Dynamics, heterogeneous mean field and global inverse problem in spiking neural networks with plasticity

Datum

09.08.2016

Zeit

14:00 - 15:00

Sprecher

Matteo di Volo (Ecole Normale Superieure

Zugehörigkeit

Paris)

Sprache

en

Hauptthema

Physik

Andere Themen

Physik

Host

Advanced Study Group

Beschreibung

The dynamics of neural networks is often characterized by collective behavior and quasi-synchronous events, where a large fraction of neurons fire in short time intervals, separated by uncorrelated firing activity. These global temporal signals are crucial for brain functioning. They strongly depend on the topology of the network and on the fluctuations of the connectivity. We propose a heterogeneous mean–fieldapproach to neural dynamics on random networks, that explicitly preserves the disorder in the topology at growing network sizes, and leads to a set of self-consistent equations. Within this approach, we provide an effective description of microscopic and large scale temporal signals in a leaky integrate-and-fire model with short term plasticity. After describing the emergence of non trivial collective dynamics we formulate and solve a global inverse problem of reconstructing the in-degree distribution from the knowledge of the average activity field.

Letztmalig verändert: 09.08.2016, 09:47:42