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 and A.M. Batista

Physica A: Statistical Mechanics and its Applications, 2018, vol. 492, issue C, 1045-1052

Abstract: In this work, we study the dynamic range in a neural 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. The learning rules are related to neuroplasticity that describes change to the neural connections in the brain. 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.

Keywords: Plasticity; Cellular automaton; Dynamic range (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:492:y:2018:i:c:p:1045-1052

DOI: 10.1016/j.physa.2017.11.034

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