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Maximum likelihood decoding of neuronal inputs from an interspike interval distribution

By Xuejuan Zhang, Gongqiang You, Tianping Chen and Jianfeng Feng

Abstract

An expression for the probability distribution of the interspike interval\ud of a leaky integrate-and-fire (LIF) model neuron is rigorously derived,\ud based on recent theoretical developments in the theory of stochastic processes.\ud This enables us to find for the first time a way of developing\ud maximum likelihood estimates (MLE) of the input information (e.g., afferent\ud rate and variance) for an LIF neuron from a set of recorded spike\ud trains. Dynamic inputs to pools of LIF neurons both with and without\ud interactions are efficiently and reliably decoded by applying the MLE,\ud even within time windows as short as 25 msec

Topics: QA
Publisher: MIT Press
Year: 2009
OAI identifier: oai:wrap.warwick.ac.uk:3039

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