Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Bayesian spiking neurons I: inference.

Sophie Deneve1

  • 1Département d'Etudes Cognitives, Ecole Normale Supérieure, Collège de France, 75005 Paris, France. sophie.deneve@ens.fr

Neural Computation
|November 30, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Conspiracy beliefs and perceptual inference in times of political uncertainty.

Scientific reports·2024
Same author

Functional connectivity and glutamate levels of the medial prefrontal cortex in schizotypy are related to sensory amplification in a probabilistic reasoning task.

NeuroImage·2023
Same author

From hallucinations to synaesthesia: A circular inference account of unimodal and multimodal erroneous percepts in clinical and drug-induced psychosis.

Neuroscience and biobehavioral reviews·2022
Same author

No increased circular inference in adults with high levels of autistic traits or autism.

PLoS computational biology·2021
Same author

Efficient and robust coding in heterogeneous recurrent networks.

PLoS computational biology·2021
Same author

Predictive learning as a network mechanism for extracting low-dimensional latent space representations.

Nature communications·2021
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Spiking neuron dynamics function as Bayesian inference over time. Neurons optimally integrating external evidence exhibit properties of leaky integrate-and-fire models, with spikes signaling novel information.

Area of Science:

  • Computational Neuroscience
  • Theoretical Neuroscience
  • Information Theory

Background:

  • Spiking neurons are fundamental units of neural computation.
  • Understanding how neurons process information dynamically is crucial.
  • Bayesian inference provides a normative framework for optimal decision-making.

Purpose of the Study:

  • To interpret the dynamics of spiking neurons through the lens of Bayesian inference.
  • To identify neural properties that facilitate optimal evidence integration.
  • To characterize the information conveyed by neuronal spikes.

Main Methods:

  • Theoretical modeling of spiking neuron dynamics.
  • Analysis of evidence integration in neural systems.
  • Comparison of model properties with leaky integrate-and-fire neurons.

Related Experiment Videos

Main Results:

  • Spiking neuron dynamics can be understood as temporal Bayesian inference.
  • Optimal evidence integrators resemble leaky integrate-and-fire neurons with adaptation.
  • Spikes represent unpredictable new information, leading to near-Poisson firing statistics.

Conclusions:

  • Neuronal spiking is a mechanism for performing Bayesian inference in real-time.
  • The properties of optimal neurons align with established biophysical models.
  • Spikes deterministically encode probabilities by signaling information novelty.