Bayesian Spiking Neurons I: Inference

Humans integrate visual and haptic information in a statistically optimal fashion

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An introduction to hidden Markov models

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Learning in Graphical Models

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Computational principles of movement neuroscience

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Bayesian integration in sensorimotor learning

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The analysis of visual motion: a comparison of neuronal and psychophysical performance

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Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey

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Perception as Bayesian Inference

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Noise, neural codes and cortical organization

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Vectorized Algorithms for Spiking Neural Network Simulation

Brette, R., & Goodman, D. F. M. (2011) · Neural Computation · doi:10.1162/neco_a_00123 · Cited by 41

LIF and Simplified SRM Neurons Encode Signals Into Spikes via a Form of Asynchronous Pulse Sigma–Delta Modulation

Yoon, Y. C. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2526029 · Cited by 40

Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration

Bobrowski, O., Meir, R., & Eldar, Y. C. (2008) · Neural Computation · doi:10.1162/neco.2008.01-08-692 · Cited by 40

Generalization of Stochastic Visuomotor Rotations

Fernandes, H. L., Stevenson, I. H., & Kording, K. P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0043016 · Cited by 35

Towards the design principles of neural population codes

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Applying the Theory of Constructed Emotion to Police Decision Making

Fridman, J., Barrett, L. F., Wormwood, J. B., & Quigley, K. S. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.01946 · Cited by 50

Towards a Neural Implementation of Causal Inference in Cue Combination

Ma, W. J., & Rahmati, M. (2013) · Multisensory Research · doi:10.1163/22134808-00002407 · Cited by 36

Reward-Modulated Hebbian Learning of Decision Making

Pfeiffer, M., Nessler, B., Douglas, R. J., & Maass, W. (2010) · Neural Computation · doi:10.1162/neco.2010.03-09-980 · Cited by 35

Bayesian Models of Brain and Behaviour

Penny, W. D. (2012) · ISRN Biomathematics · doi:10.5402/2012/785791 · Cited by 38

Novelty, Information and Surprise

Palm, G. (2012) · Springer eBooks · doi:10.1007/978-3-642-29075-6 · Cited by 34

Cognitive Science Progresses Toward Interactive Frameworks

Spivey, M. J. (2023) · Topics in Cognitive Science · doi:10.1111/tops.12645 · Cited by 41

The Generalization of Prior Uncertainty during Reaching

Fernandes, H. L., Stevenson, I. H., Vilares, I., & Kording, K. P. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.3882-13.2014 · Cited by 35

Signal processing in the vagus nerve: Hypotheses based on new genetic and anatomical evidence

Shaffer, C., Barrett, L. F., & Quigley, K. S. (2023) · Biological Psychology · doi:10.1016/j.biopsycho.2023.108626 · Cited by 34

Error-based analysis of optimal tuning functions explains phenomena observed in sensory neurons

Yaeli, S. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00130 · Cited by 33

Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation

Durá-Bernal, S., Wennekers, T., & Denham, S. L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048216 · Cited by 34

Learning to make external sensory stimulus predictions using internal correlations in populations of neurons

Sederberg, A., MacLean, J. N., & Palmer, S. E. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710779115 · Cited by 31

Getting Warmer: Predictive Processing and the Nature of Emotion

Wilkinson, S., Deane, G., Nave, K., & Clark, A. (2019) · Springer eBooks · doi:10.1007/978-3-030-15667-1_5 · Cited by 35

Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints

Habenschuss, S., Bill, J., & Nessler, B. (2012) · Cited by 29

Emergent Inference of Hidden Markov Models in Spiking Neural Networks Through Winner-Take-All

Yu, Z., Guo, S., Deng, F., Yan, Q., Huang, K., Liu, J. K., & Chen, F. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2871144 · Cited by 31

Maximum A-Posteriori Decoding for Diffusion-Based Molecular Communication Using Analog Filters

Chou, C. T. (2015) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2015.2469301 · Cited by 30

Evolving interpretable plasticity for spiking networks

Jordan, J., Schmidt, M., Senn, W., & Petrovici, M. A. (2021) · eLife · doi:10.7554/elife.66273 · Cited by 30

