Cited by 314Neural dynamics and brain functionstochastic dynamics and bifurcationAdvanced Memory and Neural Computing

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Bayesian Spiking Neurons I: Inference

Deneve, Sophie·2008·Neural Computation

DOI 10.1162/neco.2008.20.1.91

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Abstract

We show that the dynamics of spiking neurons can be interpreted as a form of Bayesian inference in time. Neurons that optimally integrate evidence about events in the external world exhibit properties similar to leaky integrate-and-fire neurons with spike-dependent adaptation and maximally respond to fluctuations of their input. Spikes signal the occurrence of new information-what cannot be predicted from the past activity. As a result, firing statistics are close to Poisson, albeit providing a deterministic representation of probabilities.

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Authors: Deneve, S.
Affiliations: Group for Neural Theory, Département d'Etudes Cognitives, Ecole Normale Supérieure, Collège de France, 75005 Paris, France
Venue: Neural Computation
Publisher: MIT Press
Published: 2008-01
Volume: 20
Issue: 1
Pages: 91-117
ISSN: 0899-7667 (ISSN-L)
1530-888X
DOI: 10.1162/neco.2008.20.1.91
OpenAlex: W2139295975
Cited by: 313
References: 50
Citations by year: 2026: 3 · 2025: 14 · 2024: 12 · 2023: 16 · 2022: 9 · 2021: 15 · 2020: 10 · 2019: 13 · 2018: 14 · 2017: 14 · 2016: 29 · 2015: 26 · 2014: 24 · 2013: 22 · 2012: 29
FWCI: 7.66
Type: journal article
Language: en
Open access: closed
Locations: Neural Computation (publishedVersion)
PubMed (publishedVersion)
Funders: Gatsby Charitable Foundation
Ministry of Education, Culture, Sports, Science and Technology
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
Keywords: spiking neurons, Bayesian inference, leaky integrate-and-fire neurons, evidence integration, neural coding
MeSH terms: Action Potentials, Adaptation, Physiological, Algorithms, Animals, Bayes Theorem, Central Nervous System, Computer Simulation, Humans, Markov Chains, Movement, Nerve Net, Synaptic Transmission, Neurons, Perception, Models, Statistical, Neural Networks, Computer

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Bayesian Spiking Neurons I: Inference

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