Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit

We simulated the neuronal electrical activity using the Hodgkin-Huxley model (HH) and a superconductor circuit, containing Josephson junctions. These HH model make possible simulate the main neuronal dynamics characteristics such as action potentials, firing threshold and refractory period. The purp...

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Autores:
Diaz M, Jose A
Téquita, Oscar
Naranjo, Fernando
Tipo de recurso:
Fecha de publicación:
2016
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
eng
OAI Identifier:
oai:repository.eafit.edu.co:10784/11285
Acceso en línea:
http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/3035
http://hdl.handle.net/10784/11285
Palabra clave:
Hodgkin-Huxley model
Josephson Junction
Lyapunov Method
método modelo de Hodgkin-Huxley
uniones Josephson
funciones de Lyapunov
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License
Copyright (c) 2016 Ingeniería y Ciencia | ing.cienc.
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spelling 2016-02-222017-04-03T16:10:26Z2016-02-222017-04-03T16:10:26Z2256-43141794–9165http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/3035http://hdl.handle.net/10784/1128510.17230/ingciencia.12.23.5We simulated the neuronal electrical activity using the Hodgkin-Huxley model (HH) and a superconductor circuit, containing Josephson junctions. These HH model make possible simulate the main neuronal dynamics characteristics such as action potentials, firing threshold and refractory period. The purpose of the manuscript is show a method to syncronize a RCLshunted Josephson junction to a neuronal dynamics represented by the HH model. Thus the RCLSJ circuit is able to mimics the behavior of the HH neuron. We controlated the RCLSJ circuit, using and improved adaptative track scheme, that with the improved Lyapunov functions and the two controllable gain coefficients allowing synchronization of two neuronal models. Results will provide the path to follow forward the understanding neuronal networks synchronization about, generating the intrinsic brain behavior.Simulamos la actividad eléctrica neuronal mediante el modelo de Hodgkin-Huxley (HH) y un circuito superconductor, que contiene uniones Josephson. El modelo HH simulan las características principales de la dinámica neuronal tales como potenciales de acción, umbrales de disparo y el períodos refractarios. El propósito del manuscrito es mostrar un método para sincronizar un circuito con union Josephson RCLSJ a una dinámica neuronal representado por el modelo HH. Así, el circuito RCLSJ es capaz de imitar el comportamiento de la neurona HH. Controlamos el circuito RCLSJ, utilizando un esquema de control adaptativo, que con funciones de Lyapunov y dos coeficientes de ganancia controlables nos permiten la sincronización de los dos modelos neuronales. Los resultados proporcionan una ruta a seguir adelante en el entendimiento de la sincronización de redes neuronales, generadas por el comportamiento intrinseco del cerebro.application/pdfengUniversidad EAFIThttp://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/3035Copyright (c) 2016 Ingeniería y Ciencia | ing.cienc.http://creativecommons.org/licenses/by/4.0Acceso abiertohttp://purl.org/coar/access_right/c_abf2instname:Universidad EAFITreponame:Repositorio Institucional Universidad EAFITIngeniería y Ciencia | ing.cienc.; Vol 12, No 23 (2016); 93-106Ingeniería y Ciencia | ing.cienc.; Vol 12, No 23 (2016); 93-106Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ CircuitSincronización de la actividad eléctrica neuronal, utilizando el modelo de Hodgkin-Huxley y el circuito RCLSJinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionarticlepublishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Hodgkin-Huxley modelJosephson JunctionLyapunov Methodmétodo modelo de Hodgkin-Huxleyuniones Josephsonfunciones de LyapunovDiaz M, Jose A47699ce0-c331-40d9-ac1a-22606c48bf3e-1Téquita, Oscarc7867a00-e510-4125-9cc5-60fd2c7e5cd7-1Naranjo, Fernandod568c275-d620-40a6-a3da-9dba482539d5-1Ingeniería y Ciencia122393106ing.ciencORIGINALdocument (33).pdfdocument (33).pdfTexto completo PDFapplication/pdf720017https://repository.eafit.edu.co/bitstreams/1d7325f7-2266-41d4-94bc-ea7442806965/downloadcd015eb738392127ef356a467c9f44a4MD52articulo.htmlarticulo.htmlTexto completo HTMLtext/html374https://repository.eafit.edu.co/bitstreams/71e0c8a6-721e-4d68-b82c-8b72202115a1/downloadcd9587fb20a77730354bdb81e62d38afMD54THUMBNAILminaitura-ig_Mesa de trabajo 1.jpgminaitura-ig_Mesa de trabajo 1.jpgimage/jpeg265796https://repository.eafit.edu.co/bitstreams/b6e6844f-5134-4f99-9c27-76caa64c3099/downloadda9b21a5c7e00c7f1127cef8e97035e0MD5310784/11285oai:repository.eafit.edu.co:10784/112852024-12-04 11:48:27.367open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co
