Statistics of spike trains in conductance-based neural networks: Rigorous results

Details Statistics of spike trains in conductance-based neural networks: Rigorous results Statistics of spike trains in conductance-based neural networks: Rigorous results

2011-04-19

arxiv.org/abs/1104.3795v2

Physics

Mathematical Physics

42 pages, 1 figure, to appear in Journal of Mathematical Neuroscience

Scientific paper

We consider a conductance based neural network inspired by the generalized Integrate and Fire model introduced by Rudolph and Destexhe. We show the existence and uniqueness of a unique Gibbs distribution characterizing spike train statistics. The corresponding Gibbs potential is explicitly computed. These results hold in presence of a time-dependent stimulus and apply therefore to non-stationary dynamics.

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