An Evolutionary Credit Apportionment Algorithm for Neuromolecular Computer Design

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The goal of this research is to apply a newly developed evolutionary learning algorithm to neuromolecular computer design. The distinguishing feature of neuromolecular networks is that the input-output behavior of the neuron-like modules is controlled by significant intraneuronal dynamics, including macromolecular recognition mechanics and transduction of input signals to a form that can be recognized by marcomolecules. The working hypothesis is that significant biological information processes are mediated by systems of this type; moreover, the rapid development of molecular electronics technologies should in the future allow for bona fide molecular implementations. A simulation system that can be used for designing such systems and that also has artificial intelligence applicability in the context of existing silicon technologies has been developed. During the past year an advanced evolutionary learning algorithm that utilizes a memory manipulation capability to assign credit to individual neurons in a network has been also been developed. Credit apportionment is based on input control and reward sharing in a population of virtual networks. The algorithm has been tuned on connectionist neural networks and has been tested successfully on neuromolecular networks with relatively simple intraneuronal dynamics. Cellular automaton dynamics will be used to represent signal processing in the cytoskeleton, and the credit apportionment algorithm will be used to train networks of "cytoskeletal neurons". A key objective is to build " evolution friendliness" into the intraneuronal and network dynamics on which evolutionary algorithms act by simulating the type of structure-function relations that enable natural biological systems to learn efficiently through evolution. High dimensionality is critical since it increases the likelihood of finding easily traversible pathways between functionally useful systems. The study will focus on the effects of increasing the intraneuronal cellular automaton dynamics in neuromolecular networks and memory- manipulation algorithms capable of orchestrating special purpose cytoskeletal neurons into coherent groups capable of performing complex pattern recognition/effector control tasks.