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
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