The Visual System's Internal Model of the World

Lee, T. S. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2434601 · Cited by 28

A neural implementation of Bayesian inference based on predictive coding

Spratling, M. (2016) · Connection Science · doi:10.1080/09540091.2016.1243655 · Cited by 32

Ecological expected utility and the mythical neural code

Feldman, J. A. (2009) · Cognitive Neurodynamics · doi:10.1007/s11571-009-9090-4 · Cited by 29

Robust Bayesian Inference for Gas Identification in Electronic Nose Applications by Using Random Matrix Theory

Hassan, M., & Bermak, A. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2507580 · Cited by 30

A predictive coding account of value-based learning in PTSD: Implications for precision treatments

Putica, A., Felmingham, K. L., Garrido, M. I., O'Donnell, M. L., & Dam, N. T. V. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104704 · Cited by 29

Bayesian Integration of Information in Hippocampal Place Cells

Madl, T., Franklin, S., Chen, K., Montaldi, D., & Trappl, R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089762 · Cited by 30

Training Spiking Neural Models Using Artificial Bee Colony

Vazquez, R. A., & Garro, B. A. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/947098 · Cited by 27

Neural representation of probabilities for Bayesian inference

Rich, D., Cazettes, F., Wang, Y., Peña, J. L., & Fischer, B. J. (2015) · Journal of Computational Neuroscience · doi:10.1007/s10827-014-0545-1 · Cited by 25

Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks

Huang, Y., & Rao, R. P. N. (2014) · neural information processing systems · Cited by 25

Evidence Accumulation and Change Rate Inference in Dynamic Environments

Radillo, A. E., Veliz‐Cuba, A., Josić́, K., & Kilpatrick, Z. P. (2017) · Neural Computation · doi:10.1162/neco_a_00957 · Cited by 27

What can neurons do for their brain? Communicate selectivity with bursts

Balduzzi, D., & Tononi, G. (2012) · Theory in Biosciences · doi:10.1007/s12064-012-0165-0 · Cited by 31

Information transmission with spiking Bayesian neurons

Lochmann, T., & Denève, S. (2008) · New Journal of Physics · doi:10.1088/1367-2630/10/5/055019 · Cited by 26

Action selection performance of a reconfigurable basal ganglia inspired model with Hebbian–Bayesian Go-NoGo connectivity

Berthet, P., Kotaleski, J. H., & Lansner, A. (2012) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2012.00065 · Cited by 26

Improving Spiking Neural Network With Frequency Adaptation for Image Classification

Chen, T., Wang, L., Li, J., Duan, S., & Huang, T. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3308347 · Cited by 25

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels

Afshar, S., George, L., Tapson, J. C., André, V. S., & Hamilton, T. J. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00377 · Cited by 26

Two Kinds of Information Processing in Cognition

Sprevak, M. D. (2019) · Review of Philosophy and Psychology · doi:10.1007/s13164-019-00438-9 · Cited by 26

What is optimal in optimal inference?

Tavoni, G., Balasubramanian, V., & Gold, J. I. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2019.07.008 · Cited by 24

Complexity and specificity of experimentally-induced expectations in motion perception

Gekas, N., Chalk, M., Seitz, A. R., & Seriès, P. (2013) · Journal of Vision · doi:10.1167/13.4.8 · Cited by 24

Computing with Spikes: The Advantage of Fine-Grained Timing

Verzi, S., Rothganger, F. H., Parekh, O., Quach, T.-T., Miner, N. E., Vineyard, C. M., James, C. D., & Aimone, J. B. (2018) · Neural Computation · doi:10.1162/neco_a_01113 · Cited by 24

Training a Hidden Markov Model with a Bayesian Spiking Neural Network

Tavanaei, A., & Maida, A. S. (2016) · Journal of Signal Processing Systems · doi:10.1007/s11265-016-1153-2 · Cited by 24

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Bitzer, S., & Kiebel, S. J. (2012) · Biological Cybernetics · doi:10.1007/s00422-012-0490-x · Cited by 24

Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains

Wang, Z., Sornborger, A. T., & Tao, L. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004979 · Cited by 22

Computational Modeling of Bilingual Language Learning: Current Models and Future Directions

Li, P., & Xu, Q. (2022) · Language Learning · doi:10.1111/lang.12529 · Cited by 25

A Biological Gradient Descent for Prediction Through a Combination of STDP and Homeostatic Plasticity

Galtier, M. N., & Wainrib, G. (2013) · Neural Computation · doi:10.1162/neco_a_00512 · Cited by 21

Irregularly timed electrical pulses reduce adaptation of retinal ganglion cells

Soto-Breceda, A., Kameneva, T., Hamish Meffin, A., Maturana, M. I., & Ibbotson, M. R. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aad46e · Cited by 25

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons

Probst, D., Petrovici, M. A., Bytschok, I., Bill, J., Pecevski, D., Schemmel, J., & Meier, K. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00013 · Cited by 23

A functional theory of bistable perception based on dynamical circular inference

Leptourgos, P., Bouttier, V., Jardri, R., & Denève, S. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008480 · Cited by 22

Beyond Bayes: On the Need for a Unified and Jaynesian Definition of Probability and Information within Neuroscience

Fiorillo, C. D. (2012) · Information · doi:10.3390/info3020175 · Cited by 20

Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity

Berthet, P., Lindahl, M., Tully, P. J., Kotaleski, J. H., & Lansner, A. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00053 · Cited by 19

Continuous real-world inputs can open up alternative accelerator designs

Belhadj, B., Joubert, A., Li, Z., Héliot, R., & Temam, O. (2013) · ACM SIGARCH Computer Architecture News · doi:10.1145/2508148.2485923 · Cited by 20

Bayesian Inference and Online Learning in Poisson Neuronal Networks

Huang, Y., & Rao, R. P. N. (2016) · Neural Computation · doi:10.1162/neco_a_00851 · Cited by 21

Predictive coding I: Introduction

Sprevak, M. D. (2023) · Philosophy Compass · doi:10.1111/phc3.12950 · Cited by 22

Deterministic networks for probabilistic computing

Jordan, J., Petrovici, M. A., Breitwieser, O., Schemmel, J., Meier, K., Diesmann, M., & Tetzlaff, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-54137-7 · Cited by 22

The meaning of spikes from the neuron’s point of view: predictive homeostasis generates the appearance of randomness

Fiorillo, C. D., Kim, J. K., & Hong, S. Z. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00049 · Cited by 19

The tuning of tuning: How adaptation influences single cell information transfer

Zeldenrust, F., Calcini, N., Xuan, Y., Bijlsma, A., & Celikel, T. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012043 · Cited by 19

Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series

Zeldenrust, F., de Knecht, S., Wadman, W. J., Denève, S., & Gutkin, B. S. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00049 · Cited by 18

Noise Facilitation in Associative Memories of Exponential Capacity

Karbasi, A., Salavati, A. H., Shokrollahi, A., & Varshney, L. R. (2014) · Neural Computation · doi:10.1162/neco_a_00655 · Cited by 17

A consensus layer V pyramidal neuron can sustain interpulse-interval coding

Singh, C., & Levy, W. B. (2017) · PLoS ONE · doi:10.1371/journal.pone.0180839 · Cited by 18

Controlling gain one photon at a time

Schwartz, G. W., & Rieke, F. M. (2013) · eLife · doi:10.7554/elife.00467 · Cited by 17

The Probabilistic Cell: Implementation of a Probabilistic Inference by the Biochemical Mechanisms of Phototransduction

Houillon, A., Bessìère, P., & Jacques Droulez, A. (2010) · Acta Biotheoretica · doi:10.1007/s10441-010-9104-y · Cited by 16

Hebbian Learning of Bayes Optimal Decisions

Nessler, B., Pfeiffer, M., & Maass, W. (2008) · Cited by 16

Heuristic to Bayesian: The evolution of reasoning from childhood to adulthood

Barash, J., Brocas, I., Carrillo, J. D., & Kodaverdian, N. (2018) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2018.05.008 · Cited by 16

