Configuring silicon neural networks using genetic algorithms

Abstract

There are various neuron models which can be used to emulate the neural networks responsible for cortical and spinal processes. One example is the Central Pattern Generator (CPG) networks, which are spinal neural circuits responsible for controlling the timing of periodic systems in vertebrates. In order to model the CPG effectively, it is necessary to model not just multiple individual neurons, but also the interactions between them. Due to the complexity of these types of systems, CPG models typically require large numbers (> 10) of parameters making them difficult to understand and control. Genetic Algorithms (GAs) provide a means for optimizing systems with many parameters. We present an automated method that uses a GA to And sets of parameters for a silicon implementation of a neural network capable of producing CPG type signals. This methodology can be used to configure large silicon neural circuits. In this work, constructed networks involving an 18-parameter space, can be used for controlling legged robots and neuroprosthetic devices.