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ISSN
0899-7667
E-ISSN
1530-888X
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2.21

Neural Computation

November 2009, Vol. 21, No. 11, Pages 3079-3105.
(doi: 10.1162/neco.2009.06-08-807)
© 2009 Massachusetts Institute of Technology
Maximum Likelihood Decoding of Neuronal Inputs from an Interspike Interval Distribution
Article PDF (767.43 KB)
Abstract

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