 · Cited by 357 · cited 1× · see citing works
“Gamma band oscillations have been found to depend on neuronal interactions mediated by both gap junctions and synaptic transmission (Traub et al., 2001).”
Top-down laminar organization of the excitatory network in motor cortex
Weiler, N., Wood, L., Yu, J., Solla, S. A., & Shepherd, G. M. G. (2008) · Nature Neuroscience · doi:10.1038/nn2049 · Cited by 330 · cited 2× · see citing works
“Whilst this core circuit has been established mostly for primary visual cortex (which is also our focus here), recent studies have also demonstrated that other cortical areas such as primary somatosensory and primary motor cortex share similar circuit properties (Lefort et al., 2009; Weiler et al., 2008).”
Dynamics of spatial summation in primary visual cortex of alert monkeys
Kapadia, M. K., Westheimer, G., & Gilbert, C. D. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.21.12073 · Cited by 303 · cited 3× · see citing works
“The underlying assumption here is that the cortex acts as a dynamic fi lter of visual stimuli — that shows rapid nonlinear adaptation and where the local centre – surround interactions determine the frequency of gamma oscillations (Kapadia et al., 1999).”
InfluentialVisual Spatial Characterization of Macaque V1 Neurons
Sceniak, M. P., Hawken, M. J., & Shapley, R. M. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.85.5.1873 · Cited by 288 · cited 1× · see citing works
“It is well-known that the local processing of information in the visual cortex is highly sensitive to the contrast of the stimuli being processed; see e.g. Sceniak et al. (2001, 2006).”
BackgroundDendritic morphology of pyramidal neurones of the visual cortex of the rat: III. Spine distributions
Larkman, A. U. (1991) · The Journal of Comparative Neurology · doi:10.1002/cne.903060209 · Cited by 274 · cited 2× · see citing works
“For the analyses of this paper, we use those sites in the primary visual cortex that showed high sensitivity to the stimuli.”
Robust Gamma Coherence between Macaque V1 and V2 by Dynamic Frequency Matching
Roberts, M. J., Lowet, E., Brunet, N. M., Wal, M. T., Tiesinga, P. H. E., Fries, P., & Weerd, P. D. (2013) · Neuron · doi:10.1016/j.neuron.2013.03.003 · Cited by 253 · cited 1× · see citing works
“With increasing contrast, gamma oscillations increase in peak frequency (see also Roberts et al., 2013).”
Self-citationHaeusler, S., & Maass, W. (2006) · Cerebral Cortex · doi:10.1093/cercor/bhj132 · Cited by 221 · cited 2× · see citing works
“Haeusler and Maass used Hodgkin and Huxley neurons to build a realistic microcircuit model and showed that a cortical column – whose connectivity conforms to the canonical microcircuit – can perform various computations more efficiently, in relation to a column with random connectivity (Haeusler and Maass, 2007).”
Simulation of Gamma Rhythms in Networks of Interneurons and Pyramidal Cells
Traub, R. D., Jefferys, J. G. R., & Whittington, M. A. (1997) · Journal of Computational Neuroscience · doi:10.1023/a:1008839312043 · Cited by 205 · cited 1× · see citing works
“This model can generate differences in gamma responses that depend upon stimulus conditions that are thought to result from the interaction between local excitation and inhibition, (Brunel and Wang, 2003; Kang et al., 2010; Traub et al., 1997).”
DCM for complex-valued data: Cross-spectra, coherence and phase-delays
Friston, K. J., Bastos, A. M., Vladimir Litvak, A., Stephan, K. E., Fries, P., & Moran, R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.048 · Cited by 135 · cited 3× · see citing works
“This is an important aspect of neural mass modelling of complex cross spectra in the time domain (see Friston et al 2012).”
BackgroundInfluentialSelf-citationBayesian estimation of synaptic physiology from the spectral responses of neural masses
Moran, R. J., Stephan, K. E., Kiebel, S. J., Rombach, N., O'Connor, W. T., Murphy, K. J., Reilly, R. B., & Friston, K. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.01.025 · Cited by 126 · cited 2× · see citing works
“In the context of dynamic causal modelling, these quantities have been shown to accurately reflect neurotransmitter density and synaptic efficacy (Moran et al., 2008).”
