Parametric computation and persistent gamma in a cortical model (Chambers et al. 2012)

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Accession:144579
Using the Traub et al (2005) model of the cortex we determined how 33 synaptic strength parameters control gamma oscillations. We used fractional factorial design to reduce the number of runs required to 4096. We found an expected multiplicative interaction between parameters.
Reference:
1 . Chambers JD, Bethwaite B, Diamond NT, Peachey T, Abramson D, Petrou S, Thomas EA (2012) Parametric computation predicts a multiplicative interaction between synaptic strength parameters that control gamma oscillations. Front Comput Neurosci 6:53 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Axon; Synapse; Channel/Receptor; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; Neocortex fast spiking (FS) interneuron; Neocortex spiny stellate cell; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s): I A; I K; I K,leak; I K,Ca; I Calcium; I_K,Na;
Gap Junctions: Gap junctions;
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Parameter sensitivity;
Implementer(s): Thomas, Evan [evan at evan-thomas.net]; Chambers, Jordan [jordandchambers at gmail.com];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; NMDA; I A; I K; I K,leak; I K,Ca; I Calcium; I_K,Na; Gaba; Glutamate;
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FRBGamma
hoc
balcomp.hoc *
defvar.hoc *
lbcreate.hoc *
mscreate.hoc *
parlib.hoc
parlib_traub.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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