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Reconstruction Of Sparse Recurrent Connectivity And Inputs From The Nonlinear Dynamics Of Neuronal Networks, Victor J. Barranca
Reconstruction Of Sparse Recurrent Connectivity And Inputs From The Nonlinear Dynamics Of Neuronal Networks, Victor J. Barranca
Mathematics & Statistics Faculty Works
Reconstructing the recurrent structural connectivity of neuronal networks is a challenge crucial to address in characterizing neuronal computations. While directly measuring the detailed connectivity structure is generally prohibitive for large networks, we develop a novel framework for reverse-engineering large-scale recurrent network connectivity matrices from neuronal dynamics by utilizing the widespread sparsity of neuronal connections. We derive a linear input-output mapping that underlies the irregular dynamics of a model network composed of both excitatory and inhibitory integrate-and-fire neurons with pulse coupling, thereby relating network inputs to evoked neuronal activity. Using this embedded mapping and experimentally feasible measurements of the firing rate …