Self-citationDynamic causal modeling with neural fields
Pinotsis, D. A., Moran, R., & Friston, K. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.020 · Cited by 101 · cited 7× · see citing works
“The advantage of neural fi eld models is that they can accommodate spatially extended activity on cortical manifold or patches that endows the predicted responses with a complicated frequency dependency (for a further discussion, see Pinotsis et al., 2012).”
InfluentialSelf-citationJocoy, E. L., André, V. M., Cummings, D. M., Rao, S. P., Wu, N., Ramsey, A. J., Caron, M. G., Cepeda, C., & Levine, M. S. (2011) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2011.00004 · Cited by 80 · cited 2× · see citing works
“This separates the sources of forward and backward connections in cortical hierarchies and has proved useful when trying to explain several aspects of distributed cortical computations in theoretical neurobiology (Bastos et al., 2012; Buffalo et al., 2011).”
Dynamic causal modelling of precision and synaptic gain in visual perception — an EEG study
Brown, H. R., & Friston, K. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.044 · Cited by 79 · see citing works
Self-citationLFP spectral peaks in V1 cortex: network resonance and cortico-cortical feedback
Kang, K., Shelley, M., Henrie, J. A., & Shapley, R. M. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-009-0190-2 · Cited by 76 · cited 2× · see citing works
“This model can generate differences in gamma responses that depend upon stimulus conditions that are thought to result from the interaction between local excitation and inhibition, (Brunel and Wang, 2003; Kang et al., 2010;Traubetal.,1997).”
Cholinergic neuromodulation controls directed temporal communication in neocortex in vitro
Roopun, A. K. (2010) · Frontiers in Neural Circuits · doi:10.3389/fncir.2010.00008 · Cited by 75 · see citing works
Dynamic causal modelling of lateral interactions in the visual cortex
Pinotsis, D. A., Schwarzkopf, D. S., Vladimir Litvak, A., Rees, G. E., Barnes, G. R., & Friston, K. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.078 · Cited by 64 · cited 5× · see citing works
“Examplesofmorerealisticdynamiccausalmodellinginthiscontext can be found in Marreiros et al. (2010) and Pinotsis et al. (2013b).”
InfluentialSelf-citationVisual Spatial Summation in Macaque Geniculocortical Afferents
Sceniak, M. P., Chatterjee, S., & Callaway, E. M. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00734.2006 · Cited by 54 · cited 1× · see citing works
“It is well-known that the local processing of information in the visual cortex is highly sensitive to the contrast of the stimuli being processed; see e.g. Sceniak et al. (2001, 2006).”
A dynamic causal model study of neuronal population dynamics
Marreiros, A. C., Kiebel, S. J., & Friston, K. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.098 · Cited by 53 · cited 1× · see citing works
“Examplesofmorerealisticdynamiccausalmodellinginthiscontext can be found in Marreiros et al. (2010) and Pinotsis et al. (2013b).”
Self-citationMaris, E., Womelsdorf, T., Desimone, R., & Fries, P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.007 · Cited by 41 · cited 1× · see citing works
“Our data suggest that there was no induced gamma peak for the two lowest contrasts and the corresponding spectra were rather featureless: but see also (Maris et al., 2013).”
BackgroundSelf-citationSpontaneous pattern formation and pinning in the primary visual cortex
Baker, T. I., & Cowan, J. D. (2009) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2009.05.011 · Cited by 39 · cited 2× · see citing works
“This is a ubiquitous choice in the neural fi eld literature, motivated by the sparse (heavy tailed) distribution of horizontal connections in sensory cortices: for more details, see (Baker and Cowan, 2009).”
On conductance-based neural field models
Pinotsis, D. A., Leite, M., & Friston, K. J. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00158 · Cited by 24 · cited 5× · see citing works
“Examplesofmorerealisticdynamiccausalmodellinginthiscontext can be found in Marreiros et al. (2010) and Pinotsis et al. (2013b).”
