The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics

Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works

Advances in neural information processing systems 7

Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works

Emergence of simple-cell receptive field properties by learning a sparse code for natural images

Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694 · see citing works

Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects

Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · see citing works

Rhythms of the Brain

Buzsáki, G. (2006) · doi:10.1093/acprof:oso/9780195301069.001.0001 · Cited by 4,873 · see citing works

Humans integrate visual and haptic information in a statistically optimal fashion

Ernst, M. O., & Banks, M. S. (2002) · Nature · doi:10.1038/415429a · Cited by 4,844 · see citing works

Hybrid Monte Carlo

Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987) · Physics Letters B · doi:10.1016/0370-2693(87)91197-x · Cited by 3,920 · see citing works

Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons

Wilson, H. R., & Cowan, J. D. (1972) · Biophysical Journal · doi:10.1016/s0006-3495(72)86068-5 · Cited by 3,579 · see citing works

An internal model for sensorimotor integration

Wolpert, D. M., Ghahramani, Z., & Jordan, M. I. (1995) · Science · doi:10.1126/science.7569931 · Cited by 3,553 · see citing works

Handbook of Markov Chain Monte Carlo

Brooks, S., Gelman, A., Jones, G. L., & Meng, X. (2011) · Chapman & Hall/CRC eBooks · doi:10.1201/b10905 · Cited by 3,019 · see citing works

Neuronal Synchrony: A Versatile Code for the Definition of Relations?

Singer, W. (1999) · Neuron · doi:10.1016/s0896-6273(00)80821-1 · Cited by 2,581 · see citing works

MCMC Using Hamiltonian Dynamics

Neal, R. M. (2011) · Handbook of Markov Chain Monte Carlo · doi:10.1201/b10905-6 · Cited by 2,574 · see citing works

Bayesian integration in sensorimotor learning

Körding, K. P., & Wolpert, D. M. (2004) · Nature · doi:10.1038/nature02169 · Cited by 2,004 · see citing works

Neuronal gamma-band synchronization as a fundamental process in cortical computation

Fries, P. (2009) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135603 · Cited by 1,713 · see citing works

Highly nonrandom features of synaptic connectivity in local cortical circuits

Song, S., Sjöström, P. J., Reigl, M., Nelson, S., & Chklovskii, D. B. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030068 · Cited by 1,512 · see citing works

Bayesian inference with probabilistic population codes

Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006) · Nature Neuroscience · doi:10.1038/nn1790 · Cited by 1,457 · see citing works

Modulation of Neuronal Interactions Through Neuronal Synchronization

Womelsdorf, T., Schoffelen, J.-M., Oostenveld, R., Singer, W., Desimone, R., Engel, A. K., & Fries, P. (2007) · Science · doi:10.1126/science.1139597 · Cited by 1,367 · see citing works

A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.

Alison Gopnik, C., Glymour, C., Sobel, D. M., Schulz, L., Kushnir, T., & Danks, D. J. (2004) · Psychological Review · doi:10.1037/0033-295x.111.1.3 · Cited by 1,227 · see citing works

Spatial Point Processes

Huber, M. (2011) · Chapman & Hall/CRC eBooks · doi:10.1201/b10905-10 · Cited by 1,177 · see citing works

Stimulus onset quenches neural variability: A widespread cortical phenomenon

Churchland, M. M., Yu, B. M., Cunningham, J. P., Sugrue, L. P., Cohen, M. R., Corrado, G. S., Newsome, W. T., Clark, A. M., Hosseini, P., Scott, B. B., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2501 · Cited by 1,084 · see citing works

Theoretical Neuroscience

Chklovskii, D. B. (2002) · Neuron · doi:10.1016/s0896-6273(02)00703-1 · Cited by 1,074 · see citing works

Exponential Convergence of Langevin Distributions and Their Discrete Approximations

Roberts, G. O., & Tweedie, R. L. (1996) · Bernoulli · doi:10.2307/3318418 · Cited by 1,000 · see citing works

Theory-based Bayesian models of inductive learning and reasoning

Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.009 · Cited by 880 · see citing works

Natural signal statistics and sensory gain control

Schwartz, O., & Simoncelli, E. P. (2001) · Nature Neuroscience · doi:10.1038/90526 · Cited by 835 · see citing works

Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment

Berkes, P., Orbán, G., Lengyel, M., & Fiser, J. (2011) · Science · doi:10.1126/science.1195870 · Cited by 782 · see citing works