A Learning Framework for Winner-Take-All Networks with Stochastic Synapses

Mostafa, H., & Cauwenberghs, G. (2018) · Neural Computation · doi:10.1162/neco_a_01080 · Cited by 12

Unsupervised learning predicts human perception and misperception of glossPreprint

Storrs, K. R., Anderson, B. L., & Fleming, R. W. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.07.026120 · Cited by 15

A stimulus-dependent spike threshold is an optimal neural coder

Jones, D. L., Johnson, E. C., & Ratnam, R. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00061 · Cited by 15

The Human KernelPreprint

Wilson, A. G., Dann, C., Lucas, C. G., & Xing, E. P. (2015) · arXiv (Cornell University) · Cited by 14

Canonical cortical circuits and the duality of Bayesian inference and optimal control

Doya, K. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.07.003 · Cited by 14

Characterization of dynamics and information processing of integrate-and-fire neuron models

Woo, J., Kim, S. H., Han, K., & Choi, M. Y. (2021) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8121/ac2a54 · Cited by 13

Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model

Bohté, S. M. (2012) · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · Cited by 12

Integration, coincidence detection and resonance in networks of spiking neurons expressing Gamma oscillations and asynchronous states

Susin, E., & Alain Destexhe, A. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009416 · Cited by 15

Know Thy Neighbour: A Normative Theory of Synaptic Depression

Pfister, J.-P., Dayan, P., & Lengyel, M. (2009) · MPG.PuRe (Max Planck Society) · Cited by 13

Iterative free-energy optimization for recurrent neural networks (INFERNO)

Pitti, A., Gaussier, P., & Quoy, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173684 · Cited by 15

Computational Models of Learning the Raising-Control Distinction

Mitchener, W. G., & Becker, M. (2010) · Research on Language and Computation · doi:10.1007/s11168-011-9073-6 · Cited by 12

Predictive Ensemble Decoding of Acoustical Features Explains Context-Dependent Receptive Fields

Yildiz, I. B., Mesgarani, N., & Denève, S. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.4648-15.2016 · Cited by 11

The Human KernelPreprint

Wilson, A. G., Dann, C., Lucas, C. G., & Xing, E. P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.07389 · Cited by 31

The thermodynamic temperature of a rhythmic spiking networkPreprint

Merolla, P. A., Ursell, T. S., & Arthur, J. V. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1009.5473 · Cited by 13

Further Challenges and the Road Ahead

Blauert, J. P., Kolossa, D., Obermayer, K., & Adiloğlu, K. (2013) · Springer eBooks · doi:10.1007/978-3-642-37762-4_18 · Cited by 11

Probabilistic inference of binary Markov random fields in spiking neural networks through mean-field approximation

Zheng, Y., Jia, S., Yu, Z., Huang, T., Liu, J. K., & Tian, Y. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.03.003 · Cited by 10

Bayesian Inference with Spiking Neurons

Denève, S. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_568 · Cited by 11

Electrical Signaling Beyond Neurons

Monk, T., Dennler, N., Ralph, N. O., Rastogi, S., Afshar, S., Urbizagastegui, P. A. D. A., Jarvis, R. J., André, V. S., & Adamatzky, A. (2024) · Neural Computation · doi:10.1162/neco_a_01696 · Cited by 11

Categorization is ‘baked’ into the brain

Barrett, L. F., & Miller, E. K. (2026) · Nature reviews. Neuroscience · doi:10.1038/s41583-026-01036-2 · Cited by 29

On Look-Ahead in Language: Navigating a Multitude of Familiar Paths

Edelman, S. (2011) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195395518.003.0060 · Cited by 12

Decoding stimulus duration from neural responses in the auditory midbrain

Aubie, B., Sayegh, R., Fremouw, T., Covey, E., & Faure, P. A. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00360.2014 · Cited by 10

Spiking neuron network Helmholtz machine

Sountsov, P., & Miller, P. C. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00046 · Cited by 11

