HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Lytton, W. W. Articles by Hines, M. L. Search for Related Content PubMed PubMed Citation Articles by Lytton, W. W. Articles by Hines, M. L.

Independent Variable Time-Step Integration of Individual Neurons for Network Simulations

billl{at}neurosim.downstate.edu, Department of Physiology, Pharmacology, and Neurology, State University of New York, Downstate, Brooklyn, NY 11203-2098, U.S.A.

Michael L. Hines

michael.hines{at}yale.edu, Department of Computer Science, Yale University, New Haven, CT 06520-8001, U.S.A.

Realistic neural networks involve the coexistence of stiff, coupled, continuous differential equations arising from the integrations of individual neurons, with the discrete events with delays used for modeling synaptic connections. We present here an integration method, the local variable time-step method (lvardt), that uses separate variable-step integrators for individual neurons in the network. Cells that are undergoing excitation tend to have small time steps, and cells that are at rest with little synaptic input tend to have large time steps. A synaptic input to a cell causes reinitialization of only that cell's integrator without affecting the integration of other cells. We illustrated the use of lvardt on three models: a worst-case synchronizing mutual-inhibition model, a best-case synfire chain model, and a more realistic thalamocortical network model.

This article has been cited by other articles:

				E. Ros, R. Carrillo, E. M. Ortigosa, B. Barbour, and R. Agis Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics. Neural Comput., December 1, 2006; 18(12): 2959 - 2993. [Abstract] [Full Text] [PDF]

				M. Rudolph and A. Destexhe Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for Event-Driven Simulation Strategies. Neural Comput., September 1, 2006; 18(9): 2146 - 2210. [Abstract] [Full Text] [PDF]

				R. Brette Exact Simulation of Integrate-and-Fire Models with Synaptic Conductances Neural Comput., August 1, 2006; 18(8): 2004 - 2027. [Abstract] [Full Text] [PDF]

				A. Morrison, S. Straube, H. E. Plesser, and M. Diesmann Exact Subthreshold Integration with Continuous Spike Times in Discrete-Time Neural Network Simulations Neural Comput., January 1, 2006; 19(1): 47 - 79. [Abstract] [Full Text] [PDF]