Firing rate estimation using an approximate bayesian method

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. © 2009 Springer Berlin Heidelberg.