InfluentialSelf-citation
Flagged first, then by citations
Research works printed but not linked: 2
- Bastos, A.M., PhD Thesis, University of California Davis (2013)
- Chavane, 2011, Lateral spread of orientation selectivity in V1 is controlled by intracortical cooperativity, Front. Syst. Neurosci., 5
Cited by
Sorted by citations
The graphical brain: Belief propagation and active inference
Friston, K. J., Parr, T., & de Vries, B. (2017) · Network Neuroscience · doi:10.1162/netn_a_00018 · Cited by 415 · see citing works
“…attention (Auksztulewicz & Friston, 2015; Bauer, Stenner, Friston, & Dolan, 2014; Brown, Adams, Parees, Edwards, & Friston, 2013; Kanai, Komura, Shipp, & Friston, 2015; Pinotsis et al., 2014); namely, a precision engineered gain control of prediction error units (i.e., superficial pyramidal cells).”
BackgroundSelf-citationLayer and rhythm specificity for predictive routing
Bastos, A. M., Lundqvist, M., Waite, A. S., Kopell, N., & Miller, E. K. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2014868117 · Cited by 268 · see citing works
“In addition, computational modeling studies have shown the plausibility of superficial gamma circuits to engage in prediction error computations (38, 41, 42).”
Self-citationMejias, J. F., Murray, J. D., Kennedy, H., & Wang, X.-J. (2016) · Science Advances · doi:10.1126/sciadv.1601335 · Cited by 227 · see citing works
Top-Down Beta Enhances Bottom-Up Gamma
Richter, C. G., Thompson, W. H., Bosman, C. A., & Fries, P. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3771-16.2017 · Cited by 195 · see citing works
Self-citationLFP and oscillations—what do they tell us?
Friston, K. J., Bastos, A. M., Pinotsis, D. A., & Vladimir Litvak, A. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.05.004 · Cited by 187 · see citing works
“These relationships have been examined using Bayesian model comparison of dynamic causal models that embody competing hypotheses about how visual contrast effects lateral interactions [32].”
Self-citationA theta rhythm in macaque visual cortex and its attentional modulation
Spyropoulos, G., Bosman, C. A., & Fries, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719433115 · Cited by 178 · see citing works
Self-citationBastos, A. M., Litvak, V., Moran, R., Bosman, C. A., Fries, P., & Friston, K. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.081 · Cited by 152 · see citing works
“, 2013) and ECoG within-area cross-spectral densities (Pinotsis et al., 2014).”
BackgroundMethodSelf-citationComputational psychiatry: from synapses to sentience
Friston, K. J. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01743-z · Cited by 158 · see citing works
“, by targeting inhibitory interneurons that are coupled to the superficial pyramidal cells broadcasting prediction errors [78, 79].”
BackgroundSelf-citationGamma Synchronization between V1 and V4 Improves Behavioral Performance
Rohenkohl, G., Bosman, C. A., & Fries, P. (2018) · Neuron · doi:10.1016/j.neuron.2018.09.019 · Cited by 103 · see citing works
Self-citationCortical layers, rhythms and BOLD signals
Scheeringa, R., & Fries, P. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.002 · Cited by 88 · see citing works
“, 2003) as well as electrophysiology (Bastos et al., 2015a; Friston et al., 2012; Pinotsis et al., 2014).”
Self-citationRanlund, S., Adams, R. A., Díez, Á., Constante, M., Dutt, A., Hall, M., Maestro Carbayo, A., McDonald, C., Petrella, S., Schulze, K., et al. (2016) · Human Brain Mapping · doi:10.1002/hbm.23035 · Cited by 77 · see citing works
“Given the prominence of NMDA-Rs in superficial cortical layers, it is unsurprising that the gain of superficial pyramidal cell populations is strongly affected by NMDAR function [Fox et al., 1990; Pinotsis et al., 2014].”
