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];
load_file("nrngui.hoc")
load_file("net1.hoc")
load_file("vinit.hoc")
load_file("spkplot.hoc")

proc atolscale() {
	cvode.atolscale("GABAA.Ron", NetCon[0].weight)
	cvode.atolscale("GABAA.Roff", NetCon[0].weight)
}
atolscale()

proc setweight() {local i
	for i = 0, nclist.count-1 {
		nclist.object(i).weight = wval/(ncell - 1)
	}
}

order_ = 0

load_file("ctrl1.ses")
setfreq()
setdel()

objref sp
sp = new SpikePlot1(cells)

proc defnet() {
	sp.unmap()
	sp = nil
	makenet()
	setweight()
	atolscale()
	sp = new SpikePlot1(cells)
	ranfreq()
	ranvinit()
	randelay()
}


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