Perceptual phenomena in destructured sensory fields: Probing the brain’s intrinsic functional architectures

Miskovic, V., Lynn, S. J., Bagg, J. O., Pouliot, J. J., Ríos, M., & Wackermann, J. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.01.014 · Cited by 12

Bayesian Inference with Spiking NeuronsPreprint

Paulin, M. G., & André, V. S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.5115 · Cited by 12

Modelling neural probabilistic computation using vector symbolic architectures

Furlong, P. M., & Eliasmith, C. (2023) · Cognitive Neurodynamics · doi:10.1007/s11571-023-10031-7 · Cited by 9

Spike-Based Probabilistic Inference in Analog Graphical Models Using Interspike-Interval Coding

Steimer, A., & Douglas, R. J. (2013) · Neural Computation · doi:10.1162/neco_a_00477 · Cited by 10

Visual-haptic cue integration with spatial and temporal disparity during pointing movements

Serwe, S., Kording, K. P., & Trommershäuser, J. (2011) · Experimental Brain Research · doi:10.1007/s00221-011-2603-5 · Cited by 9

Learning words in space and time: Contrasting models of the suspicious coincidence effect

Jenkins, G. W., Samuelson, L. K., Penny, W. D., & Spencer, J. P. (2021) · Cognition · doi:10.1016/j.cognition.2020.104576 · Cited by 10

Optimal Prediction of Moving Sound Source Direction in the Owl

Cox, W., & Fischer, B. J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004360 · Cited by 7

Neuronal Spike Generation Mechanism as an Oversampling, Noise-shaping A-to-D converter

Chklovskii, D. B., & Soudry, D. (2012) · Cited by 8

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo, Y., Zou, X., Hu, Y., Yang, Y., Wang, X., He, Y., Kong, R., Guo, Y., Li, G., Zhang, W., et al. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100054 · Cited by 14

Probabilistic Spike Propagation for Efficient Hardware Implementation of Spiking Neural Networks

Nallathambi, A., Sen, S., Raghunathan, A., & Chandrachoodan, N. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.694402 · Cited by 7

Neurons as ideal change-point detectors

Kim, H., Richmond, B. J., & Shinomoto, S. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-011-0344-x · Cited by 7

Approximate, Computationally Efficient Online Learning in Bayesian Spiking Neurons

Kuhlmann, L., Hauser-Raspe, M., Manton, J. H., Grayden, D. B., Tapson, J. C., & André, V. S. (2013) · Neural Computation · doi:10.1162/neco_a_00560 · Cited by 7

Optimal Sequential Detection of Stimuli from Multiunit Recordings Taken in Densely Populated Brain Regions

Nossenson, N., & Messer, H. (2011) · Neural Computation · doi:10.1162/neco_a_00257 · Cited by 7

The Evolutionary Origin of Nervous Systems and Implications for Neural Computation

Monk, T. (2014) · Otago University Research Archive (University of Otago) · Cited by 7

Knowing what you don’t know: Estimating the uncertainty of feedforward and feedback inputs with prediction-error circuitsPreprint

Hertäg, L., Wilmes, K. A., & Claudia Clopath, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.13.571410 · Cited by 7

LETTER Communicated by Richard Zemel Bayesian Spiking Neurons I: Inference

Denève, S. (2013)

Bayesian Inference with Spiking Neurons

Denève, S. (2015) · Springer eBooks · doi:10.1007/978-1-4614-6675-8_568 · Cited by 50

POSTER PRESENTATION Open Access Functional interpretation of biophysical properties of spiking neurons

Zeldenrust, F., Gutkin, B. S., & Denève, S. (2016)

Neurons as Monte Carlo Samplers: Sequential Bayesian Inference in Spiking Neural Populations

Rao, R. P. N. (2009) · Frontiers in Systems Neuroscience · doi:10.3389/conf.neuro.06.2009.03.048 · Cited by 1

Bayesian spiking neurons II: learning

Deneve, S. (2008) · Neural Computation · doi:10.1162/neco.2008.20.1.118 · Cited by 71