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Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times
In simulating realistic neuronal circuitry composed of diverse types of neurons, we need an elemental spiking neuron model that is capable of not only quantitatively reproducing spike times of biological neurons given in vivo-like fluctuating inputs, but also qualitatively representing a variety of...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Research Foundation
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3215233/ https://www.ncbi.nlm.nih.gov/pubmed/22203798 http://dx.doi.org/10.3389/fncom.2011.00042 |
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author | Yamauchi, Satoshi Kim, Hideaki Shinomoto, Shigeru |
author_facet | Yamauchi, Satoshi Kim, Hideaki Shinomoto, Shigeru |
author_sort | Yamauchi, Satoshi |
collection | PubMed |
description | In simulating realistic neuronal circuitry composed of diverse types of neurons, we need an elemental spiking neuron model that is capable of not only quantitatively reproducing spike times of biological neurons given in vivo-like fluctuating inputs, but also qualitatively representing a variety of firing responses to transient current inputs. Simplistic models based on leaky integrate-and-fire mechanisms have demonstrated the ability to adapt to biological neurons. In particular, the multi-timescale adaptive threshold (MAT) model reproduces and predicts precise spike times of regular-spiking, intrinsic-bursting, and fast-spiking neurons, under any fluctuating current; however, this model is incapable of reproducing such specific firing responses as inhibitory rebound spiking and resonate spiking. In this paper, we augment the MAT model by adding a voltage dependency term to the adaptive threshold so that the model can exhibit the full variety of firing responses to various transient current pulses while maintaining the high adaptability inherent in the original MAT model. Furthermore, with this addition, our model is actually able to better predict spike times. Despite the augmentation, the model has only four free parameters and is implementable in an efficient algorithm for large-scale simulation due to its linearity, serving as an element neuron model in the simulation of realistic neuronal circuitry. |
format | Online Article Text |
id | pubmed-3215233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Frontiers Research Foundation |
record_format | MEDLINE/PubMed |
spelling | pubmed-32152332011-12-27 Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times Yamauchi, Satoshi Kim, Hideaki Shinomoto, Shigeru Front Comput Neurosci Neuroscience In simulating realistic neuronal circuitry composed of diverse types of neurons, we need an elemental spiking neuron model that is capable of not only quantitatively reproducing spike times of biological neurons given in vivo-like fluctuating inputs, but also qualitatively representing a variety of firing responses to transient current inputs. Simplistic models based on leaky integrate-and-fire mechanisms have demonstrated the ability to adapt to biological neurons. In particular, the multi-timescale adaptive threshold (MAT) model reproduces and predicts precise spike times of regular-spiking, intrinsic-bursting, and fast-spiking neurons, under any fluctuating current; however, this model is incapable of reproducing such specific firing responses as inhibitory rebound spiking and resonate spiking. In this paper, we augment the MAT model by adding a voltage dependency term to the adaptive threshold so that the model can exhibit the full variety of firing responses to various transient current pulses while maintaining the high adaptability inherent in the original MAT model. Furthermore, with this addition, our model is actually able to better predict spike times. Despite the augmentation, the model has only four free parameters and is implementable in an efficient algorithm for large-scale simulation due to its linearity, serving as an element neuron model in the simulation of realistic neuronal circuitry. Frontiers Research Foundation 2011-10-04 /pmc/articles/PMC3215233/ /pubmed/22203798 http://dx.doi.org/10.3389/fncom.2011.00042 Text en Copyright © 2011 Yamauchi, Kim and Shinomoto. http://www.frontiersin.org/licenseagreement This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with. |
spellingShingle | Neuroscience Yamauchi, Satoshi Kim, Hideaki Shinomoto, Shigeru Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times |
title | Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times |
title_full | Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times |
title_fullStr | Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times |
title_full_unstemmed | Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times |
title_short | Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times |
title_sort | elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3215233/ https://www.ncbi.nlm.nih.gov/pubmed/22203798 http://dx.doi.org/10.3389/fncom.2011.00042 |
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