How synapses can enhance sensibility of a neural network

P.R. Protachevicz, F.S. Borges, K Iarosz* (Corresponding Author), I.L. Caldas, M S Baptista, R.L. Viana, E.L. Lameu, E.E.N. Macau, Antonio M. Batista

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Pages (from-to)1045 - 1052
Number of pages8
JournalPhysica. A, Statistical Mechanics and its Applications
Early online date24 Nov 2017
Publication statusPublished - 15 Feb 2018

Bibliographical note

This work was possible by partial financial support from the following Brazilian government agencies: CNPq (154705/2016-0, 311467/2014-8), CAPES, Fundação Araucária , and São Paulo Research Foundation (processes FAPESP 2011/19296-1, 2015/07311-7, 2016/16148-5, 2016/23398-8, 2015/50122-0). Research supported by grant 2015/50122-0São Paulo Research Foundation (FAPESP) and DFG-IRTG 1740/2


  • plasticity
  • cellular automaton
  • dynamic range


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