Parallel network simulations with NEURON (Migliore et al 2006)

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Accession:64229
The NEURON simulation environment has been extended to support parallel network simulations. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters.
Reference:
1 . Migliore M, Cannia C, Lytton WW, Markram H, Hines ML (2006) Parallel Network Simulations with NEURON. J Comp Neurosci 21:110-119 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
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("invl.hoc")}
objref nil

proc create_cells() { local i, gid  localobj cell, nc
	for pcitr(&i, &gid) {
		pnm.set_gid2node(gid, pc.id)
		pnm.gid_exists(gid)
		cell = new IF_IntervalFire(gid*ranoffset_ + 1)
		pnm.cells.append(cell)
		cell.connect2target(nil, nc)
		pnm.pc.cell(gid, nc)
		pnm.pc.outputcell(gid)
	}
}

proc connect_cells() { local i, j, gid, r   localobj cell
	// random connections but not self
	mcell_ran4_init(connect_random_low_start_)
	for pcitr(&i, &gid) {
		cell = pnm.cells.object(i)
		cell.r.uniform(1, ncell-1) // 0 refers to "this"
		cell.ranstart()
		for j=0, ncon-1 {
			r = (cell.r.repick + gid)%ncell // can never be gid
			pnm.nc_append(r, gid, -1, 0, 1)
		}
	}
}


proc init_run_random() {local i, gid  localobj cell
	mcell_ran4_init($1)
	for pcitr(&i, &gid) {
		cell = pnm.cells.object(i)
		cell.r.uniform(10,20) //interval variation
		cell.ranstart()
	}
}

create_cells()
connect_cells()


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