The Neuroid revisited: A heuristic approach to model neural spike trains
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Research on Biomedical Engineering (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000400331 |
Resumo: | AbstractIntroduction: Since it was introduced in 2012, the Neuroid has been used to aid in understanding how functionally different neural populations contribute to sensory information processing. However, insights about whether this neuron-model could perform better than others or about when its utilization should be considered have not been provided yet. Methods In an attempt to address this issue, a comparison between the Neuroid and the leaky-integrate-and-fire (LIF) model in terms of accuracy and computational cost was performed. Both models were tested for different stimulation amplitudes and stimulation periods, with time step sizes ranging from 10-4 to 1 ms. Results It was found that, although the Neuroid was able to produce more accurate results than its original version, its accuracy was lower than the achieved with the LIF model solved by the forward Euler method. On the other hand, the Neuroid performed its calculations in an amount of time significantly lower (Mulfactorial ANOVA test, p < 0.05) than that required by the LIF model when it was solved by using the forward Euler method. Moreover, it was possible to use Neuroid-based networks to replicate biologically relevant firing patterns produced by low-scale networks composed of more detailed neuron-models. Conclusion Results suggest that the Neuroid could be an interesting choice when computational resources are limited, although its use might be restricted to a narrow band of applications. |
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The Neuroid revisited: A heuristic approach to model neural spike trainsNeuroidSpiking neuron-modelFrequency-intensity curveAccuracyComputational costHeuristicAbstractIntroduction: Since it was introduced in 2012, the Neuroid has been used to aid in understanding how functionally different neural populations contribute to sensory information processing. However, insights about whether this neuron-model could perform better than others or about when its utilization should be considered have not been provided yet. Methods In an attempt to address this issue, a comparison between the Neuroid and the leaky-integrate-and-fire (LIF) model in terms of accuracy and computational cost was performed. Both models were tested for different stimulation amplitudes and stimulation periods, with time step sizes ranging from 10-4 to 1 ms. Results It was found that, although the Neuroid was able to produce more accurate results than its original version, its accuracy was lower than the achieved with the LIF model solved by the forward Euler method. On the other hand, the Neuroid performed its calculations in an amount of time significantly lower (Mulfactorial ANOVA test, p < 0.05) than that required by the LIF model when it was solved by using the forward Euler method. Moreover, it was possible to use Neuroid-based networks to replicate biologically relevant firing patterns produced by low-scale networks composed of more detailed neuron-models. Conclusion Results suggest that the Neuroid could be an interesting choice when computational resources are limited, although its use might be restricted to a narrow band of applications.Sociedade Brasileira de Engenharia Biomédica2017-10-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000400331Research on Biomedical Engineering v.33 n.4 2017reponame:Research on Biomedical Engineering (Online)instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)instacron:SBEB10.1590/2446-4740.02617info:eu-repo/semantics/openAccessPrada,Erick Javier ArgüelloArteaga,Ignacio Antonio BuscemaMartínez,Antonio José D’Alessandroeng2018-01-09T00:00:00Zoai:scielo:S2446-47402017000400331Revistahttp://www.rbejournal.org/https://old.scielo.br/oai/scielo-oai.php||rbe@rbejournal.org2446-47402446-4732opendoar:2018-01-09T00:00Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)false |
dc.title.none.fl_str_mv |
The Neuroid revisited: A heuristic approach to model neural spike trains |
title |
The Neuroid revisited: A heuristic approach to model neural spike trains |
spellingShingle |
The Neuroid revisited: A heuristic approach to model neural spike trains Prada,Erick Javier Argüello Neuroid Spiking neuron-model Frequency-intensity curve Accuracy Computational cost Heuristic |
title_short |
The Neuroid revisited: A heuristic approach to model neural spike trains |
title_full |
The Neuroid revisited: A heuristic approach to model neural spike trains |
title_fullStr |
The Neuroid revisited: A heuristic approach to model neural spike trains |
title_full_unstemmed |
The Neuroid revisited: A heuristic approach to model neural spike trains |
title_sort |
The Neuroid revisited: A heuristic approach to model neural spike trains |
author |
Prada,Erick Javier Argüello |
author_facet |
Prada,Erick Javier Argüello Arteaga,Ignacio Antonio Buscema Martínez,Antonio José D’Alessandro |
author_role |
author |
author2 |
Arteaga,Ignacio Antonio Buscema Martínez,Antonio José D’Alessandro |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Prada,Erick Javier Argüello Arteaga,Ignacio Antonio Buscema Martínez,Antonio José D’Alessandro |
dc.subject.por.fl_str_mv |
Neuroid Spiking neuron-model Frequency-intensity curve Accuracy Computational cost Heuristic |
topic |
Neuroid Spiking neuron-model Frequency-intensity curve Accuracy Computational cost Heuristic |
description |
AbstractIntroduction: Since it was introduced in 2012, the Neuroid has been used to aid in understanding how functionally different neural populations contribute to sensory information processing. However, insights about whether this neuron-model could perform better than others or about when its utilization should be considered have not been provided yet. Methods In an attempt to address this issue, a comparison between the Neuroid and the leaky-integrate-and-fire (LIF) model in terms of accuracy and computational cost was performed. Both models were tested for different stimulation amplitudes and stimulation periods, with time step sizes ranging from 10-4 to 1 ms. Results It was found that, although the Neuroid was able to produce more accurate results than its original version, its accuracy was lower than the achieved with the LIF model solved by the forward Euler method. On the other hand, the Neuroid performed its calculations in an amount of time significantly lower (Mulfactorial ANOVA test, p < 0.05) than that required by the LIF model when it was solved by using the forward Euler method. Moreover, it was possible to use Neuroid-based networks to replicate biologically relevant firing patterns produced by low-scale networks composed of more detailed neuron-models. Conclusion Results suggest that the Neuroid could be an interesting choice when computational resources are limited, although its use might be restricted to a narrow band of applications. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-10-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000400331 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000400331 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2446-4740.02617 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Engenharia Biomédica |
publisher.none.fl_str_mv |
Sociedade Brasileira de Engenharia Biomédica |
dc.source.none.fl_str_mv |
Research on Biomedical Engineering v.33 n.4 2017 reponame:Research on Biomedical Engineering (Online) instname:Sociedade Brasileira de Engenharia Biomédica (SBEB) instacron:SBEB |
instname_str |
Sociedade Brasileira de Engenharia Biomédica (SBEB) |
instacron_str |
SBEB |
institution |
SBEB |
reponame_str |
Research on Biomedical Engineering (Online) |
collection |
Research on Biomedical Engineering (Online) |
repository.name.fl_str_mv |
Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB) |
repository.mail.fl_str_mv |
||rbe@rbejournal.org |
_version_ |
1752126288766697472 |