Linear response for spiking neuronal networks with unbounded memory

Abstract : We establish a general linear response relation for spiking neuronal networks, based on chains with unbounded memory. This relation allows quantifying the influence of a weak amplitude external stimuli on spatio-temporal spike correlations, in a general context where the memory in spike dynamics can go arbitrarily far in the past. With this approach, we show how linear response is explicitly related to neuron dynamics with an example, the gIF model, introduced by M. Rudolph and A. Destexhe [91]. This illustrates the effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike statistics.
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https://hal.inria.fr/hal-01895095
Contributor : Bruno Cessac <>
Submitted on : Saturday, October 13, 2018 - 8:23:54 PM
Last modification on : Friday, April 19, 2019 - 4:55:27 PM
Long-term archiving on : Monday, January 14, 2019 - 1:22:55 PM

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  • HAL Id : hal-01895095, version 1
  • ARXIV : 1704.05344

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Bruno Cessac, Rodrigo Cofré. Linear response for spiking neuronal networks with unbounded memory. 2018. ⟨hal-01895095⟩

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