BackgroundSelf-citationExpectation violation and attention to pain jointly modulate neural gain in somatosensory cortex
Fardo, F., Auksztulewicz, R., Allen, M., Dietz, M. J., Roepstorff, A., & Friston, K. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.041 · Cited by 59 · see citing works
“This interpretation appears to have a degree of validity in relation to attentional gain and enjoys the support of several empirical studies (e.g., Fogelson et al., 2014; Pinotsis et al., 2014; Auksztulewicz and Friston, 2015; Vossel et al., 2015).”
ResultSelf-citationBayesian population receptive field modelling
Zeidman, P., Silson, E. H., Schwarzkopf, D. S., Baker, C. I., & Penny, W. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.008 · Cited by 70 · see citing works
The Cumulative Effects of Predictability on Synaptic Gain in the Auditory Processing Stream
Auksztulewicz, R., Barascud, N., Cooray, G., Nobre, A. C., Chait, M., & Friston, K. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.0291-17.2017 · Cited by 55 · see citing works
Self-citationIn vivo ephaptic coupling allows memory network formation
Pinotsis, D. A., & Miller, E. K. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad251 · Cited by 62 · see citing works
“The ephaptic model isusedtwicebelow:first,in“Methods,”toderivethemathematical expression of ephaptic coupling and then, in “Results,” to find evidence of ephaptic coupling using BMC.”
InfluentialSelf-citationCytoelectric coupling: Electric fields sculpt neural activity and “tune” the brain’s infrastructure
Pinotsis, D. A., Fridman, G. Y., & Miller, E. K. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102465 · Cited by 64 · see citing works
“…, including working memory (Miller et al., 2018; Pinotsis et al., 2018), attention (Fiebelkorn and Kastner, 2019; Buschman and Miller, 2007; Lakatos et al., 2008; Pinotsis et al., 2014; Fries et al., 2001; Gregoriou et al., 2009), and predictive coding (Bastos et al., 2020; Pinotsis et al., 2017b).”
BackgroundSelf-citationBayesian fusion and multimodal DCM for EEG and fMRI
Wei, H., Jafarian, A., Zeidman, P., Vladimir Litvak, A., Razi, A., Hu, D., & Friston, K. J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116595 · Cited by 54 · see citing works
Self-citationWorking Memory Load Modulates Neuronal Coupling
Pinotsis, D. A., Buschman, T. J., & Miller, E. K. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy065 · Cited by 51 · see citing works
“This process has also been described in detail in several earlier publications (Friston et al. 2012; Pinotsis et al. 2014).”
InfluentialSelf-citationGeneric dynamic causal modelling: An illustrative application to Parkinson's disease
van Wijk, B. C. M., Cagnan, H., Vladimir Litvak, A., Kühn, A. A., & Friston, K. J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.039 · Cited by 47 · see citing works
Self-citationShaw, A. D., Moran, R., Muthukumaraswamy, S. D., Brealy, J., Linden, D. E. J., Friston, K. J., & Singh, K. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.034 · Cited by 48 · see citing works
“Indeed the CMC used here is based upon the model and populations in (22, 23, 32, 34, 36, 62), differing only in the local coupling among populations.”
Self-citationCharacterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling
Cooray, G., Sengupta, B., Douglas, P. K., Englund, M., Wickström, R., & Friston, K. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.064 · Cited by 43 · see citing works
“These cell populations are the sources of forward and backward extrinsic (betweensource) connections respectively (Pinotsis et al., 2014).”
BackgroundSelf-citationAn Investigation of the Free Energy Principle for Emotion Recognition
Demekas, D., Parr, T., & Friston, K. J. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00030 · Cited by 41 · see citing works
“…why superficial pyramidal cells have so many synaptic gain control mechanisms (NMDA receptors, D1 dopamine receptors, etc.), in order to set the gain or precision of prediction errors—thought to be encoded by superficial pyramidal cells (Pinotsis et al., 2014; Kanai et al., 2015; Shipp, 2016).”
BackgroundSelf-citationPinotsis, D. A., Geerts, J. P., Pinto, L., FitzGerald, T. H. B., Vladimir Litvak, A., Auksztulewicz, R., & Friston, K. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.041 · Cited by 38 · see citing works
“In this setting, laminar LFP responses y i – of the sort measured with multi-electrode shanks – are generated by contributions from excitatory and inhibitory populations that occupy one or more cortical layers, see also (Pinotsis et al., 2012b, 2014).”
