The authors describe an exploratory approach to the parametric modeling of dynamical (time-varying) neurophysiological data. The models use stimulus data from a window of time to predict the neuronal firing rate at the end of that window. The most successful models were feedforward three-layered networks of input, hidden, and output "nodes" connected by weights that were adjusted during a training phase by the backpropagation algorithm. The memory in these models were represented by delay lines of varying length propagating activation between the layers. Connectionist models with no memory (1 sequential node per layer) as well as zero-memory nonlinear nonconnectionist models were also tested.
Originally published in: Journal of Neurophysiology, v. 69, no. 3, March 1993, pp. 980-991.
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