Local variable time step method (Lytton, Hines 2005)

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Accession:33975
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.
Reference:
1 . Lytton WW, Hines ML (2005) Independent variable time-step integration of individual neurons for network simulations. Neural Comput 17:903-21 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Methods;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu];
objectvar save_window_, rvp_
objectvar scene_vector_[6]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}

//Begin MulRunFitter[0]
{
load_file("mulfit.hoc", "MulRunFitter")
}
{
ocbox_ = new MulRunFitter(1)
}
{object_push(ocbox_)}
{
version(5)
ranfac = 2
fspec = new File("fitgabaa-5atol.ses.ft1")
fdat = new File("fitgabaa-5atol.ses.fd1")
read_data()
build()
}
{object_pop()}
{
ocbox_.map("MulRunFitter[0]", 457, 53, 360.96, 199.68)
}
objref ocbox_
//End MulRunFitter[0]

objectvar scene_vector_[1]
{doNotify()}

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