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]
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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 V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic 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 V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic 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
cells
data
hoc
mod
net
README
balanal.hoc *
balcomp.hoc *
cell_templates.hoc *
des2runs.py
EFP2PSD.m
EFPgen.m
evan.hoc
finit.hoc *
fortmap.hoc *
FRBGamm.val
FRBGamma.des
gidcell.hoc *
manage_setup.hoc
singleoutput.m
spkplt.hoc *
synstrengthdef.hoc
                            
objref type_vinit, vgaba, typename[14]
type_vinit = new Vector(14)
vgaba = new Vector(14)

//cell type id according to the mpirank in the fortran program
//cell.type() returns this value. Note that the table order is below is the
//same as the base order in groucho.hoc
for i=0, 13 {
	j = fscan()
	typename[j] = new String() getstr(typename[j].s, 1)
	type_vinit.x[j] = fscan()  vgaba.x[j] = fscan()
}

suppyrRS_template.hoc:  rank = 0  suppyrRS    -70 -81
suppyrFRB_template.hoc: rank = 1  suppyrFRB   -70 -81
supbask_template.hoc:   rank = 2  supbask     -65 -75
supaxax_template.hoc:   rank = 3  supaxax     -65 -75
supLTS_template.hoc:    rank = 4  supLTS      -65 -75
spinstell_template.hoc: rank = 5  spinstell   -65 -75
tuftIB_template.hoc:    rank = 6  tuftIB      -70 -75
tuftRS_template.hoc:    rank = 7  tuftRS      -70 -75
nontuftRS_template.hoc: rank = 8  nontuftRS   -70 -75
deepbask_template.hoc:  rank = 9  deepbask    -65 -75
deepaxax_template.hoc:  rank = 10 deepaxax    -65 -75
deepLTS_template.hoc:   rank = 11 deepLTS     -65 -75
TCR_template.hoc:       rank = 12 TCR         -70 -81
nRT_template.hoc:       rank = 13 nRT         -75 -75

//note that the TCR rank value is used explicitly in initialization since the
//states are initialized at a different voltage value than the membrane potential.

TCRtype = 12