Identification of Multi-Input Biological Systems

Abstract
The Wiener theory of nonlinear system identification is extended to multi-input-output systems and experimentally applied. The experimental applicability of the method is discussed with regard to biological systems. It is shown that the method is well suited for the treatment of the idiosyncratic features of such systems: nonlinearities, short lifetimes of experimental preparations, and high noise content. A preliminary analysis is outlined, taking into account the characteristics of the system under study, which results in the determination of the parameters of the identifying experiment. An error analysis is made which can be used to increase the accuracy of the derived model within certain constraints. Several examples are given of the experimental application of the method to certain neural networks in a vertebrate retina (the catfish, Ictalurus punctatus, retina was used for these experiments). In addition to functional identification through white-noise stimulation, these same retinal neurons are identified morphologically through intracellular dye injection. The performance of the derived functional models, as compared to the physical system, is evaluated through a variety of tests and it is found to be very satisfactory.