Alcohol action in a detailed Purkinje neuron model and an efficient simplified model (Forrest 2015)

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Accession:180789
" ... we employ a novel reduction algorithm to produce a 2 compartment model of the cerebellar Purkinje neuron from a previously published, 1089 compartment model. It runs more than 400 times faster and retains the electrical behavior of the full model. So, it is more suitable for inclusion in large network models, where computational power is a limiting issue. We show the utility of this reduced model by demonstrating that it can replicate the full model’s response to alcohol, which can in turn reproduce experimental recordings from Purkinje neurons following alcohol application. ..."
Reference:
1 . Forrest MD (2015) Simulation of alcohol action upon a detailed Purkinje neuron model and a simpler surrogate model that runs >400 times faster. BMC Neurosci 16:27 [PubMed]
Citations  Citation Browser
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: Cerebellum;
Cell Type(s): Cerebellum Purkinje GABA cell;
Channel(s): I Na,t; I T low threshold; I A; I K; I K,leak; I M; I h; I K,Ca; I Sodium; I Calcium; I Potassium; I A, slow; I_HERG; Na/Ca exchanger; Na/K pump; I_AHP; I Cl, leak; I Na, leak; I Ca,p; I_KD; Ca pump;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Dendritic Action Potentials; Bursting; Ion Channel Kinetics; Oscillations; Simplified Models; Active Dendrites; Influence of Dendritic Geometry; Detailed Neuronal Models; Sodium pump; Depolarization block; Dendritic Bistability; Markov-type model; Alcohol Use Disorder;
Implementer(s): Forrest, Michael [mikeforrest at hotmail.com];
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell; I Na,t; I T low threshold; I A; I K; I K,leak; I M; I h; I K,Ca; I Sodium; I Calcium; I Potassium; I A, slow; I_HERG; Na/Ca exchanger; Na/K pump; I_AHP; I Cl, leak; I Na, leak; I Ca,p; I_KD; Ca pump;
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Forrest2015
collapse_algorithm
README.txt
bkpkj.mod *
cad.mod *
cadiff.mod *
cae.mod *
cap2.mod *
captain.mod *
cat.mod *
cha.mod *
erg.mod *
gkca.mod *
hpkj.mod *
k23.mod *
ka.mod *
kc3.mod *
kd.mod *
kdyn.mod *
khh.mod *
km.mod *
kpkj.mod *
kpkj2.mod *
kpkjslow.mod *
kv1.mod *
leak.mod *
lkpkj.mod *
myexchanger.mod *
myexchangersoma.mod *
mypump.mod *
mypumpsoma.mod *
nadifl.mod *
narsg.mod *
newnew.mod *
pump.mod *
2_compartment.hoc
full.ses *
full_data_writer.hoc
full_morph.hoc
lesbos.ses *
mex.hoc
mosinit.hoc
mosinit_full.hoc
mosinit_simple.hoc
simple_data_writer.hoc
                            
TITLE ERG CURRENT

COMMENT

ERG CURRENT
Coded up by Michael David Forrest using equations and parameters from:
Canavier CC, Oprisan SA, Callaway  JC, Ji H, Shepard PD (2007) Computational model predicts a role for ERG current in repolarizing plateau potentials in dopamine neurons: implications for modulation of neuronal activity. J Neurophysiol 98(5):3006-3022.

ENDCOMMENT


NEURON {
	SUFFIX erg
	USEION k READ ek WRITE ik
	NONSPECIFIC_CURRENT i
	RANGE gbar, g, ik, i, igate, nc, ca, cva, cka, cb, cvb, ckb, vth, delay, vhalf, vslope, vhalfhat, vslopehat
	GLOBAL ninf, tau
	GLOBAL gateCurrent, gunit
}

UNITS {
	(mV) = (millivolt)
	(mA) = (milliamp)
	(nA) = (nanoamp)
	(pA) = (picoamp)
	(S)  = (siemens)
	(mS) = (millisiemens)
	(nS) = (nanosiemens)
	(pS) = (picosiemens)
	(um) = (micron)
	(molar) = (1/liter)
	(mM) = (millimolar)		
}

