Elena Bertolotti – Università degli Studi di Parma #
Synchronization properties of excitatory and inhibitory neural networks with highly connected nodes #
Synchronization is one of the most surprising phenomena that occurs in systems of coupled oscillators. A 
system is synchronized if its individual elements show a common or a temporal correlated evolution and 
therefore a coherent collective dynamics, which largely depends on the interactions structure of the system, 
on its individual elements and on their coupling. Synchronization is a universal phenomenon, which appears 
in systems belonging to various research fields and particularly in the brain tissue, which can be modelled 
through neural networks. A neural network is a dynamic system built on a graph, whose nodes are occupied by 
individual oscillators (neurons), and whose links represent their synapses. These neural systems can 
reproduce the synchronous phases that are experimentally observed and that in brain tissues are linked, 
depending on the cerebral area in which they occur, with cognitive processes, such as memory, or malfunction 
phases.In the present work we want to see how synchronization properties of a neural network change if the 
interactions structure of the system is modified, especially if some nodes with a very high number of 
connections (hubs) are present and if we take into account both the excitatory and inhibitory mechanisms. 
The starting idea is based on a recent study1, which from analysis of electrical activity in some cerebral 
areas of mice proves the existence of population of inhibitory hubs nodes, which carry out a regulatory 
function for the whole system.
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[1] P. Bonifazi and et al., Science, 326(5958):1419–1424, December 2009