MEG of Somatosensory Neocortex (Jones et al. 2007)

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Accession:113732
"... To make a direct and principled connection between the SI (somatosensory primary neocortex magnetoencephalography) waveform and underlying neural dynamics, we developed a biophysically realistic computational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. ... our model provides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception."
Reference:
1 . Jones SR, Pritchett DL, Stufflebeam SM, Hämäläinen M, Moore CI (2007) Neural correlates of tactile detection: a combined magnetoencephalography and biophysically based computational modeling study. J Neurosci 27:10751-64 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell;
Channel(s): I T low threshold; I K; I M; I K,Ca; I Sodium; I Calcium; I R;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Magnetoencephalography; Touch;
Implementer(s): Sikora, Michael [Sikora at umn.edu];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; GabaA; GabaB; AMPA; NMDA; I T low threshold; I K; I M; I K,Ca; I Sodium; I Calcium; I R; Gaba; Glutamate;
COMMENT
26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error.
    Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal

km.mod

Potassium channel, Hodgkin-Huxley style kinetics
Based on I-M (muscarinic K channel)
Slow, noninactivating

Author: Zach Mainen, Salk Institute, 1995, zach@salk.edu
	
ENDCOMMENT

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

NEURON {
	SUFFIX km
	USEION k READ ek WRITE ik
	RANGE n, gk, gbar
	RANGE ninf, ntau
	GLOBAL Ra, Rb
	GLOBAL q10, temp, tadj, vmin, vmax
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
} 

PARAMETER {
	gbar = 10   	(pS/um2)	: 0.03 mho/cm2
	v 		(mV)
								
	tha  = -30	(mV)		: v 1/2 for inf
	qa   = 9	(mV)		: inf slope		
	
	Ra   = 0.001	(/ms)		: max act rate  (slow)
	Rb   = 0.001	(/ms)		: max deact rate  (slow)

	dt		(ms)
	celsius		(degC)
	temp = 23	(degC)		: original temp 	
	q10  = 2.3			: temperature sensitivity

	vmin = -120	(mV)
	vmax = 100	(mV)
} 


ASSIGNED {
	a		(/ms)
	b		(/ms)
	ik 		(mA/cm2)
	gk		(pS/um2)
	ek		(mV)
	ninf
	ntau (ms)	
	tadj
}
 

STATE { n }

INITIAL { 
	trates(v)
	n = ninf
}

BREAKPOINT {
        SOLVE states METHOD cnexp
	gk = tadj*gbar*n
	ik = (1e-4) * gk * (v - ek)
} 

LOCAL nexp

DERIVATIVE states {   :Computes state variable n 
        trates(v)      :             at the current v and dt.
        n' = (ninf-n)/ntau

}

PROCEDURE trates(v) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.
        
        TABLE ninf, ntau
	DEPEND  celsius, temp, Ra, Rb, tha, qa
	
	FROM vmin TO vmax WITH 199

	rates(v): not consistently executed from here if usetable_hh == 1


:        tinc = -dt * tadj
:        nexp = 1 - exp(tinc/ntau)
}


PROCEDURE rates(v) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.

        a = Ra * (v - tha) / (1 - exp(-(v - tha)/qa))
        b = -Rb * (v - tha) / (1 - exp((v - tha)/qa))

        tadj = q10^((celsius - temp)/10)
        ntau = 1/tadj/(a+b)
	ninf = a/(a+b)
}


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