Firing rate estimation using an approximate Bayesian method

Authors:
Kazuho Watanabe;Masato Okada
Affiliations:
Department of Complexity Science and Engineering, The University of Tokyo, Kashiwa, Japan;Department of Complexity Science and Engineering, The University of Tokyo, Kashiwa, Japan and Brain Science Institute, RIKEN, Wako, Saitama, Japan
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

Bayesian estimation methods are used for estimation of an event rate (firing rate) from a series of event (spike) times. Generally, however, the computation of the Bayesian posterior distribution involves an analytically intractable integration. An event rate is defined in a very high dimensional space, which makes it computationally demanding to obtain the Bayesian posterior distribution of the rate. We consider the estimation of the firing rate underlying behind a sequence that represents the counts of spikes. We derive an approximate Bayesian inference algorithm for it, which enables the analytical calculation of the posterior distribution. We also provide a method to estimate the prior hyperparameter which determines the smoothness of the estimated firing rate.