title: Accuracy of rate coding: When shorter time window and higher spontaneous activity help.
abstract: It is widely accepted that neuronal firing rates contain a significant amount of information about the stimulus intensity. Nevertheless, theoretical studies on the coding accuracy inferred from the exact spike counting distributions are rare. We present an analysis based on the number of observed spikes assuming the stochastic perfect integrate-and-fire model with a change point, representing the stimulus onset, for which we calculate the corresponding Fisher information to investigate the accuracy of rate coding. We analyze the effect of changing the duration of the time window and the influence of several parameters of the model, in particular the level of the presynaptic spontaneous activity and the level of random fluctuation of the membrane potential, which can be interpreted as noise of the system. The results show that the Fisher information is nonmonotonic with respect to the length of the observation period. This counterintuitive result is caused by the discrete nature of the count of spikes. We observe also that the signal can be enhanced by noise, since the Fisher information is nonmonotonic with respect to the level of spontaneous activity and, in some cases, also with respect to the level of fluctuation of the membrane potential.
authors.affiliation: Department of Computational Neuroscience, Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14220 Prague 4, Czech Republic.
authors.forename: Marie
authors.initials: M
authors.lastname: Levakova
authors.affiliation: Institute for Stochastics, Johannes Kepler University Linz, Altenbergerstraße 69, 4040 Linz, Austria.
authors.forename: Massimiliano
authors.initials: M
authors.lastname: Tamborrino
authors.affiliation: Department of Computational Neuroscience, Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14220 Prague 4, Czech Republic.
authors.forename: Lubomir
authors.initials: L
authors.lastname: Kostal
authors.affiliation: Department of Computational Neuroscience, Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14220 Prague 4, Czech Republic.
authors.forename: Petr
authors.initials: P
authors.lastname: Lansky