A two-state neuronal model with alternating exponential excitation
We develop a stochastic neural model based on point excitatory inputs. The nerve cell depolarisation is determined by a two-state point process corresponding the two states of the cell. The model presumes state-dependent excitatory stimuli amplitudes and decay rates of membrane potential. The state...
Autor Principal: | |
---|---|
Formato: | Artículo (Article) |
Lenguaje: | Inglés (English) |
Publicado: |
American Institute of Mathematical Sciences
2019
|
Materias: | |
Acceso en línea: | https://repository.urosario.edu.co/handle/10336/22559 https://doi.org/10.3934/mbe.2019171 |
id |
ir-10336-22559 |
---|---|
recordtype |
dspace |
spelling |
ir-10336-225592021-06-11T04:07:11Z A two-state neuronal model with alternating exponential excitation Ratanov, Nikita Decay (organic) Depolarization Excited states Laplace transforms Neurons Stochastic systems Time switches Asymptotical behaviour First passage time Laplace transform techniques Membrane potentials Neural activity Neural modeling Neuronal model State-dependent Stochastic models Article Depolarization Excitation Laplace transform Membrane potential Nerve cell Probability Stochastic model Asymptotical behaviour Firing probability First passage time Jump-telegraph process Neural activity We develop a stochastic neural model based on point excitatory inputs. The nerve cell depolarisation is determined by a two-state point process corresponding the two states of the cell. The model presumes state-dependent excitatory stimuli amplitudes and decay rates of membrane potential. The state switches at each stimulus time. We analyse the neural firing time distribution and the mean firing time. The limit of the firing time at a definitive scaling condition is also obtained. The results are based on an analysis of the first crossing time of the depolarisation process through the firing threshold. The Laplace transform technique is widely used. © 2019 the author. 2019 2020-05-25T23:56:55Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 15471063 15510018 https://repository.urosario.edu.co/handle/10336/22559 https://doi.org/10.3934/mbe.2019171 eng info:eu-repo/semantics/openAccess application/pdf American Institute of Mathematical Sciences instname:Universidad del Rosario reponame:Repositorio Institucional EdocUR |
institution |
EdocUR - Universidad del Rosario |
collection |
DSpace |
language |
Inglés (English) |
topic |
Decay (organic) Depolarization Excited states Laplace transforms Neurons Stochastic systems Time switches Asymptotical behaviour First passage time Laplace transform techniques Membrane potentials Neural activity Neural modeling Neuronal model State-dependent Stochastic models Article Depolarization Excitation Laplace transform Membrane potential Nerve cell Probability Stochastic model Asymptotical behaviour Firing probability First passage time Jump-telegraph process Neural activity |
spellingShingle |
Decay (organic) Depolarization Excited states Laplace transforms Neurons Stochastic systems Time switches Asymptotical behaviour First passage time Laplace transform techniques Membrane potentials Neural activity Neural modeling Neuronal model State-dependent Stochastic models Article Depolarization Excitation Laplace transform Membrane potential Nerve cell Probability Stochastic model Asymptotical behaviour Firing probability First passage time Jump-telegraph process Neural activity Ratanov, Nikita A two-state neuronal model with alternating exponential excitation |
description |
We develop a stochastic neural model based on point excitatory inputs. The nerve cell depolarisation is determined by a two-state point process corresponding the two states of the cell. The model presumes state-dependent excitatory stimuli amplitudes and decay rates of membrane potential. The state switches at each stimulus time. We analyse the neural firing time distribution and the mean firing time. The limit of the firing time at a definitive scaling condition is also obtained. The results are based on an analysis of the first crossing time of the depolarisation process through the firing threshold. The Laplace transform technique is widely used. © 2019 the author. |
format |
Artículo (Article) |
author |
Ratanov, Nikita |
author_facet |
Ratanov, Nikita |
author_sort |
Ratanov, Nikita |
title |
A two-state neuronal model with alternating exponential excitation |
title_short |
A two-state neuronal model with alternating exponential excitation |
title_full |
A two-state neuronal model with alternating exponential excitation |
title_fullStr |
A two-state neuronal model with alternating exponential excitation |
title_full_unstemmed |
A two-state neuronal model with alternating exponential excitation |
title_sort |
two-state neuronal model with alternating exponential excitation |
publisher |
American Institute of Mathematical Sciences |
publishDate |
2019 |
url |
https://repository.urosario.edu.co/handle/10336/22559 https://doi.org/10.3934/mbe.2019171 |
_version_ |
1702626598974390272 |
score |
12,131701 |