Neural Flows in Hopfield Network Approach

In most of the applications involving neural networks, the main problem consists in finding an optimal procedure to reduce the real neuron to simpler models which still express the biological complexity but allow highlighting the main characteristics of the system. We effectively investigate a simpl...

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Main Authors: Ionescu Carmen, Panaitescu Emilian, Stoicescu Mihai
Format: Article
Language:English
Published: Sciendo 2013-12-01
Series:Annals of West University of Timisoara: Physics
Subjects:
Online Access:https://doi.org/10.1515/awutp-2015-0101
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spelling doaj-d2aec893c51c4a7890be6218fab655252021-09-06T19:40:24ZengSciendoAnnals of West University of Timisoara: Physics1224-97182013-12-015711910.1515/awutp-2015-0101Neural Flows in Hopfield Network ApproachIonescu Carmen0Panaitescu Emilian1Stoicescu Mihai2Faculty of Exact Sciences, Department of Physics, University of Craiova, Str. A.I. Cuza, nr.13, 200585, Craiova, Dolj, RomaniaFaculty of Exact Sciences, Department of Physics, University of Craiova, Str. A.I. Cuza, nr.13, 200585, Craiova, Dolj, RomaniaFaculty of Exact Sciences, Department of Physics, University of Craiova, Str. A.I. Cuza, nr.13, 200585, Craiova, Dolj, RomaniaIn most of the applications involving neural networks, the main problem consists in finding an optimal procedure to reduce the real neuron to simpler models which still express the biological complexity but allow highlighting the main characteristics of the system. We effectively investigate a simple reduction procedure which leads from complex models of Hodgkin-Huxley type to very convenient binary models of Hopfield type. The reduction will allow to describe the neuron interconnections in a quite large network and to obtain information concerning its symmetry and stability. Both cases, on homogeneous voltage across the membrane and inhomogeneous voltage along the axon will be tackled out. Few numerical simulations of the neural flow based on the cable-equation will be also presented.https://doi.org/10.1515/awutp-2015-0101neural cellnonlinear dynamics
collection DOAJ
language English
format Article
sources DOAJ
author Ionescu Carmen
Panaitescu Emilian
Stoicescu Mihai
spellingShingle Ionescu Carmen
Panaitescu Emilian
Stoicescu Mihai
Neural Flows in Hopfield Network Approach
Annals of West University of Timisoara: Physics
neural cell
nonlinear dynamics
author_facet Ionescu Carmen
Panaitescu Emilian
Stoicescu Mihai
author_sort Ionescu Carmen
title Neural Flows in Hopfield Network Approach
title_short Neural Flows in Hopfield Network Approach
title_full Neural Flows in Hopfield Network Approach
title_fullStr Neural Flows in Hopfield Network Approach
title_full_unstemmed Neural Flows in Hopfield Network Approach
title_sort neural flows in hopfield network approach
publisher Sciendo
series Annals of West University of Timisoara: Physics
issn 1224-9718
publishDate 2013-12-01
description In most of the applications involving neural networks, the main problem consists in finding an optimal procedure to reduce the real neuron to simpler models which still express the biological complexity but allow highlighting the main characteristics of the system. We effectively investigate a simple reduction procedure which leads from complex models of Hodgkin-Huxley type to very convenient binary models of Hopfield type. The reduction will allow to describe the neuron interconnections in a quite large network and to obtain information concerning its symmetry and stability. Both cases, on homogeneous voltage across the membrane and inhomogeneous voltage along the axon will be tackled out. Few numerical simulations of the neural flow based on the cable-equation will be also presented.
topic neural cell
nonlinear dynamics
url https://doi.org/10.1515/awutp-2015-0101
work_keys_str_mv AT ionescucarmen neuralflowsinhopfieldnetworkapproach
AT panaitescuemilian neuralflowsinhopfieldnetworkapproach
AT stoicescumihai neuralflowsinhopfieldnetworkapproach
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