Dynamics and functional connectivity in a network derived from anatomical data of the mouse brain

Abstract

This project studies the collective dynamics of a neural network constructed from experimental connectivity data of the mouse brain. The Izhikevich model has been used to simulate the dynamics of neurons under different noise and excitatory strength conditions. The analysis has focused on how these parameters affect the global synchronization of the network, as well as on the comparison of three structural configurations: the original one, one with randomly redistributed connections, and another one with an eliminated module of the original network. From the activity data, functional matrices have been generated, and measures such as mean degree, global efficiency, and degree of modularity have been calculated. The results show how the structural properties influence the functional organization of the network and its synchronization capacity