Integration of Proprioceptive and Visual Position-Information: An Experimentally Supported Model

van Beers, R. J., Sittig, A. C., & Gon, J. J. D. V. D. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.81.3.1355 · Cited by 742 · see citing works

Specificity of monosynaptic connections from thalamus to visual cortex

Reid, R. C., & Alonso, J. (1995) · Nature · doi:10.1038/378281a0 · Cited by 732 · see citing works

Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities

Okun, M., & Lampl, I. (2008) · Nature Neuroscience · doi:10.1038/nn.2105 · Cited by 701 · see citing works

Statistically optimal perception and learning: from behavior to neural representations

Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.003 · Cited by 672 · see citing works

Probabilistic population codes for Bayesian decision making

Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E., & Pouget, A. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.021 · Cited by 645 · see citing works

Probabilistic brains: knowns and unknowns

Pouget, A., Beck, J. M., Ma, W. J., & Latham, P. E. (2013) · Nature Neuroscience · doi:10.1038/nn.3495 · Cited by 615 · cited 1× · see citing works

Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease.

Buzsáki, G., & Watson, B. O. (2012) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2012.14.4/gbuzsaki · Cited by 579 · see citing works

Probabilistic models of cognition: Conceptual foundations

Chater, N., Tenenbaum, J. B., & Yuille, A. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.007 · Cited by 546 · see citing works

Rapid Adaptation in Visual Cortex to the Structure of Images

Müller, J. R., Metha, A. B., Krauskopf, J., & Lennie, P. (1999) · Science · doi:10.1126/science.285.5432.1405 · Cited by 458 · see citing works

Neural Activity in Barrel Cortex Underlying Vibrissa-Based Object Localization in Mice

O’Connor, D. H., Peron, S., Huber, D., & Svoboda, K. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.026 · Cited by 453 · see citing works

The probability of neurotransmitter release: variability and feedback control at single synapses

Branco, T., & Staras, K. (2009) · Nature reviews. Neuroscience · doi:10.1038/nrn2634 · Cited by 445 · see citing works

Scale Mixtures of Gaussians and the Statistics of Natural Images

Wainwright, M. J., & Simoncelli, E. P. (1999) · Cited by 439 · 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 424 · see citing works

Reading population codes: a neural implementation of ideal observers

Denève, S., Latham, P. E., & Pouget, A. (1999) · Nature Neuroscience · doi:10.1038/11205 · Cited by 392 · see citing works

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons

Buesing, L., Bill, J., Nessler, B., & Maass, W. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002211 · Cited by 384 · see citing works

Sustained firing in auditory cortex evoked by preferred stimuli

Wang, X., Lu, T., Snider, R. K., & Li, L. (2005) · Nature · doi:10.1038/nature03565 · Cited by 370 · see citing works

Spike-Timing-Dependent Plasticity: A Comprehensive Overview

Markram, H., Gerstner, W., & Sjöström, P. J. (2012) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2012.00002 · Cited by 367 · see citing works

Paradoxical Effects of External Modulation of Inhibitory Interneurons

Tsodyks, M. V., Skaggs, W. E., B, T. J. S. A., & McNaughton, B. L. (1997) · Journal of Neuroscience · doi:10.1523/jneurosci.17-11-04382.1997 · Cited by 364 · see citing works

Spontaneous Subthreshold Membrane Potential Fluctuations and Action Potential Variability of Rat Corticostriatal and Striatal Neurons In Vivo

Stern, E. A., Kincaid, A. E., & Wilson, C. J. (1997) · Journal of Neurophysiology · doi:10.1152/jn.1997.77.4.1697 · Cited by 362 · see citing works

Sampling from multimodal distributions using tempered transitions

Neal, R. M. (1996) · Statistics and Computing · doi:10.1007/bf00143556 · Cited by 355 · see citing works

Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements

Hennequin, G., Vogels, T. P., & Gerstner, W. (2014) · Neuron · doi:10.1016/j.neuron.2014.04.045 · Cited by 348 · see citing works

Balanced Amplification: A New Mechanism of Selective Amplification of Neural Activity Patterns

Murphy, B. K., & Miller, K. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.02.005 · Cited by 338 · see citing works

Probabilistic Interpretation of Population Codes

Zemel, R. S., Dayan, P., & Pouget, A. (1998) · Neural Computation · doi:10.1162/089976698300017818 · Cited by 333 · see citing works

