Literature DB >> 22714650

Population density models of integrate-and-fire neurons with jumps: well-posedness.

Grégory Dumont1, Jacques Henry.   

Abstract

In this paper we study the well-posedness of different models of population of leaky integrate-and-fire neurons with a population density approach. The synaptic interaction between neurons is modeled by a potential jump at the reception of a spike. We study populations that are self excitatory or self inhibitory. We distinguish the cases where this interaction is instantaneous from the one where there is a repartition of conduction delays. In the case of a bounded density of delays both excitatory and inhibitory population models are shown to be well-posed. But without conduction delay the solution of the model of self excitatory neurons may blow up. We analyze the different behaviours of the model with jumps compared to its diffusion approximation.

Mesh:

Year:  2012        PMID: 22714650     DOI: 10.1007/s00285-012-0554-5

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  13 in total

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Review 5.  A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input.

Authors:  A N Burkitt
Journal:  Biol Cybern       Date:  2006-04-19       Impact factor: 2.086

6.  Critical analysis of dimension reduction by a moment closure method in a population density approach to neural network modeling.

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Journal:  Neural Comput       Date:  2007-08       Impact factor: 2.026

7.  Synchronization properties of networks of electrically coupled neurons in the presence of noise and heterogeneities.

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8.  Synchrony and asynchrony in a fully stochastic neural network.

Authors:  R E Lee DeVille; Charles S Peskin
Journal:  Bull Math Biol       Date:  2008-05-30       Impact factor: 1.758

9.  Cascade-induced synchrony in stochastically driven neuronal networks.

Authors:  Katherine A Newhall; Gregor Kovačič; Peter R Kramer; David Cai
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2010-10-01

10.  Analysis of nonlinear noisy integrate & fire neuron models: blow-up and steady states.

Authors:  María J Cáceres; José A Carrillo; Benoît Perthame
Journal:  J Math Neurosci       Date:  2011-07-18       Impact factor: 1.300

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  1 in total

1.  Noisy threshold in neuronal models: connections with the noisy leaky integrate-and-fire model.

Authors:  G Dumont; J Henry; C O Tarniceriu
Journal:  J Math Biol       Date:  2016-04-04       Impact factor: 2.259

  1 in total

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