Modeling single neuron behavior using stochastic differential equations

Authors:
Antti Saarinen;Marja-Leena Linne;Olli Yli-Harja
Affiliations:
Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland
Venue:
Neurocomputing
Year:
2006

Citing 2
Cited 2

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An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations

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On the noise-enhancing ability of stochastic hodgkin-huxley neuron systems

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Real-time simulation of biologically realistic stochastic neurons in VLSI

IEEE Transactions on Neural Networks

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Abstract

We model the intrinsic dynamic behavior of a neuron using stochastic differential equations and Brownian motion. Basis of our work is the deterministic one-compartmental multi-conductance model of cerebellar granule cell. We develop a novel modeling approach for our test neuron by incorporating the stochasticity inherently present in the operation of voltage-dependent ion channels. Our new stochastic Hodgkin-Huxley type of model is able to reproduce a large range of dynamics more realistically than previous deterministic models for the granule cell. Proper inclusion of stochastic elements is therefore essential in modeling the behavior of single small neuron.