Abstract
The use of nonparametric modeling methods for the analysis of nonlinear neurophysiological systems is discussed. The obtained models are in the form of Volterra-Wiener functionals or equivalent block-structured models (whenever appropriate). The latter are more compact representations and lend themselves to easier physiological interpretation. A particular class of block-structured models, containing a threshold-trigger operator, is presented and shown to be suitable for studies of spike generating neural systems. This class of models may offer a general framework for the study of information processing in the nervous system. Illustrative examples from actual applications are presented, and the role of nonlinear models in enhancing our understanding of Some aspects of nonlinear neurophysiological function (viz., rectification, amplitude compression, nonlinear inhibition and spike encoding) is discussed.
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