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Chemical implementation and thermodynamics of collective neural networks
1Max-Planck-Institut für Biophysikalische Chemie, Göttingen, Federal Republic of Germany.
Abstract:
The chemical implementation of a neuron and connections among neurons described in prior work is used to construct collective neural networks. With stated approximations, these chemical networks are reduced to networks of the Hopfield type. Chemical networks approaching a stationary or equilibrium state provide a Liapunov function with the same extremal properties as Hopfield's energy function. Numerical comparisons of chemical and Hopfield networks with small numbers (2-16) of neurons show agreement on the results of given computations.