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
mod
alphasyndiffeq.mod *
alphasynkin.mod *
alphasynkint.mod *
ampa.mod *
ar.mod *
cad.mod *
cal.mod *
cat.mod *
cat_a.mod *
gabaa.mod *
iclamp_const.mod *
k2.mod *
ka.mod *
ka_ib.mod *
kahp.mod *
kahp_deeppyr.mod *
kahp_slower.mod *
kc.mod *
kc_fast.mod *
kdr.mod *
kdr_fs.mod *
km.mod *
naf.mod
naf_tcr.mod *
naf2.mod
nap.mod
napf.mod *
napf_spinstell.mod *
napf_tcr.mod *
par_ggap.mod *
pulsesyn.mod *
rampsyn.mod *
rand.mod *
ri.mod
traub_nmda.mod *
                            
COMMENT
alphasynkint.mod
Alpha Synapse Traub-like implemented with Kinetic Scheme as per 
Chapter 10 NEURON book
Used to return peak conductance of 1, however now it is set so that 
a peak conductance of tau2*exp(-1) is reached because that's what
the Traub alpha function (t-t_0)*exp(-(t-t_0)/tau) reaches..
ENDCOMMENT
NEURON {
	POINT_PROCESS AlphaSynKinT : ending T is for Traub, see notes
	RANGE tau, e, i
	NONSPECIFIC_CURRENT i
}

UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(uS) = (microsiemens)
}

PARAMETER {
	tau = 0.1 (ms) <1e-9,1e9>
	e = 0	(mV)
}

ASSIGNED {
	v (mV)
	i (nA)
}

STATE { a (microsiemens) g (uS) }

INITIAL {
	g=0
}

BREAKPOINT {
	SOLVE state METHOD sparse
	i = g*(v - e)
}

KINETIC state {
	~ a <-> g (1/tau, 0)
	~ g -> (1/tau)
}

NET_RECEIVE(weight (uS)) {
:	a = a + weight*exp(1) * (tau*exp(-1))
: the above last factor changes peak conductance to from
: 1 to tau*exp(-1) so formula becomes:
	a = a + weight*tau*1(/ms)
}