The Stabilized Supralinear Network: A Unifying Circuit Motif Underlying Multi-Input Integration in Sensory Cortex

Rubin, D. B., Hooser, S. D. V., & Miller, K. D. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.026 · Cited by 330 · see citing works

Optimal integration of texture and motion cues to depth

Jacobs, R. A. (1999) · Vision Research · doi:10.1016/s0042-6989(99)00088-7 · Cited by 324 · see citing works

Preconfigured, Skewed Distribution of Firing Rates in the Hippocampus and Entorhinal Cortex

Mizuseki, K., & Buzsáki, G. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.07.039 · Cited by 319 · see citing works

Probabilistic Interpretation of Population Codes

Zemel, R. S., Dayan, P., & Pouget, A. (1999) · The MIT Press eBooks · doi:10.7551/mitpress/4924.003.0011 · Cited by 308 · see citing works

A review of brain oscillations in cognitive disorders and the role of neurotransmitters

Başar, E., & Güntekin, B. (2008) · Brain Research · doi:10.1016/j.brainres.2008.06.103 · Cited by 269 · see citing works

Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex

Orbán, G., Berkes, P., Fiser, J., & Lengyel, M. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.038 · Cited by 258 · see citing works

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 · see citing works

Plasticity of Inhibition

Kullmann, D. M., Moreau, A. W., Bakiri, Y., & Nicholson, E. (2012) · Neuron · doi:10.1016/j.neuron.2012.07.030 · Cited by 234 · see citing works

Emergence of complex cell properties by learning to generalize in natural scenes

Karklin, Y., & Lewicki, M. S. (2008) · Nature · doi:10.1038/nature07481 · Cited by 216 · see citing works

Theoretical Neuroscience

Dayan, P., & Abbott, L. (2001) · UCL Discovery (University College London) · Cited by 216 · see citing works

A formula for human retinal ganglion cell receptive field density as a function of visual field location.

Watson, A. B. (2014) · Journal of Vision · doi:10.1167/14.7.15 · Cited by 197 · see citing works

Rapid enhancement of visual cortical response discriminability by microstimulation of the frontal eye field

Armstrong, K. M., & Moore, T. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701104104 · Cited by 184 · see citing works

Empirical models of spiking in neural populations

Macke, J. H., Buesing, L., Cunningham, J. P., Yu, B. M., Shenoy, K. V., & Sahani, M. (2011) · Max Planck Digital Library · Cited by 172 · see citing works

Gating of Sensory Input by Spontaneous Cortical Activity

Luczak, A., Barthó, P., & Harris, K. D. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2928-12.2013 · Cited by 157 · see citing works

Surface orientation from texture: ideal observers, generic observers and the information content of texture cues

Knill, D. C. (1998) · Vision Research · doi:10.1016/s0042-6989(97)00324-6 · Cited by 147 · see citing works

Highly nonrandom features of synaptic connectivity in local cortical circuits (vol 3, pg e68, 2005)

Song, S.-F., Sjöström, P. J., Reigl, M., Nelson, S. B., & Chklovskii, D. B. (2005) · UCL Discovery (University College London) · Cited by 147 · see citing works

Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior

Hoyer, P. O., & Hyvärinen, A. (2002) · Cited by 144 · see citing works

1/f noise: a pedagogical reviewPreprint

Milotti, E. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.physics/0204033 · Cited by 142 · see citing works

Information Conveyed by Onset Transients in Responses of Striate Cortical Neurons

Müller, J. R., Metha, A. B., Krauskopf, J., & Lennie, P. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-17-06978.2001 · Cited by 133 · see citing works

Marginalization in Neural Circuits with Divisive Normalization

Beck, J. M., Latham, P. E., & Pouget, A. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.1706-11.2011 · Cited by 131 · see citing works

Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

Coen-Cagli, R., Dayan, P., & Schwartz, O. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002405 · Cited by 107 · see citing works

NeuroElectro: a window to the world's neuron electrophysiology data

Tripathy, S. J., Savitskaya, J., Burton, S. D., Urban, N. N., & Gerkin, R. C. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00040 · Cited by 105 · see citing works

Perceptual organization in the tilt illusion

Schwartz, O., B, T. J. S. A., & Dayan, P. (2009) · Journal of Vision · doi:10.1167/9.4.19 · Cited by 102 · see citing works

Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity

Sahani, M., & Dayan, P. (2003) · Neural Computation · doi:10.1162/089976603322362356 · Cited by 90 · see citing works

Formation and reverberation of sequential neural activity patterns evoked by sensory stimulation are enhanced during cortical desynchronization.

