Evolving Spike Neural Network Sensors to Characterize the Alcoholic Brain Using Visually Evoked Response Potential

Abstract

The electrical activity of the brain in response to a visual stimulus can be recorded using EEG. These signals are complex spatially-distributed time series. Here we investigate if it is possible to find hidden temporal patterns in these evoked electrical signals that could characterize the alcoholic brain. We have developed a technology for evolving spike neural network (SNN) sensors for detecting such hidden patterns in time-varying signals. The evolutionary computation involves a novel chromosome structure and a hybrid crossover operator for it. We have also developed a design rule for SNN-based temporal pattern detectors (TPD) that can detect a predefined inter-spike interval pattern in an incoming spike train. The design rule eliminates the need to tune the network parameters leaving only the design specifications to be learned. The primary goal of the evolutionary process is to select a set of EEG leads along with weights and to evolve the design specifications for the TPDs. After converting the composite EEG signal to a spike train, the TPDs are evaluated based on their ability to distinguish the alcoholic and the control cases. The early results suggest that this approach may be reliably used for characterizing the alcoholic brain.