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 *
                            
objref vmat, vtrajeclist
proc vcompsim() { local i, j, numcomp  localobj vv, vn, vf, f, s
	s = new String()
	vtrajeclist = new List()
	vn = vtrajeclist

	f = new File()
	numcomp=0 forsec cell.all numcomp += 1
	vmat = new Matrix(10*100-1, numcomp+1)
	classname(cell, s.s)
	sprint(s.s, "../p2c/state/%s.dat", s.s)
	f.ropen(s.s)
	vmat.scanf(f, vmat.nrow, vmat.ncol)

	vf = vmat
	i = 1
	tsyn = new Vector()
	cell.comp[1] {tsyn.record(&t)}
	for i=1, vf.ncol-1 cell.comp[i] {
		vv = new Vector()
		vv.record(&v(.5))
		vn.append(vv)
	}
	stdinit()
	continuerun(100)
	seev(2)
}
proc seev() {localobj gf, gn, s
   s = new String()
   gg.erase_all()
   seewhich = $1
   gf = vmat
   gn = vtrajeclist
   if (seewhich > gn.count) {seewhich = gn.count-1}
   if (seewhich < 1) { seewhich = 1 }
   cell.comp[seewhich] { sprint(s.s,"%s.v(.5)", secname()) }
   gg.label(.5,.8,s.s,2,1,0,0,1)
   gf.getcol(seewhich).line(gg, gf.getcol(0), 2, 1)
   gn.object(seewhich-1).line(gg, tsyn) 
}	

proc mkseev() {
	xpanel("compare compartment voltages")
	xvalue("which", "seewhich", 1, "seev(seewhich)")
	xpanel()
}




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