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Hawkes processes with variable length memory and an infinite number of components

  • Pierre Hodara (a1) and Eva Löcherbach (a1)
Abstract
Abstract

In this paper we propose a model for biological neural nets where the activity of the network is described by Hawkes processes having a variable length memory. The particularity in this paper is that we deal with an infinite number of components. We propose a graphical construction of the process and build, by means of a perfect simulation algorithm, a stationary version of the process. To implement this algorithm, we make use of a Kalikow-type decomposition technique. Two models are described in this paper. In the first model, we associate to each edge of the interaction graph a saturation threshold that controls the influence of a neuron on another. In the second model, we impose a structure on the interaction graph leading to a cascade of spike trains. Such structures, where neurons are divided into layers, can be found in the retina.

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* Postal address: CNRS UMR 8088, Département de Mathématiques, Université de Cergy-Pontoise, 2 avenue Adolphe Chauvin, 95302 Cergy-Pontoise Cedex, France.
** Email address: pierre.hodara@u-cergy.fr
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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

[9] N. Fournier and E. Löcherbach (2016).On a toy model for interacting neurons.Ann. Inst. H. Poincaré Prob. Statist. 52,18441876.

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[18] P. Reynaud-Bouret and S. Schbath (2010).Adaptive estimation for Hawkes processes: application to genome analysis.Ann. Statist. 38,27812822.

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Advances in Applied Probability
  • ISSN: 0001-8678
  • EISSN: 1475-6064
  • URL: /core/journals/advances-in-applied-probability
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