Bayesian filtering in spiking neural networks: noise, adaptation, and multisensory integration.


Journal Article

A key requirement facing organisms acting in uncertain dynamic environments is the real-time estimation and prediction of environmental states, based on which effective actions can be selected. While it is becoming evident that organisms employ exact or approximate Bayesian statistical calculations for these purposes, it is far less clear how these putative computations are implemented by neural networks in a strictly dynamic setting. In this work, we make use of rigorous mathematical results from the theory of continuous time point process filtering and show how optimal real-time state estimation and prediction may be implemented in a general setting using simple recurrent neural networks. The framework is applicable to many situations of common interest, including noisy observations, non-Poisson spike trains (incorporating adaptation), multisensory integration, and state prediction. The optimal network properties are shown to relate to the statistical structure of the environment, and the benefits of adaptation are studied and explicitly demonstrated. Finally, we recover several existing results as appropriate limits of our general setting.

Full Text

Cited Authors

  • Bobrowski, O; Meir, R; Eldar, YC

Published Date

  • May 2009

Published In

Volume / Issue

  • 21 / 5

Start / End Page

  • 1277 - 1320

PubMed ID

  • 19018706

Pubmed Central ID

  • 19018706

Electronic International Standard Serial Number (EISSN)

  • 1530-888X

International Standard Serial Number (ISSN)

  • 0899-7667

Digital Object Identifier (DOI)

  • 10.1162/neco.2008.01-08-692


  • eng