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The double queue method: a numerical method for integrate-and-fire neuron networks

delete2001-07-01
delete17
PRE
AI
G
Geehyuk Lee
N
Nabil H. Farhat *
DOI：10.1016/S0893-6080(01)00034-Xdelete
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Abstract

Abstract

En 中文
Numerical methods for initial-value problems based on finite-differencing of differential equations (FDM) are not well suited for the simulation of an integrate-and-fire neuron network (IFNN) due to the discontinuities implied by the firing condition of the neurons. The Double Queue Method (DQM) is an event-queue based numerical method designed for the simulation of an IFNN that can deal with such discontinuities properly. In the DQM, the states of individual neurons at the next predicted discontinuous points are determined by an analytic solution, meaning an optimal performance in both accuracy and speed. A comparison study with the FDM demonstrates the superiority of the DQM, and provides some examples where the FDM gives inaccurate results that can possibly lead to a false conclusion about the dynamics of an IFNN. (C) 2001 Elsevier Science Ltd. All rights reserved.
Keywords:
integrate-and-fire neuron network
double queue method
finite difference method
synchronization
propagation of firings
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Journal

Neural Networks cover
Neural Networks
IF:
6.3
Papers:
7.8K
Citations:
3.0W

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