Firing neocortical layer V pyramidal neuron (Reetz et al. 2014; Stadler et al. 2014)

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Accession:168148
Neocortical Layer V model with firing behaviour adjusted to in vitro observations. The model was used to investigate the effects of IFN and PKC on the excitability of neurons (Stadler et al 2014, Reetz et al. 2014). The model contains new channel simulations for HCN1, HCN2 and the big calcium dependent potassium channel BK.
References:
1 . Stadler K, Bierwirth C, Stoenica L, Battefeld A, Reetz O, Mix E, Schuchmann S, Velmans T, Ros (2014) Elevation in type I interferons inhibits HCN1 and slows cortical neuronal oscillations. Cereb Cortex 24:199-210 [PubMed]
2 . Reetz O, Stadler K, Strauss U (2014) Protein kinase C activation mediates interferon-β-induced neuronal excitability changes in neocortical pyramidal neurons Journal of Neuroinflammation 11(1):185 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I A; I K; I M; I h; I K,Ca; I Sodium; I Calcium; I Mixed; I Potassium; I Q;
Gap Junctions:
Receptor(s):
Gene(s): HCN1; HCN2;
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Detailed Neuronal Models; Action Potentials; Signaling pathways;
Implementer(s): Stadler, Konstantin [konstantin.stadler at ntnu.no];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; I Na,p; I Na,t; I L high threshold; I A; I K; I M; I h; I K,Ca; I Sodium; I Calcium; I Mixed; I Potassium; I Q;
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

kv.mod

Potassium channel, Hodgkin-Huxley style kinetics
Kinetic rates based roughly on Sah et al. and Hamill et al. (1991)

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

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

NEURON {
	SUFFIX kv
	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 = 5   	(pS/um2)	: 0.03 mho/cm2
	v 		(mV)
								
	tha  = 25	(mV)		: v 1/2 for inf
	qa   = 9	(mV)		: inf slope		
	
	Ra   = 0.02	(/ms)		: max act rate
	Rb   = 0.002	(/ms)		: max deact rate	

	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)
} 



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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