Stochastic model neuron without resetting of dendritic potential: Application to the olfactory system
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.
two-dimensional stochastic neuronal model
scientific article; zbMATH DE number 425638
scientific article