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Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data

Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is...

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Detalles Bibliográficos
Autores principales: Lynch, Eoin P., Houghton, Conor J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4403314/
https://www.ncbi.nlm.nih.gov/pubmed/25941485
http://dx.doi.org/10.3389/fninf.2015.00010
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author Lynch, Eoin P.
Houghton, Conor J.
author_facet Lynch, Eoin P.
Houghton, Conor J.
author_sort Lynch, Eoin P.
collection PubMed
description Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.
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spelling pubmed-44033142015-05-04 Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data Lynch, Eoin P. Houghton, Conor J. Front Neuroinform Neuroscience Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model. Frontiers Media S.A. 2015-04-20 /pmc/articles/PMC4403314/ /pubmed/25941485 http://dx.doi.org/10.3389/fninf.2015.00010 Text en Copyright © 2015 Lynch and Houghton. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Lynch, Eoin P.
Houghton, Conor J.
Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data
title Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data
title_full Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data
title_fullStr Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data
title_full_unstemmed Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data
title_short Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data
title_sort parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4403314/
https://www.ncbi.nlm.nih.gov/pubmed/25941485
http://dx.doi.org/10.3389/fninf.2015.00010
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