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

 Download zip file   Auto-launch 
Help downloading and running models
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]
Citations  Citation Browser
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;
/
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 *
                            
TITLE Calcium low threshold T type current version a for RD Traub 2005

COMMENT
	This current is found in the model deepLTS cells
	Traub made this model modifying Huguenard and Prince 1992 as
	appeared on Destexhe et al 1996 (in vivo in vitro ...)
	Modification by Tom Morse for Traub et al 2005
	modified from
	Implementation by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
	RD Traub, J Neurophysiol 89:909-921, 2003
ENDCOMMENT

INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }

UNITS { 
	(mV) = (millivolt) 
	(mA) = (milliamp) 
}
 
NEURON { 
	SUFFIX cat_a
	NONSPECIFIC_CURRENT i   : not causing [Ca2+] influx
	RANGE gbar, i, m, h, alphah, betah 	: m,h, alphah, betah for comparison with FORTRAN
}

PARAMETER { 
	gbar = 0.0 	(mho/cm2)
	v 		(mV)  
}
 
ASSIGNED { 
	i 		(mA/cm2) 
	minf hinf 	(1)
	mtau (ms) htau 	(ms) 
	alphah (/ms) betah	(/ms)
}
 
STATE {
	m h
}

BREAKPOINT { 
	SOLVE states METHOD cnexp 
	i = gbar * m * m * h * ( v - 125 ) 
	alphah = hinf/htau
	betah = 1/htau - alphah
}
 
INITIAL { 
	settables(v) 
:	m  = minf
	h  = hinf
	m  = 0
} 

DERIVATIVE states { 
	settables(v) 
	m' = ( minf - m ) / mtau 
	h' = ( hinf - h ) / htau
}

UNITSOFF 

PROCEDURE settables(v(mV)) { 
	TABLE minf, mtau,hinf, htau FROM -120 TO 40 WITH 641
        minf  = 1 / ( 1 + exp( ( -v - 52 ) / 7.4 ) )
        mtau  = 1 + .33 / ( exp( ( v + 27.0 ) / 10.0 ) + exp( ( - v - 102 ) / 15.0 ) )

        hinf  = 1 / ( 1 + exp( ( v + 80 ) / 5 ) )
        htau = 28.30 +.33 / (exp(( v + 48.0)/ 4.0) + exp( ( -v - 407.0) / 50.0 ) )

}

UNITSON