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
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nrntraub
hoc
balcomp.hoc *
defvar.hoc *
lbcreate.hoc *
mscreate.hoc *
parlib.hoc
parlib2.hoc *
traubcon.hoc *
traubcon_net.hoc *
                            
// The change to a connection coefficient gets changed back to
// its value determined by diam,L,Ra,topology after any change of any
// of those properties in any section.
// However the topology change implied by the traub_exact process is persistent.
// Thus one possibility is to do the traub_exact topology change
// along with the connection coefficient setting AFTER a complete setup
// that includes gaps, synapses, and stimuli, and then let NEURON do its
// thing in response to diam_changed,
// and then change all the connection coefficients.
// Another possiblity, which perhaps is not as efficient but is
// certainly simpler, is to
// let traub_exact do its thing on the creation of each cell, which will accomplish
// the persistent topology change, and save the info regarding the
// connection coefficients, and then fill them again after the complete setup.
// We choose the latter.

// for all cells
proc reset_connection_coefficients() {local i, gid, ix  localobj cell
	if (use_traubexact) {
		// do the topology first
		for pcitr(&i, &gid) {
			cell = pc.gid2cell(gid)
			ix = cell.type
			traubexact_topology(cell, traubExactInfo.tci[ix], traubExactInfo.traub_parent[ix])
		}
		doNotify()
		// now the coefficients
		for pcitr(&i, &gid) {
			cell = pc.gid2cell(gid)
			ix = cell.type
			traubexact_coef(cell, traubExactInfo.tci[ix], traubExactInfo.traub_parent[ix])
		}
	}
}

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