@Article{ProtacheviczBICBVLMB:2018:HoSyCa,
author = "Protachevicz, P. R. and Borges, F. S. and Iarosz, K. C. and
Caldas, I. L. and Baptista, M. S. and Viana, R. L. and Lameu,
Ewandson Luiz and Macau, Elbert Einstein Nehrer and Batista, A.
M.",
affiliation = "{Universidade Estadual de Ponta Grossa (UEPG)} and {Universidade
de S{\~a}o Paulo (USP)} and {Universidade de S{\~a}o Paulo
(USP)} and {Universidade de S{\~a}o Paulo (USP)} and {Institute
for Complex Systems and Mathematical Biology} and {Universidade
Federal do Paran{\'a} (UFPR)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Universidade Estadual de Ponta Grossa
(UEPG)}",
title = "How synapses can enhance sensibility of a neural network",
journal = "Physica A: Statistical Mechanics and its Applications",
year = "2018",
volume = "492",
pages = "1045--1052",
month = "Feb.",
keywords = "Plasticity, Cellular automaton, Dynamic range.",
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.",
doi = "10.1016/j.physa.2017.11.034",
url = "http://dx.doi.org/10.1016/j.physa.2017.11.034",
issn = "0378-4371",
language = "en",
targetfile = "protachevicz_how.pdf",
urlaccessdate = "27 abr. 2024"
}