A novel reservoir network of asynchronous cellular automaton based neurons for MIMO neural system reproduction

Modeling and implementation of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of the high nonlinearity, the traditional modeling and implementation approaches have difficulties in terms of generalization ability (i.e., performance on reproducing an unknown data) and computational resources. To overcome these difficulties, asynchronous cellular automaton based neuron models has been presented, which are neuron models described as special kinds of cellular automata and can be implemented as small asynchronous sequential logic circuits. This paper presents a novel network of such models, which can mimic input-output relationships of biological and nonlinear ODE model neural networks. Computer simulations confirm that the presented network has a higher generalization ability than another modeling and implementation approach. In addition, brief comparisons of the computational resources for execution and learning shows that the presented network requires less computational resources.

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