Heterogeneous Mean Field for neural networks with short term plasticity

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.

References in corpus (3)

• Critical phenomena in complex networks
• Stability of the splay state in pulse--coupled networks
• Stability of splay states in globally coupled rotators

Cited by in corpus (4)

• Chaos and correlated avalanches in excitatory neural networks with synaptic plasticity
• Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity
• A microscopic mechanism for self-organized quasi periodicity in random networks of non linear oscillators
• Stochastic mean field formulation of the dynamics of diluted neural networks