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A dynamical state underlying the second order maximum entropy principle in neuronal networks
2017
Communications in Mathematical Sciences
The maximum entropy principle is widely used in diverse fields. We address the issue of why the second order maximum entropy model, by using only firing rates and second order correlations of neurons as constraints, can well capture the observed distribution of neuronal firing patterns in many neuronal networks, thus, conferring its great advantage in that the degree of complexity in the analysis of neuronal activity data reduces drastically from O(2 n ) to O(n 2 ), where n is the number of
doi:10.4310/cms.2017.v15.n3.a5
fatcat:6bymxdueqrcjfdoblt6rtbcgwa