Bayesian Spiking Neurons I: Inference

Humans integrate visual and haptic information in a statistically optimal fashion

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A Computational Framework for the Study of Confidence Across Species

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What and where: a bayesian inference theory of visual attention

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How a well-adapting immune system remembersPreprint

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Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization

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The neural basis for uncertainty processing in hierarchical decision making

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Attention as Reward-Driven Optimization of Sensory Processing

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Probabilistic models of state estimation predict visuomotor transformations during prism adaptation

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Information transfer and recovery for the sense of touch

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Neuronal Sequence Models for Bayesian Online Inference

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Bayesian decision making in two-alternative forced choices

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

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Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences

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A neural model that implements probabilistic topics

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Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics

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The structural aspects of neural dynamics and information flow

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Cell signaling as a probabilistic computer

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Novelty detection improves performance of reinforcement learners in fluctuating, partially observable environments

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The Leaky Integrate-and-Fire Neuron Is a Change-Point Detector for Compound Poisson Processes

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Impact of sub and supra-threshold adaptation currents in networks of spiking neurons

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Probabilistic and Bayesian Networks

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A detailed theory of thalamic and cortical microcircuits for predictive visual inference

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Streaming Solutions for Time-Varying Optimization Problems

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Subjective Physics

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CMOL/CMOS Hardware Architectures and Performance/price for Bayesian Memory - The Building Block of Intelligent Systems

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Subjective physicsPreprint

Brette, R. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1311.3129 · Cited by 6

Probabilistic spike propagation for FPGA implementation of spiking neural networksPreprint

Nallathambi, A., & Chandrachoodan, N. (2020) · doi:10.36227/techrxiv.11565627.v1 · Cited by 3

Predictive learning rules generate a cortical-like replay of probabilistic sensory experiencesPreprint

Asabuki, T., & Fukai, T. (2024) · eLife · doi:10.7554/elife.92712 · Cited by 4

Bayesian electronics for trustworthy artificial intelligence

Querlioz, D., & Vianello, E. (2025) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-025-00226-x · Cited by 3

Predictive coding with spiking neural networks: A survey

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Estimation of firing rate from instantaneous interspike intervals

Kostal, L., & Kovacova, K. (2024) · Neuroscience Research · doi:10.1016/j.neures.2024.06.006 · Cited by 2

Optogenetic Stimulation in a Computational Model of the Basal Ganglia Biases Action Selection and Reward Prediction Error

Berthet, P., & Lansner, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090578 · Cited by 2

Functional interpretation of biophysical properties of spiking neurons

Zeldenrust, F., Gutkin, B. S., & Denève, S. (2013) · BMC Neuroscience · doi:10.1186/1471-2202-14-s1-p104 · Cited by 2

Active dendrites: adaptation to spike-based communication

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Neural Information Processing and Computations of Two-Input Synapses

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Inference in spiking Bayesian neurons using stochastic computation

Thakur, C. S., Molin, J. L., Etienne‐Cummings, R., & André, V. S. (2017) · Conference on Information Sciences and Systems · doi:10.1109/ciss.2017.7926170 · Cited by 2

Formal Tools for the Analysis of Brain-Like Structures and Dynamics

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Computation in Balanced Networks

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Toward Biochemical Probabilistic ComputationPreprint

Jacques Droulez, A., Colliaux, D., Houillon, A., & Bessìère, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.02623 · Cited by 2

Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible to Various Temporal CodesPreprint

Hong, C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.00583 · Cited by 3

Stochastic models of evidence accumulation in changing environmentsPreprint

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Towards the Next Generation of Retinal Neuroprosthesis: Visual Computation with SpikesPreprint

Yu, Z., Liu, J. K., Jia, S., Zhang, Y., Zheng, Y., Tian, Y., & Huang, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.04064 · Cited by 2

Multisensory integration in chaotic networks

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Probabilistic and Bayesian Networks

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Multi-Scale Information, Network, Causality, and Dynamics: Mathematical Computation and Bayesian Inference to Cognitive Neuroscience and Aging

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Bayesian approaches to modelling action selection

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From computation to biophysics: functional theory of adaptive neural excitability

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Online learning in Bayesian Spiking Neurons

