The effect of connectivity on information in neural networks. Issue 2 (2nd February 2018) - Onesto, V.; Narducci, R.; Amato, F.; Cancedda, L.; Gentile, F.
Abstract : A mathematical model quantifies the amount of information/exchanged in neural networks as a function of network connectivity Q . Abstract : We present a mathematical model that quantifies the amount of information exchanged in bi-dimensional networks of nerve cells as a function of network connectivity Q . Upon varying Q over a significant range, we found that, from a certain cell density onwards, 90% of the maximal information transferred I ( Q ) in a random neuronal network is already reached with just 40% of the total possible connections Q among the cells. As a consequence, the system would not benefit from additional connections in terms of the amount of I ( Q ), in agreement with the tendency of brains to minimize Q because of its energetic costs. The model may reveal the circuits responsible for neurodegenerative disorders in that neurodegeneration can be regarded as a connective failure affecting information.