Stochastic model neuron without resetting of dendritic potential: Application to the olfactory system
type: Item
instance of: scholarly article
title: Stochastic model neuron without resetting of dendritic potential: Application to the olfactory system
zbMATH Open document ID: 0783.92008
DOI: 10.1007/BF00203125
author: Jean Pierre Rospars
author: Petr Lansky
published in: Biological Cybernetics
publication date: 1993-10-18T00:00:00Z
review text: A two-dimensional stochastic neuronal model is proposed and studied. In classical one-dimensional models the dendritic and axonal membrane potentials cannot be distinguished, and thus it has to be reset after firing of an action potential. The firing is identified with the first- passage time of the stochastic process simulating membrane potential. In the present model, the dendritic potential is not reset as there is no threshold imposed on it. Further, it evolves without any influence from the axonal potential. The axonal potential is described by a simplified leaky integrator model and thus it exponentially tracks the value of the dendritic potential. For the axonal potential the classical scheme is used -- the first-passage time and then reset to a fixed value. The new model can produce a sequence of first-passage times which is positively correlated. This is the main difference from the classical models in which the threshold crossings create a renewal process.
Mathematics Subject Classification ID: 92C20
Mathematics Subject Classification ID: 60K99
zbMATH DE Number: 425638
zbMATH Keywords: two-dimensional stochastic neuronal model
zbMATH Keywords: membrane potentials
zbMATH Keywords: action potential
zbMATH Keywords: first-passage time
zbMATH Keywords: dendritic potential
zbMATH Keywords: threshold
zbMATH Keywords: axonal potential
zbMATH Keywords: leaky integrator model
zbMATH Keywords: threshold crossings
Wikidata QID: Q52406692
reviewed by: Petr Lansky
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 425638