Cell splitting in neural networks extends strong scaling (Hines et al. 2008)

 Download zip file 
Help downloading and running models
Accession:97917
Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by each subtree at each time step. Application of the cell splitting method to two published network models exhibits good runtime scaling on twice as many processors as could be effectively used with whole-cell balancing.
Reference:
1 . Hines ML, Eichner H, Schürmann F (2008) Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors. J Comput Neurosci 25:203-10 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Generic;
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];
/
splitcell
nrntraub
net
durand.hoc *
groucho.hoc
groucho_gapbld.hoc *
groucho_gapbld_mix.hoc *
network_specification_interface.hoc
serial_or_par_wrapper.hoc *
synaptic_compmap_construct.hoc *
synaptic_map_construct.hoc *
                            
// network_specification_interface.hoc
// from NetGUI[0], the GUI Network Builder

objref cells, nclist, netcon
{cells = new List() nclist = new List()}

func cell_append() {cells.append($o1) $o1.position($2,$3,$4)
	return cells.count - 1
}

func nc_append() { // srcindex, tarcelindex, synindex
	if ($3 >= 0) {
		cells.object($1).connect2target(cells.object($2).synlist.object($3),netcon)
		netcon.weight = $4  netcon.delay = $5
	} else {
		cells.object($1).connect2target(cells.object($2).pp.netcon)
		netcon.weight = $4  netcon.delay = $5
	}
nclist.append(netcon)
return nclist.count-1
}

/* instantiate network with commands like
cell_append(new deepaxax_Cell(), -80, 0, 0)
cell_append(new deepaxax_Cell(),  80, 0, 0)

nc_append(1, 0, 0, 4, 8)
nc_append(1, 0, 1, 3, 7)
nc_append(0, 1, 0, 2, 6)
nc_append(0, 1, 0, 1, 5)
first two arguments:
where 0 is the first deepaxax cell, 1 is the second.
next argument:
the synapse number 0 is an AlphaSynKinT0, 1 is an NMDA1 rcptr.
where 1,2,3,4 are weights, 5,6,7,8 are delays

other examples from NetGUI's export of a NetStim hoc file (*'s removed before /'s)
//Network instantiation

  /* NetStim_slow0 /  cell_append(new NetStim_slow_NetStim(),	-131,	 19, 0)
  /* NetStim1 /  cell_append(new NetStim_NetStim(),	-123,	 -30, 0)
  /* Cell2 /  cell_append(new Cell_Cell(),	45,	 -4, 0)
  /* NetStim_slow0 -> Cell2.PulseSyn0 /  nc_append(0,   2, 0,  0,1)
  /* NetStim1 -> Cell2.PulseSyn0 /  nc_append(1,   2, 0,  0,1)

*/

/* note: the automatically created from FORTRAN cell templates
contain the cell templates in the style that would be written
from network builder. */

// here is a template from network builder for the stimulation of cells

begintemplate S_NetStim
public pp, connect2target, x, y, z, position, is_art
objref pp
proc init() {
    pp = new NetStim(.5)
// the interval is expected to be reset in each object instance
    pp.interval = 1e+08
// this arbitrarily large number allows the poisson process
// to deliver as many spikes as would occur without restraint
// in the simulation
    pp.number = 1e8
    pp.start = 0
    pp.noise = 1 // setting for a poisson process
}
func is_art() { return 1 }
proc connect2target() { $o2 = new NetCon(pp, $o1) }
proc position(){x=$1  y=$2  z=$3}
endtemplate S_NetStim

// here is a function which sets up constant current injections into
// the somas of some cell types


objref iclamp_const_list, tmpobj
iclamp_const_list = new List()  // don't know if we will need these

proc set_const_curr_inj() { 
// arguments past are
// double cellname_base, double num_cellname, Vector curr_cellname
//        $1                    $2                   $o3

	for i=1,$2 { // loop over all the cells
	// create the IClamp_const

	// insert IClamp_const

	    cells.object($1 + i).comp[1] tmpobj = new IClamp_const(0.5)
//	    cells.object($1 + i).synlist.append(tmpobj)  // should this be on this list?

	    tmpobj.amp = $o3.x[i]
	    iclamp_const_list.append(tmpobj)
	}
}


if (pmesg) print "made it to end of network_specification_interface.hoc"

Loading data, please wait...