A biosensor for detecting changes in cognitive processing based on nonlinear systems analysis.

A new type of biosensor, based on hippocampal slices cultured on multielectrode arrays, and using nonlinear systems analysis for the detection and classification of agents interfering with cognitive function is described. A new method for calculating first and second order kernel was applied for impulse input-spike output datasets and results are presented to show the reliability of the estimations of this parameter. We further decomposed second order kernels as a sum of nine exponentially decaying Laguerre base functions. The data indicate that the method also reliably estimates these nine parameters. Thus, the state of the system can now be described with a set of ten parameters (first order kernel plus nine coefficients of Laguerre base functions) that can be used for detection and classification purposes.