Corticonics, echoing electronics, is the art of abstracting salient features of cortical organization for use in the modeling and numerical simulation of the cortex, which carries out higher level brain functions. Current neural and connectionist models of the cortex have not been effective in duplicating higher-level brain function, especially the ability to process dynamic input patterns. Using mathematics quite different from that used in conventional networks, University of Pennsylvania faculty have created a novel approach to cortex modeling that combines concepts and tools from nonlinear dynamics and information theory offering a radically new way to process, classify/learn, and recognize spatio-temporal signals. Their parametrically coupled logistic map nets (PCLMN) yield remarkably specific behaviors that have no parallel in sigmoidal neural network, coupled map lattices, and cellular automata. The use of PCLMNs and corticonics thus provide a unique tool for the development of intelligent systems that can operate in a natural environment where time varying signatures are the norm and not the exception.

File Number: N2420