Contreras, E. B., Schjetnan, A. G.-P., Muhammad, A., Barthó, P., McNaughton, B., Kolb, B., Gruber, A., & Luczak, A. (2013) · Neuron · doi:10.1016/j.neuron.2013.06.013 · Cited by 88 · see citing works

Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials

Pfister, J.-P., Dayan, P., & Lengyel, M. (2010) · Nature Neuroscience · doi:10.1038/nn.2640 · Cited by 74 · see citing works

Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types

Tripathy, S. J., Burton, S. D., Geramita, M. A., Gerkin, R. C., & Urban, N. N. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00237.2015 · Cited by 72 · see citing works

Computational differences between asymmetrical and symmetrical networks

Li Zhaoping, A., & Dayan, P. (1999) · Network Computation in Neural Systems · doi:10.1088/0954-898x_10_1_004 · Cited by 55 · see citing works

Binomial parameters differ across neocortical layers and with different classes of connections in adult rat and cat neocortex

Brémaud, A., West, D. C., & Thomson, A. M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0705661104 · Cited by 54 · see citing works

Processing of sounds by population spikes in a model of primary auditory cortex

Loebel, A. (2007) · Frontiers in Neuroscience · doi:10.3389/neuro.01.1.1.015.2007 · Cited by 54 · see citing works

A Structured Model of Video Reproduces Primary Visual Cortical Organisation

Berkes, P., Turner, R. E., & Sahani, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000495 · Cited by 41 · see citing works

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits

Ujfalussy, B. B., Makara, J. K., Branco, T., & Lengyel, M. (2015) · eLife · doi:10.7554/elife.10056 · Cited by 39 · see citing works

Statistical Models of Natural Images and Cortical Visual Representation

Hyvärinen, A. (2009) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2009.01057.x · Cited by 36 · see citing works

Fast Sampling-Based Inference in Balanced Neuronal Networks

Hennequin, G., Aitchison, L., & Lengyel, M. (2014) · Bristol Research (University of Bristol) · Cited by 34 · see citing works

Inferring neural population dynamics from multiple partial recordings of the same neural circuit

Turaga, S. C., Buesing, L., Packer, A. M., Dalgleish, H. W. P., Pettit, N., Häusser, M., & Macke, J. H. (2013) · Max Planck Digital Library · Cited by 32 · see citing works

Optimal Recall from Bounded Metaplastic Synapses: Predicting Functional Adaptations in Hippocampal Area CA3

Savin, C., Dayan, P., & Lengyel, M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003489 · Cited by 29 · see citing works

Contour inflections are adaptable features

Bell, J., Sampasivam, S., McGovern, D. P., Meso, A. I., & Kingdom, F. A. A. (2014) · Journal of Vision · doi:10.1167/14.7.2 · Cited by 24 · see citing works

Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing

Coen-Cagli, R., Dayan, P., & Schwartz, O. (2009) · MPG.PuRe (Max Planck Society) · Cited by 15 · see citing works

Assignment of Multiplicative Mixtures in Natural Images

Schwartz, O., Sejnowski, T. J., & Dayan, P. (2004) · Cited by 5 · see citing works

Temporal and Spatial Properties of the Retinal Ganglion Cells' Response to Natural Stimuli Described by Treves-Rolls Sparsity

Zhang, Y., Li, Y., Gong, H.-Q., & Liang, P.-J. (2009) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2009.5163564 · Cited by 2 · see citing works

Cognitive computational neuroscience

Kriegeskorte, N., & Douglas, P. K. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0210-5 · Cited by 535 · see citing works

With or without you: predictive coding and Bayesian inference in the brain

Aitchison, L., & Lengyel, M. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.08.010 · Cited by 308 · see citing works

Internal models in biological control

McNamee, D., & Wolpert, D. M. (2019) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105206 · Cited by 225 · see citing works

Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference

Echeveste, R., Aitchison, L., Hennequin, G., & Lengyel, M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0671-1 · Cited by 126 · see citing works

Where is the error? Hierarchical predictive coding through dendritic error computation

Mikulasch, F. A., Rudelt, L., Wibral, M., & Priesemann, V. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.09.007 · Cited by 128 · see citing works

The Bayesian sampler: Generic Bayesian inference causes incoherence in human probability judgments.

