Modularity in Neural Computing

This paper considers neural computing models for information processing in terms of collections of subnetwork modules. Two approaches to generating such networks are studied. The first approach includes networks with functionally independent subnetworks, where each subnetwork is designed to have specific functions, communication, and adaptation characteristics. The second approach is based on algorithms that can actually generate network and subnetwork topologies, connections, and weights to satisfy specific constraints. Associated algorithms to attain these goals include evolutionary computation and selforganizing maps. We argue that this modular approach to neural computing is more in line with the neurophysiology of the vertebrate cerebral cortex, particularly with respect to sensation and perception. We also argue that this approach has the potential to aid in solutions to large-scale network computational problems—an identified weakness of simply defined artificial neural networks.