CNR ExploRA

Articolo in rivista, 2014, ENG, 10.1103/PhysRevE.90.022811

Heterogeneous mean field for neural networks with short-term plasticity

Matteo di Volo (1,2,3); Raffaella Burioni (1,3); Mario Casartelli (1,3); Roberto Livi (2,4,5,6); Alessandro Vezzani (1,7)

(1) Dipartimento di Fisica e Scienza della Terra, Università di Parma, via G.P. Usberti, 7/A-43124, Parma, Italy (2) Centro Interdipartimentale per lo Studio delle Dinamiche Complesse, via Sansone, 1-50019 Sesto Fiorentino, Italy (3) INFN, Gruppo Collegato di Parma, via G.P. Usberti, 7/A-43124, Parma, Italy (4) Dipartimento di Fisica, Università di Firenze, via Sansone, 1-50019 Sesto Fiorentino, Italy (5) Istituto dei Sistemi Complessi, CNR, via Madonna del Piano 10-50019 Sesto Fiorentino, Italy (6) INFN Sez. Firenze, via Sansone, 1-50019 Sesto Fiorentino, Italy (7) S3, CNR Istituto di Nanoscienze, Via Campi, 213A-41125 Modena, Italy

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 use of a heterogeneous mean-field formulation of the model that is able to reproduce dynamical phases characterized by the presence of quasisynchronous 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.

Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics 90 (2), pp. 2811–2811