Zhu, J.-Q., Sanborn, A. N., & Chater, N. (2020) · Psychological Review · doi:10.1037/rev0000190 · Cited by 95 · see citing works

Synaptic plasticity as Bayesian inference

Aitchison, L., Jegminat, J., Menendez, J. A., Pfister, J.-P., Pouget, A., & Latham, P. E. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00809-5 · Cited by 85 · see citing works

Representation of visual uncertainty through neural gain variability

Hénaff, O. J., Boundy-Singer, Z. M., Meding, K., Ziemba, C. M., & Goris, R. L. T. (2020) · Nature Communications · doi:10.1038/s41467-020-15533-0 · Cited by 54 · see citing works

Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics

Kao, T.-C., & Hennequin, G. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.09.001 · Cited by 47 · see citing works

Learning probabilistic neural representations with randomly connected circuits

Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912804117 · Cited by 44 · see citing works

Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks

Kungl, Á. F., Schmitt, S., Klähn, J., Müller, P. F., Baumbach, A., Dold, D., Kugele, A., Müller, E., Koke, C., Kleider, M., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01201 · Cited by 39 · see citing works

Depersonalization disorder as a systematic downregulation of interoceptive signals

Saini, F., Ponzo, S., Silvestrin, F., Fotopoulou, A., & David, A. S. (2022) · Scientific Reports · doi:10.1038/s41598-022-22277-y · Cited by 36 · see citing works

Task-induced neural covariability as a signature of approximate Bayesian learning and inference

Lange, R. D., & Haefner, R. M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009557 · Cited by 45 · see citing works

Bayesian encoding and decoding as distinct perspectives on neural coding

Lange, R. D., Shivkumar, S., Chattoraj, A., & Haefner, R. M. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01458-6 · Cited by 38 · see citing works

Chaotic neural dynamics facilitate probabilistic computations through sampling

Terada, Y., & Toyoizumi, T. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2312992121 · Cited by 34 · see citing works

Bayesian modeling of the mind: From norms to neurons

Rescorla, M. (2020) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1540 · Cited by 30 · see citing works

Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons

Zhang, W., Wu, S., Josić́, K., & Doiron, B. (2023) · Nature Communications · doi:10.1038/s41467-023-41743-3 · Cited by 29 · see citing works

Stochasticity from function — Why the Bayesian brain may need no noise

Dold, D., Bytschok, I., Kungl, Á. F., Baumbach, A., Breitwieser, O., Senn, W., Schemmel, J., Meier, K., & Petrovici, M. A. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.08.002 · Cited by 29 · see citing works

Modeling subjective belief states in computational psychiatry: interoceptive inference as a candidate framework

Gu, X., FitzGerald, T. H. B., & Friston, K. J. (2019) · Psychopharmacology · doi:10.1007/s00213-019-05300-5 · Cited by 27 · see citing works

Fractional Neural Sampling: A Theory of Spatiotemporal Probabilistic Computations in Neural Circuits

Qi, Y., & Gong, P. (2021) · Nature Communications · doi:10.1038/s41467-022-32279-z · Cited by 19 · see citing works

Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inferencePreprint

Echeveste, R., Aitchison, L., Hennequin, G., & Lengyel, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/696088 · Cited by 17 · see citing works

Cortical oscillations support sampling-based computations in spiking neural networks

Korcsak-Gorzo, A., Müller, M. G., Baumbach, A., Leng, L., Breitwieser, O., van Albada, S. J., Senn, W., Meier, K., Legenstein, R. A., & Petrovici, M. A. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009753 · Cited by 16 · see citing works

Towards a cross-level understanding of Bayesian inference in the brain

Lin, C., & Garrido, M. I. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104649 · Cited by 15 · see citing works

Periodic population codes: From a single circular variable to higher dimensions, multiple nested scales, and conceptual spaces

Herz, A. V. M., Mathis, A., & Stemmler, M. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.07.005 · Cited by 13 · see citing works

Recognition Dynamics in the Brain under the Free Energy Principle

Kim, C. S. (2018) · Neural Computation · doi:10.1162/neco_a_01115 · Cited by 15 · see citing works

Human inference in changing environments with temporal structure.

