9409780 Conrad A multilevel neuromolecular computing architecture has been developed that provides a rich platform for evolutionary learning. The architecture comprises a network of neuron-like modules with internal dynamics modeled by cellular automata. The dynamics reflect molecular processes believed to be operative in real neurons, in particular processes connected with second messenger signals and cytoskeleton-membrane interactions. The objective is to create a repertoire of special purpose dynamic pattern processors through and evolutionary search algorithm and then to use memory manipulation algorithms to select combinations of processors from the repertoire that are capable of performing coherent pattern recognition/neurocontrol tasks. The system, as presently implemented, consists of two layers of cytoskeletal neurons and two layers of memory access neurons (called reference neurons) divided into a collection of functionally comparable sublets. Evolutionary learning can occur at the intraneuronal level through variations in the cytoskeletal structures responsible for the integration of signals in space and time or through variations in the location of elements that represent readin or readout proteins. The memory manipulation algorithms that orchestrate the repertoire of neuronal processors also use evolutionary search procedures, and are well suited for operating in an associative mode as well. The integrated system effectively employs synergies among the different levels of processing and learning to acquire pathfinding capabilities in a maze-like environment. Immediate objectives of the research include full experimental characterization of the performance capabilities of the system, widening the dynamic capabilities of neurons, studying the relation between the structure of the system and the "evolution friendliness" of the adaptive surface, utilization of the associative memory capability in combination with evolutionary learning, and study of the system in a variety of a pplication domains. Long term goals include the achievement of open ended evolutionary learning, porting evolved neurons with useful pattern processing capabilities to special silicon hardware, use of the system as an architectural paradigm for emerging molecular electronic technologies, and use of the system as a vehicle for obtaining a clearer understanding of the role of intraneuronal mechanisms in brain function. ***
