Local variable time step method (Lytton, Hines 2005)

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The local variable time-step method utilizes separate variable step integrators for individual neurons in the network. It is most suitable for medium size networks in which average synaptic input intervals to a single cell are much greater than a fixed step dt.
1 . Lytton WW, Hines ML (2005) Independent variable time-step integration of individual neurons for network simulations. Neural Comput 17:903-21 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Simulation Environment: NEURON;
Model Concept(s): Methods;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu];
"Independent variable timestep integration of individual neurons for
network simulations" is a methods paper which demonstrates that
the local variable step method has better performance than the fixed
step method for networks in which only a few cells are active at a time.

The files reproduce fig 1 and 3 and provide a test framework for
observing performance for the worst case Wang and Buzsaki model
and the best case Ring model.

Michael.Hines@yale.edu should be contacted for comments/questions.