Prat-Carrabin, A., Wilson, R. C., Cohen, J. D., & Azeredo da Silveira, R. (2021) · Psychological Review · doi:10.1037/rev0000276 · Cited by 16 · see citing works

Pupil dilation indexes automatic and dynamic inference about the precision of stimulus distributions

Silvestrin, F., Penny, W. D., & FitzGerald, T. H. B. (2021) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2021.102503 · Cited by 12 · see citing works

Building machines that adapt and compute like brains

Kriegeskorte, N., & Mok, R. M. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x17000188 · Cited by 11 · see citing works

Noise correlations and perceptual inference

Bányai, M., & Orbán, G. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.09.002 · Cited by 10 · see citing works

Natural Gradient Enables Fast Sampling in Spiking Neural Networks

Masset, P., Zavatone-Veth, J. A., Connor, J. P., Murthy, V. N., & Pehlevan, C. (2022) · neural information processing systems · doi:10.52202/068431-1600 · Cited by 8 · see citing works

Task-induced neural covariability as a signature of approximate Bayesian learning and inferencePreprint

Lange, R. D., & Haefner, R. M. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/081661 · Cited by 9 · see citing works

Retrospective Inference as a Form of Bounded Rationality, and Its Beneficial Influence on Learning

FitzGerald, T. H. B., Penny, W. D., Bonnici, H. M., & Adams, R. A. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00002 · Cited by 7 · see citing works

The Sampling Brain

Sanborn, A. N., & Chater, N. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.04.009 · Cited by 8 · see citing works

Distributed Sampling-based Bayesian Inference in Coupled Neural CircuitsPreprint

Zhang, W., Lee, T. S., Doiron, B., & Wu, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.20.212126 · Cited by 7 · see citing works

Relating natural image statistics to patterns of response covariability in macaque primary visual cortex

Farzmahdi, A., Kohn, A., & Coen-Cagli, R. (2025) · Nature Communications · doi:10.1038/s41467-025-62086-1 · Cited by 6 · see citing works

Joint modeling of choices and reaction times based on Bayesian contextual behavioral control

Schwöbel, S., Marković, D., Smolka, M. N., & Kiebel, S. J. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012228 · Cited by 5 · see citing works

Learning probability distributions of sensory inputs with Monte Carlo predictive coding

Oliviers, G., Bogacz, R., & Meulemans, A. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012532 · Cited by 6 · see citing works

Human Inference in Changing Environments with Temporal StructurePreprint

Prat-Carrabin, A., Wilson, R. C., Cohen, J. D., & da Silveira, R. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/720516 · Cited by 6 · see citing works

Bifocal tACS Enhances Visual Motion Discrimination by Modulating Phase Amplitude Coupling Between V1 and V5 RegionsPreprint

Salamanca-Giron, R., Raffin, E., Zandvliet, S. B., Seeber, M., Michel, C. M., Sauseng, P., Huxlin, K. R., & Hummel, F. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.16.382267 · Cited by 6 · see citing works

Signatures of Bayesian inference emerge from energy-efficient synapses

Malkin, J., O’Donnell, C., Houghton, C. J., & Aitchison, L. (2024) · eLife · doi:10.7554/elife.92595.3 · Cited by 4 · see citing works

A dual-receptor model of serotonergic psychedelicsPreprint

Juliani, A., Chelu, V., Graesser, L., & Safron, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.12.589282 · Cited by 4 · see citing works

Conscious active inference I: A quantum model naturally implements the path integral needed for real-time planning and control

Wiest, M. C., & Puniani, A. S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.09.017 · Cited by 5 · see citing works

Signatures of Bayesian inference emerge from energy-efficient synapses

Malkin, J., O’Donnell, C., Houghton, C. J., & Aitchison, L. (2023) · eLife · doi:10.7554/elife.92595 · Cited by 7 · see citing works

Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference

Echeveste, R., Ferrante, E., Milone, D. H., & Samengo, I. (2021) · Network Neuroscience · doi:10.1162/netn_a_00219 · Cited by 3 · see citing works

Dynamical Mechanism of Sampling-Based Probabilistic Inference Under Probabilistic Population Codes

Ichikawa, K., & Kataoka, A. (2022) · Neural Computation · doi:10.1162/neco_a_01477 · Cited by 2 · see citing works

Decentralized Neural Circuits of Multisensory Information Integration in the Brain

Zhang, W. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-981-99-7611-9_1 · Cited by 2 · see citing works

Predictive coding in the auditory brainstemPreprint

Alain De Cheveigné, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.31.573202 · Cited by 2 · see citing works