BackgroundMethodSelf-citationCortical beta oscillations reflect the contextual gating of visual action feedback
Limanowski, J., Vladimir Litvak, A., & Friston, K. J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117267 · Cited by 40 · see citing works
Self-citationResting-state neural activity and connectivity associated with subjective happiness
Sato, W., Kochiyama, T., Uono, S., Sawada, R., Kubota, Y., Yoshimura, S., & Toichi, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-48510-9 · Cited by 39 · see citing works
Zarka, D., Cevallos, C., Petieau, M., Hoellinger, T., Dan, B., & Cheron, G. A. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00169 · Cited by 34 · see citing works
“The effects of contrast on the induced rhythms are complex and specifically influence the postsynaptic gain of neuronal populations, the strength of intrinsic and horizontal connectivity which can be differentially engaged depending on stimulus properties (Pinotsis et al., 2014).”
BackgroundFunctional constraints in the evolution of brain circuits
Bosman, C. A., & Aboitiz, F. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00303 · Cited by 34 · see citing works
“Pinotsis et al. (2014) were able to reproduce stimulus contrast dependences in neuronal responses and track their origins to the pyramidal neurons with forward projections.”
BackgroundMethodSelf-citationHamburg, S., Rosch, R. E., Startin, C. M., Friston, K. J., & Strydom, A. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz043 · Cited by 27 · see citing works
Self-citationLuu, P., Tucker, D. M., & Friston, K. J. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad458 · Cited by 27 · see citing works
Self-citationDíez, Á., Ranlund, S. M., Pinotsis, D. A., Calafato, S., Shaikh, M., Hall, M., Walshe, M., Nevado, Á., Friston, K. J., Adams, R. A., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23588 · Cited by 25 · see citing works
“Crucially, synaptic gain is parameterized as the intrinsic—or self-inhibitory— connectivity of superficial pyramidal cell populations in DCM [Bastos et al., 2012; Friston, 2008; Pinotsis et al., 2014; Stephan et al., 2006].”
Self-citationModel-based prediction of muscarinic receptor function from auditory mismatch negativity responses
Schöbi, D., Homberg, F., Frässle, S., Endepols, H., Moran, R. J., Friston, K. J., Tittgemeyer, M., Heinzle, J., & Stephan, K. E. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118096 · Cited by 25 · see citing works
Self-citationPinotsis, D. A., Perry, G., Vladimir Litvak, A., Singh, K. D., & Friston, K. J. (2016) · Human Brain Mapping · doi:10.1002/hbm.23331 · Cited by 20 · see citing works
“, 2012; Alitto and Usrey, 2008], which optimize the precision or gain of units encoding visual prediction errors: see [Pinotsis et al., 2014] for a discussion of the role of gain and precision in contrast effects.”
BackgroundMethodInfluentialSelf-citationDirect social perception, mindreading and Bayesian predictive coding
de Bruin, L., & Strijbos, D. (2015) · Consciousness and Cognition · doi:10.1016/j.concog.2015.04.014 · Cited by 19 · see citing works
fMRI Evidence for Default Mode Network Deactivation Associated with Rapid Eye Movements in Sleep
Hong, C. C.-H., Fallon, J., & Friston, K. J. (2021) · Brain Sciences · doi:10.3390/brainsci11111528 · Cited by 18 · see citing works
Self-citationPerry, G., Randle, J. M., Koelewijn, L., Routley, B. C., & Singh, K. D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124798 · Cited by 17 · see citing works
“Recent modelling has begun to identify some of the parameters which are critical in determining the contrast gain of gamma [38].”
BackgroundBayesian Modelling of Induced Responses and Neuronal Rhythms
Pinotsis, D. A., Loonis, R. F., Bastos, A. M., Miller, E. K., & Friston, K. J. (2016) · Brain Topography · doi:10.1007/s10548-016-0526-y · Cited by 17 · see citing works
“For the explicit form of the transfer functions T ð k ; x ; h ð 1 Þ Þ we refer the interested reader to (Pinotsis et al. 2014).”
