How synapses can enhance sensibility of a neural network

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

doi:10.1016/j.physa.2017.11.034

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 journal › Article › peer-review

11 Downloads (Pure)

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.

Original language	English
Pages (from-to)	1045 - 1052
Number of pages	8
Journal	Physica. A, Statistical Mechanics and its Applications
Volume	492
Early online date	24 Nov 2017
DOIs	https://doi.org/10.1016/j.physa.2017.11.034
Publication status	Published - 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

Keywords

plasticity
cellular automaton
dynamic range

Access to Document

10.1016/j.physa.2017.11.034Licence: Unspecified

Accepted Manuscript
© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Accepted author manuscript, 1.09 MBLicence: CC BY-NC-ND

Cite this

@article{38ac9bc392254cc097a0202dc9e4d4f0,

title = "How synapses can enhance sensibility of a neural network",

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",

author = "P.R. Protachevicz and F.S. Borges and K Iarosz and I.L. Caldas and Baptista, {M S} and R.L. Viana and E.L. Lameu and E.E.N. Macau and Batista, {Antonio M.}",

note = "This work was possible by partial financial support from the following Brazilian government agencies: CNPq (154705/2016-0, 311467/2014-8), CAPES, Funda{\c c}{\~a}o Arauc{\'a}ria , and S{\~a}o Paulo Research Foundation (processes FAPESP 2011/19296-1, 2015/07311-7, 2016/16148-5, 2016/23398-8, 2015/50122-0). Research supported by grant 2015/50122-0S{\~a}o Paulo Research Foundation (FAPESP) and DFG-IRTG 1740/2 ",

year = "2018",

month = feb,

day = "15",

doi = "10.1016/j.physa.2017.11.034",

language = "English",

volume = "492",

pages = "1045 -- 1052",

journal = "Physica. A, Statistical Mechanics and its Applications",

issn = "0378-4371",

publisher = "Elsevier",

}

TY - JOUR

T1 - How synapses can enhance sensibility of a neural network

AU - Protachevicz, P.R.

AU - Borges, F.S.

AU - Iarosz, K

AU - Caldas, I.L.

AU - Baptista, M S

AU - Viana, R.L.

AU - Lameu, E.L.

AU - Macau, E.E.N.

AU - Batista, Antonio M.

N1 - 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

PY - 2018/2/15

Y1 - 2018/2/15

N2 - 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.

AB - 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.

KW - plasticity

KW - cellular automaton

KW - dynamic range

U2 - 10.1016/j.physa.2017.11.034

DO - 10.1016/j.physa.2017.11.034

M3 - Article

SN - 0378-4371

VL - 492

SP - 1045

EP - 1052

JO - Physica. A, Statistical Mechanics and its Applications

JF - Physica. A, Statistical Mechanics and its Applications

ER -

How synapses can enhance sensibility of a neural network

Abstract

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this