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A flexible, interactive software tool for fitting the parameters of neuronal models

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a...

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Autores principales: Friedrich, Péter, Vella, Michael, Gulyás, Attila I., Freund, Tamás F., Káli, Szabolcs
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4091312/
https://www.ncbi.nlm.nih.gov/pubmed/25071540
http://dx.doi.org/10.3389/fninf.2014.00063
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author Friedrich, Péter
Vella, Michael
Gulyás, Attila I.
Freund, Tamás F.
Káli, Szabolcs
author_facet Friedrich, Péter
Vella, Michael
Gulyás, Attila I.
Freund, Tamás F.
Káli, Szabolcs
author_sort Friedrich, Péter
collection PubMed
description The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.
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spelling pubmed-40913122014-07-28 A flexible, interactive software tool for fitting the parameters of neuronal models Friedrich, Péter Vella, Michael Gulyás, Attila I. Freund, Tamás F. Káli, Szabolcs Front Neuroinform Neuroscience The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool. Frontiers Media S.A. 2014-07-10 /pmc/articles/PMC4091312/ /pubmed/25071540 http://dx.doi.org/10.3389/fninf.2014.00063 Text en Copyright © 2014 Friedrich, Vella, Gulyás, Freund and Káli. http://creativecommons.org/licenses/by/3.0/ 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
Friedrich, Péter
Vella, Michael
Gulyás, Attila I.
Freund, Tamás F.
Káli, Szabolcs
A flexible, interactive software tool for fitting the parameters of neuronal models
title A flexible, interactive software tool for fitting the parameters of neuronal models
title_full A flexible, interactive software tool for fitting the parameters of neuronal models
title_fullStr A flexible, interactive software tool for fitting the parameters of neuronal models
title_full_unstemmed A flexible, interactive software tool for fitting the parameters of neuronal models
title_short A flexible, interactive software tool for fitting the parameters of neuronal models
title_sort flexible, interactive software tool for fitting the parameters of neuronal models
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4091312/
https://www.ncbi.nlm.nih.gov/pubmed/25071540
http://dx.doi.org/10.3389/fninf.2014.00063
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