BackgroundInfluentialSelf-citationExcitatory versus inhibitory feedback in Bayesian formulations of scene construction
Abadi, A. K., Yahya, K., Amini, M., Friston, K. J., & Heinke, D. G. (2019) · Journal of The Royal Society Interface · doi:10.1098/rsif.2018.0344 · Cited by 16 · see citing works
“There are also several examples in the literature that use dynamic causal modelling to disambiguate between inhibitory and excitatory connections in cortical hierarchies [35–41].”
BackgroundSelf-citationInteroceptive Technologies for Psychiatric Interventions: From Diagnosis to Clinical ApplicationsPreprint
Schoeller, F., Horowitz, A. H., Maes, P., Jain, A., Reggente, N., Christov‐Moore, L., Trousselard, M., Klein, A., Barca, L., Pezzulo, G., et al. (2022) · doi:10.31234/osf.io/sqr6z · Cited by 15 · see citing works
Self-citationThalamocortical inhibitory dynamics support conscious perception
Min, B.-K., Kim, H. S., Pinotsis, D. A., & Pantazis, D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117066 · Cited by 14 · see citing works
Self-citationOn self-feedback connectivity in neural mass models applied to event-related potentials
Youssofzadeh, V., Prasad, G., & Wong‐Lin, K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.067 · Cited by 14 · see citing works
“This experimental study alongwith other recent works by Bastos et al. (2012) and Pinotsis et al. (2014) suggest that the non-local inhibitory neurons implicitly behave as an effective negative feedback to excitatory cell populations.”
Kumar, V. G., Dutta, S., Talwar, S., Roy, D., & Banerjee, A. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14747 · Cited by 12 · see citing works
“, prediction errors) is thought to be mediated by intrinsic excitability (or self-inhibition; Bhatt et al., 2016; Brown & Friston, 2012; Pinotsis et al., 2014).”
BackgroundTemporal uncertainty enhances suppression of neural responses to predictable visual stimuli
Nara, S., Lizarazu, M., Richter, C. G., Dima, D. C., Cichy, R. M., Bourguignon, M., & Molinaro, N. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118314 · Cited by 8 · see citing works
“…of in terms of the precision of prediction errors, i.e., an estimate of predictability ( Ainley et al., 2016 ; Auksztulewicz and Friston, 2016 ; FitzGerald et al., 2015 ; Haarsma et al., 2020 ; Kok et al., 2012 ; Pinotsis et al., 2014 ; Shipp, 2016 ; Spratling, 2017 ; Sterzer et al., 2018 ).”
BackgroundOn memories, neural ensembles and mental flexibility
Pinotsis, D. A., Brincat, S. L., & Miller, E. K. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.068 · Cited by 10 · see citing works
“These can be accommodated by using the effective connectivity parameters obtained here as priors to fit a more complicated biophysical model that accounts for volume conduction effects (Pinotsis et al., 2014).”
Self-citationThe Functional Anatomy of Nociception: Effective Connectivity in Chronic Pain and Placebo Response
Nara, S., Baliki, M. N., Friston, K. J., & Ray, D. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1447-24.2025 · Cited by 9 · see citing works
Self-citationGamma Oscillations and Neural Field DCMs Can Reveal Cortical Excitability and Microstructure
Pinotsis, D. A., & Friston, K. J. (2014) · AIMS neuroscience · doi:10.3934/neuroscience.2014.1.18 · Cited by 9 · see citing works
“In our second study [10], neuronal signals were recorded while a monkey first fixated its gaze (in a window with 1 radius) and later released a lever when it detected a color change at fixation.”
BackgroundSelf-citationA theta rhythm in macaque visual cortex and its attentional modulationPreprint
Spyropoulos, G., Bosman, C. A., & Fries, P. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/117804 · Cited by 11 · see citing works
“The data analyzed here have been (partially) used in previous studies (15, 18, 19, 22, 33, 54-59).”