dc.title.eng.fl_str_mv Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit
dc.title.spa.fl_str_mv Sincronización de la actividad eléctrica neuronal, utilizando el modelo de Hodgkin-Huxley y el circuito RCLSJ
title Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit
spellingShingle Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit
Hodgkin-Huxley model
Josephson Junction
Lyapunov Method
método modelo de Hodgkin-Huxley
uniones Josephson
funciones de Lyapunov
title_short Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit
title_full Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit
title_fullStr Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit
title_full_unstemmed Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit
title_sort Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit
dc.creator.fl_str_mv Diaz M, Jose A
Téquita, Oscar
Naranjo, Fernando
dc.contributor.author.none.fl_str_mv Diaz M, Jose A
Téquita, Oscar
Naranjo, Fernando
dc.subject.keyword.eng.fl_str_mv Hodgkin-Huxley model
Josephson Junction
Lyapunov Method
topic Hodgkin-Huxley model
Josephson Junction
Lyapunov Method
método modelo de Hodgkin-Huxley
uniones Josephson
funciones de Lyapunov
dc.subject.keyword.spa.fl_str_mv método modelo de Hodgkin-Huxley
uniones Josephson
funciones de Lyapunov
description We simulated the neuronal electrical activity using the Hodgkin-Huxley model (HH) and a superconductor circuit, containing Josephson junctions. These HH model make possible simulate the main neuronal dynamics characteristics such as action potentials, firing threshold and refractory period. The purpose of the manuscript is show a method to syncronize a RCLshunted Josephson junction to a neuronal dynamics represented by the HH model. Thus the RCLSJ circuit is able to mimics the behavior of the HH neuron. We controlated the RCLSJ circuit, using and improved adaptative track scheme, that with the improved Lyapunov functions and the two controllable gain coefficients allowing synchronization of two neuronal models. Results will provide the path to follow forward the understanding neuronal networks synchronization about, generating the intrinsic brain behavior.
publishDate 2016
dc.date.issued.none.fl_str_mv 2016-02-22
dc.date.available.none.fl_str_mv 2017-04-03T16:10:26Z
dc.date.accessioned.none.fl_str_mv 2017-04-03T16:10:26Z
dc.date.none.fl_str_mv 2016-02-22
dc.type.eng.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
article
publishedVersion
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http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.local.spa.fl_str_mv Artículo
status_str publishedVersion
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1794–9165
dc.identifier.uri.none.fl_str_mv http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/3035
http://hdl.handle.net/10784/11285
dc.identifier.doi.none.fl_str_mv 10.17230/ingciencia.12.23.5
identifier_str_mv 2256-4314
1794–9165
10.17230/ingciencia.12.23.5
url http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/3035
http://hdl.handle.net/10784/11285
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.isversionof.none.fl_str_mv http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/3035
dc.rights.spa.fl_str_mv Copyright (c) 2016 Ingeniería y Ciencia | ing.cienc.
http://creativecommons.org/licenses/by/4.0
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.local.spa.fl_str_mv Acceso abierto
rights_invalid_str_mv Copyright (c) 2016 Ingeniería y Ciencia | ing.cienc.
http://creativecommons.org/licenses/by/4.0
Acceso abierto
http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad EAFIT
dc.source.none.fl_str_mv instname:Universidad EAFIT
reponame:Repositorio Institucional Universidad EAFIT
dc.source.eng.fl_str_mv Ingeniería y Ciencia | ing.cienc.; Vol 12, No 23 (2016); 93-106
dc.source.spa.fl_str_mv Ingeniería y Ciencia | ing.cienc.; Vol 12, No 23 (2016); 93-106
instname_str Universidad EAFIT
institution Universidad EAFIT
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