Method of pattern storage and recognition in VO2-based oscillatory neural networks

Please follow the link and read the full text of the article published in the journal Electronics. Development of neuromorphic systems based on new nanoelectronics materials and devices is of immediate interest for solving the problems of cognitive technology and cybernetics. Computational modelling of two- and three-oscillator schemes with thermally coupled VO2-switches is used to demonstrate a novel method of pattern storage and recognition in an impulse oscillator neural network (ONN) based on the high-order synchronization effect. The method allows storage of many patterns and their number depends on the number of synchronous states Ns.  The modelling demonstrates attainment of Ns of several orders both for a three-oscillator scheme Ns~650 and for a two-oscillator scheme Ns~260. A number of regularities are obtained, in particular, an optimal strength of oscillator coupling is revealed when Ns has a maximum. Algorithms of vectors storage, network training and test vector recognition are suggested, where the parameter of synchronization effectiveness is used as a degree of match. It is shown that  to reduce the ambiguity of recognition the number of coordinated in each vector should be at least by one unit less than the number of oscillators. The demonstrated результаты is a general one and it may be applied in ONNs with various mechanisms and oscillator coupling topology.

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