Abstract
A new class of the artificial neuron models is described in this work. These models are based on assumptions: (1) contributions of synapses are summing with the help of certain aggregation operation; (2) contribution of synaptic clusters are computed with the help of another aggregation operation on the set of simple synapses. These models include a big part of known functional models of neurons. The generalization of the \(\varSigma \varPi \)-neuron model on the basis of aggregation operations is presented. Correctness of the \(\varSigma \varPi \)-neuron model of aggregating type under easily verifiable conditions is also presented.
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This work is supported by grant RFBR 15-01-03381 and by research program of ONIT RAS.
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Shibzukhov, Z., Cherednikov, D. (2016). About \(\varSigma \varPi \)-neuron Models of Aggregating Type. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_75
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