Modularity in neural computing

This paper considers neural computing models for information processing in terms of collections of subnetwork modules. Two approaches to generating su ch networks are studied. The first approach includes networks with function ally independent subnetworks,, where each subnetwork is designed to have sp ecific functions, communication, and adaptation characteristics. The second a,approach is based an algorithms that cart actually generate network and subnetwork, topologies, connections, and weights to satisfy specific constr aints. Associated algorithms to attain these goals include evolutionary com putation and self-organizing maps. We argue that this modular approach to n eural computing is more in line with the neurophysiology of the vertebrate cerebral ail colter, 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 simpl y defined artificial neural networks.