MethodSelf-citationExtracting novel information from neuroimaging data using neural fields
Pinotsis, D. A., & Friston, K. J. (2014) · EPJ Nonlinear Biomedical Physics · doi:10.1140/epjnbp18 · Cited by 7 · see citing works
“In our first study [5], we exploited high density spatially resolved data from multielectrode electrocorticographic (ECoG) arrays to study the effect of varying stimulus contrast on cortical excitability and gamma peak frequency.”
MethodSelf-citationDCM, Conductance Based Models and Clinical Applications
Marreiros, A. C., Pinotsis, D. A., Brown, P., & Friston, K. J. (2015) · Springer series in computational neuroscience · doi:10.1007/978-3-319-20037-8_3 · Cited by 7 · see citing works
Self-citationStatistical decision theory and multiscale analyses of human brain data
Pinotsis, D. A. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108912 · Cited by 7 · see citing works
Self-citationDegeneracy in the neurological model of auditory speech repetition
Sajid, N., Gajardo‐Vidal, A., Ekert, J. O., Lorca‐Puls, D. L., PLORAS team, Hope, T. M. H., Green, D. W., Friston, K. J., & Price, C. J. (2023) · Communications Biology · doi:10.1038/s42003-023-05515-5 · Cited by 7 · see citing works
“…prediction errors being passed from a lower to a higher area, whereas inhibitory effective connectivity re fl ects the explaining away (reduced excitation) of the prediction error lower in the hierarchy, generally thought to be mediated by inhibitory inter-neurons within the target region 33 – 36 .”
BackgroundSelf-citationRuffini, G., Lopez-Sola, E., Palma, R., Sanchez-Todo, R., Vohryzek, J., Castaldo, F., & Friston, K. J. (2026) · Neural Computation · doi:10.1162/neco.a.1537 · Cited by 8 · see citing works
Self-citationBrain rhythms shift and deploy attentionPreprint
Richter, C. G., Bosman, C. A., Vezoli, J., Schoffelen, J., & Fries, P. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/795567 · Cited by 6 · see citing works
Self-citationModel-based prediction of muscarinic receptor function from auditory mismatch negativity responsesPreprint
Schöbi, D., Jung, F., Frässle, S., Endepols, H., Moran, R., Friston, K. J., Tittgemeyer, M., Heinzle, J., & Stephan, K. E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.08.139550 · Cited by 5 · see citing works
Self-citationde Steen, F. V., Pinotsis, D. A., Devos, W., Colenbier, N., Bassez, I., Friston, K. J., & Marinazzo, D. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009988 · Cited by 3 · see citing works
Self-citationWegner, K., Wilson, C. R. E., Procyk, E., Friston, K. J., de Steen, F. V., Pinotsis, D. A., & Marinazzo, D. (2023) · Neurons Behavior Data analysis and Theory · doi:10.51628/001c.68433 · Cited by 1 · see citing works
“For the estimation of effective connections between the dlPFC and pre-SMA underlying the changes reported above, we applied DCM for cross-spectral density based on the canonical microcircuit model (CMC; [56, 57].”
BackgroundMethodSelf-citationAdaptive observer and control of spatiotemporal delayed neural fields
Brivadis, L., Chaillet, A., & Auriol, J. (2024) · Systems & Control Letters · doi:10.1016/j.sysconle.2024.105777 · Cited by 1 · see citing works
“This interesting compromise between biological significance and abstraction explains the wide range of neural fields applications, which cover primary visual cortex [3, 37], auditory system [6], working memory [31], sensory cortex [21], and deep brain structures involved in Parkinson’s disease [14].”
BackgroundUbiquitous predictive processing in the spectral domain of sensory cortexPreprint
Sennesh, E., Westerberg, J. A., Spencer-Smith, J. B., & Bastos, A. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.31.667946 · Cited by 2 · see citing works
“Population-level modeling studies have proposed that ɣ synchronization relates to the gain of superficial pyramidal neurons ( 20 , 69 , 70 ) ; through the lens of predictive processing, ɣ oscillations may regulate feedforward prediction-error signaling ( 12 , 20 , 21 ) .”