CONSTANT {
	e0 = 1.60217646e-19 (coulombs)
	q10 = 2.7
	zn = 1.9196 (1)		: valence of n-gate
}

PARAMETER {

	ca = 0.22 (1/ms)
	cva = 16 (mV)
	cka = -26.5 (mV)
	cb = 0.22 (1/ms)           : I MOVED ALL THESE OUT OF CONSTANT BLOCK, INTO PARAMETER BLOCK, SO THEY CAN BE MODULATED
	cvb = 16 (mV)
	ckb = 26.5 (mV)        


	gateCurrent = 0 (1)	: gating currents ON = 1 OFF = 0
	
	gbar = 0.005 (S/cm2)   <0,1e9>
	gunit = 16 (pS)		: unitary conductance 
        vth = -10 (mV)
        delay = 3 (ms)
      vhalf= -35 (mV)
      vslope = 5 (mV)
      vhalfhat= -70 (mV)
      vslopehat = -20 (mV)
}

ASSIGNED {
	celsius (degC)
	v (mV)
	
	ik (mA/cm2)
        neo (mA/cm2)
	igate (mA/cm2)
	i (mA/cm2)
 
	ek (mV)
	g (S/cm2)
	nc (1/cm2)
	qt (1)

	ninf (1)
	tau (ms)
	alpha (1/ms)
	beta (1/ms)
        gategate
        gatefkt
       mousegate 

        hinfhat (1)
	tauhat (ms)
	alphahat (1/ms)
	betahat (1/ms) 

}

STATE { n h }

INITIAL {
	nc = (1e12) * gbar / gunit
:	qt = q10^((celsius-22 (degC))/10 (degC))
        qt = 1
	rateConst(v)
	n = ninf
        h = hinfhat
        neo = ik
}

BREAKPOINT {
	SOLVE state METHOD cnexp
      g = gbar * n * h
	ik = g * (v - ek) 


:	igate = nc * (1e6) * e0 * 4 * zn * ngateFlip()
:	if (gateCurrent != 0) { 
:		i = igate
:	}


}

DERIVATIVE state {
	rateConst(v)
:	n' = alpha * (1-n) - beta * n
        n' = (ninf-n)/tau 
        h' = (hinfhat-h)/tauhat 
}

PROCEDURE rateConst(v (mV)) {
	alpha = qt * alphaFkt(v)
	beta = qt * betaFkt(v)
:	ninf = alpha / (alpha + beta) 
        ninf = ninfFkt(v)
	tau = 1 / (alpha + beta)

: /////////////

	alphahat = qt * alphaFkthat(v)
	betahat = qt * betaFkthat(v)
:	hinfhat = alphahat / (alphahat + betahat) 
        hinfhat = hinfFkthat(v)
	tauhat = 1 / (alphahat + betahat)

}

FUNCTION alphaFkt(v (mV)) (1/ms) {
	alphaFkt = 0.00225 * exp(0.12 * v)
}

FUNCTION betaFkt(v (mV)) (1/ms) {
	betaFkt = 0.00004 * exp(-0.05 * v)
}

FUNCTION ninfFkt(v (mV)) (1/ms) {
	ninfFkt = 1 / ( 1 + exp(-(v - vhalf)/vslope)   )
}

: ///////////////////////////


FUNCTION alphaFkthat(v (mV)) (1/ms) {
	alphaFkthat =  0.1 * exp(0.02 * v)
}

FUNCTION betaFkthat(v (mV)) (1/ms) {
	betaFkthat = 0.003 * exp(-0.03 * v)
}

FUNCTION hinfFkthat(v (mV)) (1/ms) {
	hinfFkthat =  1 / ( 1 + exp(-(v - vhalfhat)/vslopehat)   )
}




:FUNCTION ngateFlip() (1/ms) {
:	ngateFlip = (ninf-n)/tau 
:}