Stimulus content shapes cortical response statisticsPreprint

Bányai, M., Lazăr, A., Klein, L., Klon-Lipok, J., Singer, W., & Orbán, G. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/238774 · Cited by 1 · see citing works

Cognitive computational neurosciencePreprint

Kriegeskorte, N., & Douglas, P. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.11819 · Cited by 1 · see citing works

The "Bayesian" brain, with a bit less BayesPreprint

Spaak, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.09063 · Cited by 1 · see citing works

Signatures of Bayesian inference emerge from energy efficient synapsesPreprint

Malkin, J., O’Donnell, C., Houghton, C. J., & Aitchison, L. (2023) · eLife · doi:10.7554/elife.92595.1 · Cited by 1 · see citing works

Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction SystemsPreprint

Pjanovic, V., Zavatone-Veth, J. A., Masset, P., Keemink, S. W., & Nardin, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.25.640158 · Cited by 1 · see citing works

Standard Laterality Models

Young, G. C. (2019) · Springer eBooks · doi:10.1007/978-3-030-02493-2_8

Oscillatory background activity implements a backbone for sampling-based computations in spiking neural networksPreprint

Legenstein, R. A., & Müller, M. G. (2020) · arXiv (Cornell University)

Biological Organization Principles: Biogenesis, Cognition and Evolution of Living SystemsPreprint

Maureira, C., & García, J. (2020) · Preprints.org · doi:10.20944/preprints202008.0647.v1

Dynamical Mechanism of Sampling-based Stochastic Inference under Probabilistic Population CodesPreprint

Ichikawa, K., & Kataoka, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05591

Fractional Neural Sampling: A Theory of Spatiotemporal Probabilistic Computations in Neural CircuitsPreprint

Qi, Y., & Gong, P. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3863569

Complex priors and flexible inference in recurrent circuits with dendritic nonlinearitiesPreprint

Lyo, B. S. H., & Savin, C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.03.560701

A theory of inference and learning in cortex with spiking neurons and dendritic error computation

Mikulasch, F. A. (2023) · doi:10.53846/goediss-10168

Signatures of Bayesian inference emerge from energy efficient synapsesPreprint

Malkin, J., O’Donnell, C., Houghton, C. J., & Aitchison, L. (2024) · eLife · doi:10.7554/elife.92595.2

samplr: Compare Human Performance to Sampling Algorithms

Castillo, L., Li, Y.-X., & Sanborn, A. N. (2024) · doi:10.32614/cran.package.samplr

Advancing Conversational AI: Investigating AI-to-AI Voice Dialogue Through Hybrid Active Inference (HAI) and Adaptive ReasoningPreprint

Sudbury, Q. S. (2025) · Research Square · doi:10.21203/rs.3.rs-6306194/v1

Natural gradient Bayesian sampling automatically emerges in canonical cortical circuitsPreprint

Chen, Z., Ren, Y., Sale, E., Wu, Y., & Zhang, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.03.663069

Canonical cortical circuits: A unified sampling machine for static and dynamic inferencePreprint

Sale, E., & Zhang, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.06.663395

Computational learning noise in human decision-making

Findling, C. (2018) · theses.fr (ABES) · doi:10.70675/32e62279zea97z45b1z9326z06a449dae472

Perceptual, metacognitive, and computational signatures of temporal landmark uncertainty in tactile motion perceptionPreprint

Adibi, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.29.735212

Combining sampling and attractor dynamics in spiking models of head direction systems

Pjanovic, V., Zavatone-Veth, J. A., Masset, P., Keemink, S. W., & Nardin, M. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014577 · Cited by 1 · see citing works

The Hamiltonian BrainPreprint

Aitchison, L., & Lengyel, M. (2014) · arXiv (Cornell University) · Cited by 1 · see citing works

Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inferencePreprint

Echeveste, R., Aitchison, L., Hennequin, G., & Lengyel, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/696088 · Cited by 17 · see citing works

Towards a mathematical theory of cortical microcircuits. PLoS computational biology 5

Dileep George, A., & Hawkins, J. (2009)

A neural implementation of Bayesian inference based on predictive coding

Spratling, M. (2016) · Connection Science · doi:10.1080/09540091.2016.1243655 · Cited by 30 · see citing works

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

Pecevski, D., Buesing, L., & Maass, W. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002294 · Cited by 107 · see citing works