A 1000 cell network model for Lateral Amygdala (Kim et al. 2013)

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Accession:150288
1000 Cell Lateral Amygdala model for investigation of plasticity and memory storage during Pavlovian Conditioning.
Reference:
1 . Kim D, Pare D, Nair SS (2013) Mechanisms contributing to the induction and storage of Pavlovian fear memories in the lateral amygdala. Learn Mem 20:421-30 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell; Synapse; Dendrite;
Brain Region(s)/Organism: Amygdala;
Cell Type(s): Hippocampus CA1 pyramidal cell; Hippocampus CA3 pyramidal cell; Hodgkin-Huxley neuron;
Channel(s): I Na,t; I L high threshold; I A; I M; I Sodium; I Calcium; I Potassium; I_AHP; Ca pump;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba; Dopaminergic Receptor;
Gene(s):
Transmitter(s): Dopamine; Norephinephrine;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Plasticity; Short-term Synaptic Plasticity; Long-term Synaptic Plasticity; Learning; Neuromodulation;
Implementer(s): Kim, Dongbeom [dk258 at mail.missouri.edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; Hippocampus CA3 pyramidal cell; AMPA; NMDA; Gaba; Dopaminergic Receptor; I Na,t; I L high threshold; I A; I M; I Sodium; I Calcium; I Potassium; I_AHP; Ca pump; Dopamine; Norephinephrine;
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KimEtAl2013
README.txt
bg2inter.mod
bg2pyr.mod
ca.mod *
cadyn.mod
cal2.mod *
capool.mod *
function_TMonitor.mod *
h.mod *
im.mod
interD2pyrD_STFD.mod
interD2pyrDDA_STFD.mod
interD2pyrDDANE_STFD.mod
interD2pyrDNE_STFD.mod
interD2pyrV_STFD.mod
interD2pyrVDA_STFD.mod
interV2pyrD_STFD.mod
interV2pyrDDA_STFD.mod
interV2pyrDDANE_STFD.mod
interV2pyrDNE_STFD.mod
interV2pyrV_STFD.mod
interV2pyrVDA_STFD.mod
kadist.mod *
kaprox.mod
kdrca1.mod
kdrca1DA.mod
kdrinter.mod *
leak.mod *
leakDA.mod *
leakinter.mod *
na3.mod
na3DA.mod
nainter.mod *
pyrD2interD_STFD.mod
pyrD2interV_STFD.mod
pyrD2pyrD_STFD.mod
pyrD2pyrDDA_STFD.mod
pyrD2pyrV_STFD.mod
pyrD2pyrVDA_STFD.mod
pyrV2interD_STFD.mod
pyrV2interV_STFD.mod
pyrV2pyrD_STFD.mod
pyrV2pyrDDA_STFD.mod
pyrV2pyrV_STFD.mod
pyrV2pyrVDA_STFD.mod
sahp.mod
sahpNE.mod
shock2interD.mod
shock2interV.mod
shock2pyrD.mod
shock2pyrV.mod
tone2interD.mod
tone2interDNE.mod
tone2interV.mod
tone2interVNE.mod
tone2pyrD.mod
tone2pyrD_LAdv.mod
tone2pyrDNE.mod
tone2pyrDNE_LAdv.mod
tone2pyrV.mod
tone2pyrV_LAdd.mod
tone2pyrVNE.mod
tone2pyrVNE_LAdd.mod
BgGen.hoc
Cell_list.txt
Cell_type.txt
function_ConnectInternal.hoc
function_ConnectTwoCells.hoc
function_NetStimOR.hoc
function_TimeMonitor.hoc
function_ToneGen.hoc
function_ToneSignalGen_Ctx.hoc
function_ToneSignalGen_Th.hoc
interneuron_template.hoc
LA_model_main_file.hoc
LAcells_template.hoc
NM.txt
shock2Idd.txt
shock2Idv.txt
shock2LAdd.txt
shock2LAdv.txt
shockcondi.hoc
Syn_Matrix.txt
tone2Idd.txt
tone2Idd2.txt
tone2Idv.txt
tone2Idv2.txt
tone2LAdd.txt
tone2LAdd2.txt
tone2LAdv.txt
tone2LAdv2.txt
                            
TITLE l-calcium channel
: l-type calcium channel


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)

	FARADAY = 96520 (coul)
	R = 8.3134 (joule/degC)
	KTOMV = .0853 (mV/degC)
}

PARAMETER {
	v (mV)
	celsius 	(degC)
	gcalbar=.003 (mho/cm2)
	ki=.001 (mM)
	cai (mM)
	cao (mM)
        tfa=1
}


NEURON {
	SUFFIX cal
	USEION ca READ cai,cao WRITE ica
        RANGE gcalbar,cai
        GLOBAL minf,tau
}

STATE {
	m
}

ASSIGNED {
	ica (mA/cm2)
        gcal (mho/cm2)
        minf
        tau   (ms)
}

INITIAL {
	rate(v)
	m = minf
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	gcal = gcalbar*m*m*h2(cai)
	ica = gcal*ghk(v,cai,cao)

}

FUNCTION h2(cai(mM)) {
	h2 = ki/(ki+cai)
}


FUNCTION ghk(v(mV), ci(mM), co(mM)) (mV) {
        LOCAL nu,f

        f = KTF(celsius)/2
        nu = v/f
        ghk=-f*(1. - (ci/co)*exp(nu))*efun(nu)
}

FUNCTION KTF(celsius (DegC)) (mV) {
        KTF = ((25./293.15)*(celsius + 273.15))
}


FUNCTION efun(z) {
	if (fabs(z) < 1e-4) {
		efun = 1 - z/2
	}else{
		efun = z/(exp(z) - 1)
	}
}

FUNCTION alp(v(mV)) (1/ms) {
	TABLE FROM -150 TO 150 WITH 200
	alp = 15.69*(-1.0*v+81.5)/(exp((-1.0*v+81.5)/10.0)-1.0)
}

FUNCTION bet(v(mV)) (1/ms) {
	TABLE FROM -150 TO 150 WITH 200
	bet = 0.29*exp(-v/10.86)
}

DERIVATIVE state {  
        rate(v)
        m' = (minf - m)/tau
}

PROCEDURE rate(v (mV)) { :callable from hoc
        LOCAL a
        a = alp(v)
        tau = 1/(tfa*(a + bet(v)))
        minf = tfa*a*tau
}
 














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