A single column thalamocortical network model (Traub et al 2005)

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Accession:45539
To better understand population phenomena in thalamocortical neuronal ensembles, we have constructed a preliminary network model with 3,560 multicompartment neurons (containing soma, branching dendrites, and a portion of axon). Types of neurons included superficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing behaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic types of connectivity, and located in superficial and deep cortical layers; low threshold spiking (LTS) interneurons, that contacted principal cell dendrites; deep pyramids, that could have RS or intrinsic bursting (IB) firing behaviors, and endowed either with non-tufted apical dendrites or with long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis (nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were based on published data, although many arbitrary choices were necessary.
References:
1 . Traub RD, Contreras D, Cunningham MO, Murray H, LeBeau FE, Roopun A, Bibbig A, Wilent WB, Higley MJ, Whittington MA (2005) Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J Neurophysiol 93:2194-232 [PubMed]
2 . Traub RD, Contreras D, Whittington MA (2005) Combined experimental/simulation studies of cellular and network mechanisms of epileptogenesis in vitro and in vivo. J Clin Neurophysiol 22:330-42 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex; Thalamus;
Cell Type(s): Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Neocortex U1 L6 pyramidal corticalthalamic GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium; I A, slow;
Gap Junctions: Gap junctions;
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; FORTRAN;
Model Concept(s): Activity Patterns; Bursting; Temporal Pattern Generation; Oscillations; Simplified Models; Epilepsy; Sleep; Spindles;
Implementer(s): Traub, Roger D ;
Search NeuronDB for information about:  Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex U1 L6 pyramidal corticalthalamic GLU cell; GabaA; AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium; I A, slow;
Files displayed below are from the implementation
/
nrntraub
cells
dat
hoc
mod
net
README
balanal.hoc *
balcomp.hoc *
cell_templates.hoc *
clear.hoc *
finit.hoc *
fortmap.hoc *
gidcell.hoc *
gidcell.ses *
init.hoc
manage_setup.hoc
mosinit.hoc *
onecell.hoc *
onecell.ses *
prcellstate.hoc *
printcon.hoc *
savestatetest.sh
spkplt.hoc *
vclampg.hoc *
vcompclamp.hoc *
vcompsim.hoc *
                            
load_file("stdlib.hoc")
if (!name_declared("pc")) { execute1("objref pc") }
if (!name_declared("pcitr")) {execute1("iterator pcitr() {}")}
strdef csf_name_, mtname_, msname_, varval_

proc prcellstate() {local i, gid   localobj c, csf
	if (numarg() > 1) if (!pc.gid_exists($2)) return
	sprint(csf_name_, "cs%d.%d.%d", $1, pc.id, pc.nhost)
	csf = new File()
	csf.wopen(csf_name_)
	for pcitr(&j, &gid, 1) {
		if (numarg() > 1) if (gid != $2) continue
		c = pc.gid2obj(gid)
		prcellstate_(c, csf, gid)
	}
	csf.close
}

proc prcellstate_() {local i, j, k, x, size localobj mt, ms, syn
	i = 0
	mt = new MechanismType(0)
	forsec $o1.all {
		for (x, 0) {
			$o2.printf("%d %d %.17g %s.v(%g)\n", $3, i, v(x), secname(), x)
			i += 1
			$o2.printf("%d %d %.17g area(%g)\n", $3, i, area(x), x)
			i += 1
			$o2.printf("%d %d %.17g ri(%g)\n", $3, i, ri(x), x)
			i += 1
		}
		for j=1, mt.count-1 {
			mt.select(j)
		mt.selected(mtname_)
			if (ismembrane(mtname_)) {
				ms = new MechanismStandard(mtname_,0)
				for k=0, ms.count-1 {
					size = ms.name(msname_, k)
				for (x,0) {
sprint(varval_, "hoc_ac_ = %s(%g)", msname_, x)
					execute(varval_)
$o2.printf("%d %d %.17g %s(%g)\n", $3, i, hoc_ac_, msname_, x)
						i += 1
					}
				}
			}
		}
	}
	for j=0, $o1.synlist.count-1 {
		syn = $o1.synlist.object(j)
		classname(syn, mtname_)
		ms = new MechanismStandard(mtname_,0)
		ms.in(syn)
		for k=0, ms.count-1 {
			ms.name(msname_, k)
$o2.printf("%d %d %.17g %s.%s\n", $3, i, ms.get(msname_), syn, msname_)
			i += 1
		}
	}
}

proc rdcellstate() {local i, g, j, val1, val2, diff, most localobj f1, f2, l1, l2, lmost
	most = 0
	lmost = new String()
	l1 = new String() l2 = new String()
	f1 = new File() f1.ropen($s1)
	f2 = new File() f2.ropen($s2)
	for (i=0; !f1.eof; i += 1) {
		f1.gets(l1.s)  f2.gets(l2.s)
		sscanf(l1.s, "%d %d %lf", &g, &j, &val1)
		sscanf(l2.s, "%d %d %lf", &g, &j, &val2)
		if (val1 != val2) {
			diff = abs(val1-val2)/(abs(val1) + abs(val2))
			printf("%g %s\n", diff, l1.s)
			if (most < diff) {
				most = diff
				lmost.s = l1.s	
			}
		}
	}
	if (most > 0) {
		printf("most %g %s\n", most, lmost.s)
	}
	f1.close
	f2.close
}

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