Fully Implicit Parallel Simulation of Single Neurons (Hines et al. 2008)

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Accession:97985
A 3-d reconstructed neuron model can be simulated in parallel on a dozen or so processors and experience almost linear speedup. Network models can be simulated when there are more processors than cells.
Reference:
1 . Hines ML, Markram H, Schuermann F (2008) Fully Implicit Parallel Simulation of Single Neurons J Comp Neurosci 25:439-448 [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):
objref trajectories
trajectories = new List()

proc add_trajec() {local ix  localobj vv, tvec, name, tstr
	ix = -2
	if (numarg() == 2) {
		if (section_exists($s1, $2)) {
			ix = $2
		}
	} else if (section_exists($s1)) {
		ix = -1
	}
	if (ix != -2) {
		name = new String($s1)
		if (ix >= 0) {
			sprint(name.s, "%s[%d]", $s1, ix)
		}else{
			name.s = $s1
			ix = 0
		}
		push_section(name.s)
		tvec = new Vector()
		tvec.record(&t)
		vv = new Vector()
		vv.record(&v(.5))
		pop_section()
		sprint(name.s, "trajec_%s_%d.dat", $s1, ix)
		trajectories.append(name)
		trajectories.append(tvec)
		trajectories.append(vv)
	}
}

proc print_trajec() {local i, j  localobj f, name, tvec, vvec
	for i=0, trajectories.count - 1 {
		name = trajectories.object(i)
		tvec = trajectories.object(i += 1)
		vvec = trajectories.object(i += 1)
		f = new File()
		f.wopen(name.s)
		for j=0, tvec.size-1 {
			f.printf("%g %g\n", tvec.x[j], vvec.x[j])
		}
		f.close()
	}
}


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