Analog parallel matrix structures as tools for neural network investigations

Parallel resistive structures, developed for physical process modeling, and their possible application in artificial neural systems are considered. The authors presents results of a recent project on hardware for solving boundary field problems and its possible utilization in neurocomputation. The components designed in the project may be useful for experimental neural network engineering as adjustable weights in connections