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, Paré 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 GLU cell; Hippocampus CA3 pyramidal GLU 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 GLU cell; Hippocampus CA3 pyramidal GLU 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;
/
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
                            
:Background to Pyramidal Cells AMPA+NMDA 

NEURON {
	POINT_PROCESS bg2pyr
	NONSPECIFIC_CURRENT inmda
	NONSPECIFIC_CURRENT iampa
	RANGE taun1, taun2, factorn, normconstn
	RANGE taua1, taua2, factora, normconsta
	RANGE gnmda, gnmdas, gNMDAmax, enmda
	RANGE gampa, gampas, gAMPAmax, eampa
	RANGE initW
}

UNITS {
	(mV) = (millivolt)
        (nA) = (nanoamp)
	(uS) = (microsiemens)
}

PARAMETER {

	:W
	initW = 6.3 : 6.3 : 8 :6

	:NMDA
	taun1 = 5 (ms)
	taun2 = 125 (ms)
	gNMDAmax = 0.5e-3 (uS)
	enmda = 0 (mV)

	:AMPA
	taua1 = .5 (ms)
	taua2 = 7 (ms)
	gAMPAmax = 1e-3 (uS)
	eampa = 0 (mV)
	
}

ASSIGNED {
	v (mV)
	eca (mV)
	
	:NMDA
	inmda (nA)
	gnmda
	gnmdas
	factorn
	normconstn

	:AMPA
	iampa (nA)
	gampa
	gampas
	factora
	normconsta
}

STATE {

	:NMDA
	An
	Bn

	:AMPA
	Aa
	Ba
}

INITIAL {

	:NMDA
	An = 0
	Bn = 0
	factorn = taun1*taun2/(taun2-taun1)
	normconstn = -1/(factorn*(1/exp(log(taun2/taun1)/(taun1*(1/taun1-1/taun2)))-1/exp(log(taun2/taun1)/(taun2*(1/taun1-1/taun2)))))

	:AMPA
	Aa = 0
	Ba = 0
	factora = taua1*taua2/(taua2-taua1)
	normconsta = -1/(factora*(1/exp(log(taua2/taua1)/(taua1*(1/taua1-1/taua2)))-1/exp(log(taua2/taua1)/(taua2*(1/taua1-1/taua2)))))
}

BREAKPOINT {
	SOLVE states METHOD cnexp
}

DERIVATIVE states {

	:NMDA
	An' = -An/taun1
	Bn' = -Bn/taun2
	gnmda = normconstn*factorn*(Bn-An)
	gnmdas = gnmda
	if (gnmdas>1) {gnmdas=1}
	inmda = initW*gNMDAmax*gnmdas*(v-enmda)*sfunc(v)

	:AMPA
	Aa' = -Aa/taua1
	Ba' = -Ba/taua2
	gampa = normconsta*factora*(Ba-Aa)
	gampas = gampa
	if (gampas > 1) {gampas = 1}
	iampa = initW*gAMPAmax*gampas*(v-eampa)
}

NET_RECEIVE(wgt) {
        LOCAL x
	x = wgt
	state_discontinuity(An,An+x)
	state_discontinuity(Bn,Bn+x)
	state_discontinuity(Aa,Aa+x)
	state_discontinuity(Ba,Ba+x)
}

:::::::::::: FUNCTIONs and PROCEDUREs ::::::::::::
FUNCTION sfunc (v (mV)) {
	UNITSOFF
	sfunc = 1/(1+0.33*exp(-0.06*v))
	UNITSON
}

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