Self-citationUbiquitous predictive processing in the spectral domain of sensory cortexPreprint
Sennesh, E., Westerberg, J. A., Spencer-Smith, J. B., & Bastos, A. M. (2025) · eLife · doi:10.7554/elife.109053.1 · Cited by 1 · see citing works
Self-citationBiomarkers of brain function in psychosis and their genetic basis
Ranlund, S. M. (2016) · UCL Discovery (University College London)
Causal Modeling: Methods and Their Application to Speech and Language
Baojuan Li, A., Ahlfors, S. P., Pinotsis, D. A., Friston, K. J., & Mody, M. (2017) · Innovations in cognitive neuroscience · doi:10.1007/978-1-4939-7325-5_8
Self-citationKumar, G., Dutta, S., Talwar, S., Roy, D., & Banerjee, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/819896
Extracting novel information from neuroimaging data using neural fields
Pinotsis, D. A. (2014) · BMC Neuroscience · doi:10.1186/1471-2202-15-s1-o4
Self-citationAdaptive Observer and Control of Spatiotemporal Delayed Neural FieldsPreprint
Brivadis, L., Chaillet, A., & Auriol, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4463295
Ubiquitous predictive processing in the spectral domain of sensory cortexPreprint
Sennesh, E., Westerberg, J. A., Spencer-Smith, J. B., & Bastos, A. M. (2025) · eLife · doi:10.7554/elife.109053
Self-citationThe goo that binds us: how field resonance solves neuroscience’s binding and criticality problems
Hunt, T. (2026) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2026.1738326
The phantastic organ: predictive processing in the brain
Friston, K. J., & Parr, T. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1901293
“In this and subsequent figures, precision weighted prediction errors are denoted by ξ = ε = ε − ξ , were is a precision matrix and can be regarded as the strength of horizontal connections (Pinotsis et al., 2014; Tang et al., 2023).”
Self-citationBrain Dynamics Underlying Reaching Movements Under Uncertainty
Liu, N., Zabicki, A., Fink, G. R., Mengotti, P., & Vossel, S. (2026) · Human Brain Mapping · doi:10.1002/hbm.70643
“Physiologically, the corresponding precision or postsynaptic gain control is generally thought to be mediated by neuromodulatory mechanisms of various sorts (Limanowski 2022; Pinotsis et al. 2014; Vossel et al. 2015).”
Anterior cingulate feedback facilitates rapid global context modulation in primary visual cortex
Peterka, D. S., Imai, F., Ross, J. M., Bastos, G., Hornick, M. A., Rachmany, L., Gallimore, C. G., Hockley, A., & Hamm, J. P. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.118151 · Cited by 1 · see citing works
- Authors
- Pinotsis, D. A., Brunet, N., Bastos, A., Bosman, C. A., Litvak, V., Fries, P., & Friston, K. J.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Radboud University Nijmegen (NL)
- Max Planck Society (DE)
- Corresponding author
- Pinotsis, D.A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 92
- Pages
- 143-155
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4010674
- OpenAlex
- W2081402846
- Cited by
- 68
- References
- 43
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 1 · 2023: 6 · 2022: 3 · 2021: 3 · 2020: 8 · 2019: 5 · 2018: 4 · 2017: 11 · 2016: 5 · 2015: 8 · 2014: 5
- FWCI
- 5.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Wellcome Trust
- European Commission (HEALTH-F2-2008-200728)
- National Science Foundation
- European Science Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- contrast gain control, primary visual cortex, horizontal connectivity, dynamic causal modeling, neural field models, excitation-inhibition balance, spiny stellate cells, pyramidal neurons, receptive field size, spatial frequency filtering, predictive coding, electrocorticography, macaque visual cortex
- MeSH terms
- Animals, Attention, Computer Simulation, Electroencephalography, Macaca mulatta, Male, Models, Neurological, Nerve Net, Neural Inhibition, Visual Cortex, Visual Fields, Contrast Sensitivity, Pyramidal Cells, Connectome
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review