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Various Approaches to Decision Making

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

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Noise Helps Optimization Escape From Saddle Points in the Synaptic Plasticity

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Computational Psychiatry: Combining multiple levels of analysis to understand brain disorders

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Biologically-Plausible Markov Chain Monte Carlo Sampling from Vector Symbolic Algebra-Encoded Distributions

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A 1T1M-based efficient probability storage and computing cell for the VAE-SBN hybrid model

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Bayesian Inference with Spiking Neurons

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Network analysis of high-density microelectrode recordingsPreprint

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Noise Facilitation in Associative Memories of Exponential CapacityPreprint

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Probabilistic spike propagation for FPGA implementation of spiking neural networksPreprint

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Probabilistic Boolean Operation in Networks of Spiking Neurons for Bayesian Inference of Hidden Markov ModelsPreprint

Chakraborty, A., & Chakrabarti, S. (2023) · doi:10.36227/techrxiv.22695691.v1 · Cited by 1

Optimal prediction and natural scene statistics in the retinaPreprint

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Information, Novelty, and Surprise in Brain Theory

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MSE-based analysis of optimal tuning functions predicts phenomena observed in sensory neuronsPreprint

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A Biologically Realistic Model of Saccadic Eye Control with Probabilistic Population CodesPreprint

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Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron.Preprint

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Bayesian inference from single spikes

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Instability and InformationPreprint

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The Origin of Inference: Ediacaran Ecology and the Evolution of Bayesian BrainsPreprint

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UNCERTAINTY AND DECISION-MAKING IN THE HUMAN BRAIN

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Probabilistic Inference in Neural Networks

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CONNECTIONS BETWEEN C N OMPUTATIONAL AND NEUROBIOLOGICAL PERSPECTIVES ON DECISION MAKING Decision theory, reinforcement learning, and the brain

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Investigation of Synapto-dendritic Kernel Adapting Neuron models and their use in spiking neuromorphic architectures

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Characterizing predictive auditory processing with EEG

Reiche, M. (2016) · Qucosa (Saxon State and University Library Dresden)

Development of computational models for emotional diary text analysis to support maternal carePreprint

Lahti, L., Tenhunen, H., Heinonen, S. T., Helkavaara, M., Pöyhönen‐Alho, M., & Torkki, P. M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.04158 · Cited by 1

Computational models of belief propagation

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Mechanistic philosophy and the use of deep neural networks in neuroscience

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Emotion processing in individuals with autistic spectrum conditions and autistic traits : the role of stimuli spontaneity and task demands

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Hidden State Inference in the Midbrain Dopamine System

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Information, Novelty, and Surprise in Brain Theory

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Learning rules for cortical-like spontaneous replay of an internal modelPreprint

Asabuki, T., & Fukai, T. (2023) · Research Square · doi:10.21203/rs.3.rs-2599451/v1

Probabilistic Boolean Operation in Networks of Spiking Neurons for Bayesian Inference of Hidden Markov ModelsPreprint

Chakraborty, A., & Chakrabarti, S. (2023) · doi:10.36227/techrxiv.22695691

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

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Emotion regulation monitoring in aging: the predictive effects of age and effort in determining emotion regulation failure

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Reviewer #3 (Public Review): Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences

Asabuki, T., & Fukai, T. (2024) · eLife · doi:10.7554/elife.92712.1.sa0

Predictive learning rules generate a cortical-like replay of probabilistic sensory experiencesPreprint

Asabuki, T., & Fukai, T. (2024) · eLife · doi:10.7554/elife.92712.1

Predictive learning rules generate a cortical-like replay of probabilistic sensory experiencesPreprint

Asabuki, T., & Fukai, T. (2024) · eLife · doi:10.7554/elife.92712.2

Bayesian Inference of Hidden Markov Models Through Probabilistic Boolean Operations in Spiking Neuronal Networks

Chakraborty, A., & Chakrabarti, S. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3502472

A Computational Model of Action Specification in the Basal GangliaPreprint

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Learning and inference with correlated neural variability

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Neural heterogeneity enables adaptive encoding of time sequences

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Commentary/Jones & Love: Bayesian Fundamentalism or Enlightenment?

Holyoaka, K. J., & Lua, H. (2011)

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