Band-limited white noise stimulation and reverse correlation analysis in the prediction of impulse responses of encoder models

Abstract

A method is described to test the predictability of impulse responses from responses to Gaussiandistributed random stimulation by means of the reverse correlation analysis. In addition, this analysis is tested as to whether it can handle responses of nonlinear systems to random inputs of strongly limited frequency content, which is often the case in data from physiological experiments. The basis for all computation is a simple backward averaging (peri-spike averaging, Istorder PSA) of the noise input triggered from the output pulsatile events, which was extended to two-dimensional peri-spike averaging (2nd-order PSA). These functions were shown to represent the 1st- and 2nd-order Wiener kernel and were taken to calculate the 1st-and 2nd-order response predictions to a given short random test sequence. Different models of impulse-initiating mechanisms were tested for their expression of nonlinearities in these PSAs. Output impulse densities of test sequence (the observed response) could be fairly well approximated by the result of the computations (the predicted response). The difference between observation and prediction was evaluated and expressed as the mean-least squares error. In some of the data the 2nd-order kernel seems sufficient to account for the major nonlinear component, in others, kernels of orders higher than two.