Genetic Generation of Connection Patterns for a Dynamic Artificial Neural Network

Work-in-progress on the use of a specialized genetic algorithm for training a new type of dynamic artificial neural network is described. The network architecture is completely specified by a list of addresses that are used to connect signal sources to specific artificial synapses, which have both a temporal and spatial significance. The number of different connection patterns is a combinational problem which grows factorially as the number of artificial synapses in the network and the number of sensor elements increases. The network is implemented primarily in analog electronic hardware and constructed from artificial dendritic trees which exhibit a spatiotemporal processing capability that is modeled after morphologically complex biological neurons. We describe work-in-progress on using the specialized genetic algorithm, which has an embedded optimizer in place of the standard mutation operator, for training a dynamic neural network to follow the position of a target moving across an image sensor array.