Fuzzy analyses of biological information processing

Neural information processing is mediated by complex inter-relationships amongst cells. Existing methods to carry out comprehensive evaluations of these interactions in constrained by properties intrinsic to neurobiological data: low mean cellular firing rates and aperiodic sequences of firing times. We have devised a technique to overcome these difficulties. Using fuzzy logic to represent cellular information processing characteristics, and artificial neural networks (ANN´s) as an analytic framework. We examined the functional dynamics of multi-unit spike trains. It was found that a fuzzy analog of a neural spike train enhances the opportunities to detect complex cellular interactions and together with ANN´s provides a powerful environment within which to study neural information processing