AI Article Synopsis
- The study focuses on analyzing spike trains in leaky Integrate-and-Fire neuron networks, considering discrete time and noise, while allowing for any synaptic weight.
- It establishes a unique Gibbs-type invariant measure characterizing these spike trains and explores its properties.
- The research connects these findings with Markovian approximations and discusses their relevance to current methodologies in computational neuroscience for studying experimental spike train statistics.
Article Abstract
We provide rigorous and exact results characterizing the statistics of spike trains in a network of leaky Integrate-and-Fire neurons, where time is discrete and where neurons are submitted to noise, without restriction on the synaptic weights. We show the existence and uniqueness of an invariant measure of Gibbs type and discuss its properties. We also discuss Markovian approximations and relate them to the approaches currently used in computational neuroscience to analyse experimental spike trains statistics.
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| http://dx.doi.org/10.1007/s00285-010-0358-4 | DOI Listing |
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