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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

The effect of graph connectivity on metastability in a stochastic system of spiking neurons

Texto completo
Autor(es):
Andre, Morgan [1] ; Planche, Leo [1]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Inst Matemat & Estat, Sao Paulo - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: Stochastic Processes and their Applications; v. 131, p. 292-310, JAN 2021.
Citações Web of Science: 0
Resumo

We consider a continuous-time stochastic model of spiking neurons originally introduced by Ferrari et al. in Ferrari et al. (2018). In this model, we have a finite or countable number of neurons which are vertices in some graph G where the edges indicate the synaptic connection between them. We focus on metastability, understood as the property for the time of extinction of the network to be asymptotically memory-less, and we prove that this model exhibits two different behaviors depending on the nature of the specific underlying graph of interaction G that is chosen. In this model the spiking activity of any given neuron is represented by a point process, whose rate fluctuates between 1 and 0 over time depending on whether the membrane potential is positive or null. The membrane potential of each neuron evolves in time by integrating all the spikes of its adjacent neurons up to the last spike of the said neuron, so that when a neuron spikes, its membrane potential is reset to 0 while the membrane potential of each of its adjacent neurons is increased by one unit. Moreover, each neuron is exposed to a leakage effect, modeled as an abrupt loss of membrane potential which occurs at random times driven by a Poisson process of some fixed rate gamma. It was previously proven that when the graph G is the infinite one-dimensional lattice, this model presents a phase transition with respect to the parameter gamma. It was also proven that, when gamma is small enough, the renormalized time of extinction (the first time at which all neurons have a null membrane potential) of a finite version of the system converges in law toward an exponential random variable when the number of neurons goes to infinity. The present article is divided into two parts. First we prove that, in the finite one-dimensional lattice, this last result does not hold anymore if gamma is large enough, and in fact we prove that for gamma > 1 the renormalized time of extinction is asymptotically deterministic. Then we prove that conversely, if G is the complete graph, the result of metastability holds for any positive gamma. (C) 2020 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 17/02035-7 - Inferência de grafos de interação da atividade neural
Beneficiário:Morgan Florian Thibault André
Modalidade de apoio: Bolsas no Brasil - Doutorado
Processo FAPESP: 19/14367-0 - Study of graphs underlied in brain processes
Beneficiário:Léo Benoit Planche
Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 13/07699-0 - Centro de Pesquisa, Inovação e Difusão em Neuromatemática - NeuroMat
Beneficiário:Oswaldo Baffa Filho
Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs