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]
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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):
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];
objref g, f, rings, cell, cells, time, times, meth
g = new Graph()
f = new File()
rings = new Vector()
meth = new Vector()
cells = new List()
times = new List()

proc rd() {local n, i, nrold, nr, nc, tt, me
	f.ropen($s1)
	n = f.scanvar()
print n
	nrold = -1
	for i=0, n-1 {
		me = f.scanvar()
		nr = f.scanvar()
		nc = f.scanvar()*nr
		tt = f.scanvar()
		if (nrold != nr) {
			rings.append(nr)
			meth.append(me)
			cell = new Vector()
			cells.append(cell)
			time = new Vector()
			times.append(time)
			nrold = nr
		}
		cell.append(nc)
		time.append(tt)
	}
	f.close
}

rd("rings10.dat")
//rd("rings1.dat")

proc pl() {local i
	for i=0, cells.count-1 {
		if (times.object(i).size > 1) {
times.object(i).c.log10.line(g, cells.object(i).c.log10, 3-meth.x[i], 1)
		}else{
times.object(i).c.log10.mark(g, cells.object(i).c.log10, "O", 3-meth.x[i], 1)
		}
	}
}

pl()