PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration
Omer Bobrowski, Ron Meir and Yonina Eldar
Neural Computation Volume In Press, 2008.

Abstract

A key requirement facing organisms acting in uncertain dynamic environments is the real-time estimation and prediction of environmental states, based upon 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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
Multimodal Integration
ID Code:5249
Deposited By:Ron Meir
Deposited On:24 March 2009