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arXiv · 1407.6803

Heterogeneous Mean Field for neural networks with short term plasticity

Matteo di Volo·Raffaella Burioni·Mario Casartelli·Roberto Livi·Alessandro Vezzani
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Abstract

We report about the main dynamical features of a model of leaky-integrate-and fire excitatory neurons with short term plasticity defined on random massive networks. We investigate the dynamics by a Heterogeneous Mean-Field formulation of the model, that is able to reproduce dynamical phases characterized by the presence of quasi-synchronous events. This formulation allows one to solve also the inverse problem of reconstructing the in-degree distribution for different network topologies from the knowledge of the global activity field. We study the robustness of this inversion procedure, by providing numerical evidence that the in-degree distribution can be recovered also in the presence of noise and disorder in the external currents. Finally, we discuss the validity of the heterogeneous mean-field approach for sparse networks, with a sufficiently large average in-degree.

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Matteo di Volo, Raffaella Burioni, Mario Casartelli, Roberto Livi, Alessandro Vezzani. 2014-07-25. Heterogeneous Mean Field for neural networks with short term plasticity. https://doi.org/10.1103/physreve.90.022811

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