Linear response for spiking neuronal networks with unbounded memory

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...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2021-02, Vol.23 (2)
Main Authors: Cessac, Bruno, Ampuero, Ignacio, Cofré, Rodrigo
Format: Article
Language:English
Subjects:
Condensed Matter
Disordered Systems and Neural Networks
Dynamical Systems
Life Sciences
Mathematical Physics
Mathematics
Neurons and Cognition
Physics
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Summary: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.
ISSN:1099-4300
1099-4300
DOI:10.3390/e23020155