Matteo di Volo - Università di Parma #
Heterogeneous mean field and global inverse problem in neural
networks with short term plasticity #
We discuss a heterogeneous mean field approach to neural dynamics on
random networks, that explicitly preserves the disorder in the structure
of connections 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. Furthermore, we formulate and
solve a global inverse problem of reconstructing the network in-degree
distribution from the knowledge of the average activity field.
The method is very general and applies to a large class of dynamical
models on random networks.