MODELING OF NEURAL SYSTEMS BY USE OF NEURONAL MODES

A methodology for modeling spike-output neural systems from input-outp ut data is proposed, which makes use of ''neuronal modes'' (NM) and '' multi-input threshold'' (MT) operators. The modeling concept of NM's w as introduced in a previously published paper [4] in order to provide concise and general mathematical representations of the nonlinear dyna mics involved in signal transformation and coding by a class of neural systems. This paper presents and demonstrates (with computer simulati ons) a method by which the NM's are determined using the 1st- and 2nd- order kernel estimates of the system, obtained from input-output data. The MT operator (i.e., a binary operator with multiple real-valued op erands which are the outputs of the NM's) possesses an intrinsic refra ctory mechanism and generates the sequence of output spikes. The spike -generating characteristics of the MT operator are determined by the ' 'trigger regions'' defined on the basis of data. This approach is offe red as a reasonable compromise between modeling complexity and predict ion accuracy, which may provide a common methodological framework for modeling a certain class of neural systems.