Bayesian spiking neurons I: inference.
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.
Département d'Etudes Cognitives, Ecole Normale Supérieure, Collège de France, 75005 Paris, France. sophie.deneve@ens.fr
Sophie
S
Deneve
D000200:Action Potentials / Q000502:physiology*; D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D002490:Central Nervous System / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009068:Movement / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010465:Perception / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology