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Journal of Vision
We describe an information-theoretic framework for fitting neural spike responses with a Linear-Nonlinear-Poisson cascade model. This framework unifies the spike-triggered average and spike-triggered covariance approaches to neural characterization, and recovers a set of linear filters that maximize mean and variance-dependent information between stimuli and spike responses. The resulting approach has several useful properties: (1) it recovers a set of linear filters sorted according to theirdoi:10.1167/6.4.9 pmid:16889478 fatcat:52eiednkira2tno2e3xj7tfe4m