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...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/22559
- Acceso en línea:
- https://doi.org/10.3934/mbe.2019171
https://repository.urosario.edu.co/handle/10336/22559
- Palabra clave:
- 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
- Rights
- License
- Abierto (Texto Completo)
Summary: | 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. |
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