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Stochastic Methods in Neuroscience$
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Carlo Laing and Gabriel J Lord

Print publication date: 2009

Print ISBN-13: 9780199235070

Published to Oxford Scholarship Online: February 2010

DOI: 10.1093/acprof:oso/9780199235070.001.0001

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Stochastic Simulation of Neurons, Axons, and Action Potentials

Stochastic Simulation of Neurons, Axons, and Action Potentials

Chapter:
(p.297) 11 Stochastic Simulation of Neurons, Axons, and Action Potentials
Source:
Stochastic Methods in Neuroscience
Author(s):

A. Aldo Faisal

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199235070.003.0011

Variability is inherent in neurons. To account for variability we have to make use of stochastic models. We will take a look at this biologically more rigorous approach by studying the fundamental signal of our brain’s neurons: the action potential and the voltage-gated ion channels mediating it. We will discuss how to model and simulate the action potential stochastically. We review the methods and show that classic stochastic approximation methods fail at capturing important properties of the highly nonlinear action potential mechanism, making the use of accurate models and simulation methods essential for understanding the neural code. We will review what stochastic modelling has taught us about the function, structure, and limits of action potential signalling in neurons, the most surprising insight being that stochastic effects of individual signalling molecules become relevant for whole-cell behaviour. We suggest that most of the experimentally observed neuronal variability can be explained from the bottom-up as generated by molecular sources of thermodynamic noise.

Keywords:   action potential, spike, axon, nerve fibre, stochastic simulation, noise, voltage-gated ion channel, Na channel, limits, spike time reliability, neuronal variability

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