How synapses can enhance sensibility of a neural network

P. R. Protachevicz; F. S. Borges; K. C. Iarosz; I. L. Caldas; M. S. Baptista; R. L. Viana; E. L. Lameu; E. E. N. Macau; A. M. Batista

doi:10.1016/j.physa.2017.11.034

arXiv · 1709.02719

How synapses can enhance sensibility of a neural network

P. R. Protachevicz·F. S. Borges·K. C. Iarosz·I. L. Caldas·M. S. Baptista·R. L. Viana·E. L. Lameu·E. E. N. Macau·A. M. Batista

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Abstract

In this work, we study the dynamic range in a neuronal network modelled by cellular automaton. We consider deterministic and non-deterministic rules to simulate electrical and chemical synapses. Chemical synapses have an intrinsic time-delay and are susceptible to parameter variations guided by learning Hebbian rules of behaviour. Our results show that chemical synapses can abruptly enhance sensibility of the neural network, a manifestation that can become even more predominant if learning rules of evolution are applied to the chemical synapses.

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P. R. Protachevicz, F. S. Borges, K. C. Iarosz, I. L. Caldas, M. S. Baptista, R. L. Viana, E. L. Lameu, E. E. N. Macau, A. M. Batista. 2017-09-08. How synapses can enhance sensibility of a neural network. https://doi.org/10.1016/j.physa.2017.11.034

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