Hyperexcitability from Nav1.2 channel loss in neocortical pyramidal cells (Spratt et al accepted)

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Accession:267067
Based on the Layer 5 thick-tufted pyramidal cell from the Blue Brain Project, we modify the distribution of the sodium channel Nav1.2 to recapitulate an increase in excitability observed in ex vivo slice experiments.
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: Prefrontal cortex (PFC);
Cell Type(s): Neocortex layer 5 pyramidal cell;
Channel(s): I h; I M; I Potassium; I Sodium; I L high threshold; I T low threshold;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.2 SCN2A;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s):
Implementer(s): Ben-Shalom, Roy [bens.roy at gmail.com]; Kyoung, Henry [hkyoung at berkeley.edu];
Search NeuronDB for information about:  I L high threshold; I T low threshold; I M; I h; I Sodium; I Potassium;
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SprattEtAl2021
Na12 Analysis
mechanisms
branching.mod *
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
na12.mod
na12_mut.mod
na1216.mod *
na1216_mut.mod *
na16.mod
na8st.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
nax8st.mod *
ProbAMPANMDA_EMS.mod *
ProbGABAAB_EMS.mod *
SK_E2.mod *
SKv3_1.mod *
vclmp_pl.mod *
26412.tmp *
                            
:Comment : The transient component of the K current
:Reference : :		Voltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats:subtypes and gradients,Korngreen and Sakmann, J. Physiology, 2000
:Comment : shifted -10 mv to correct for junction potential
:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21

NEURON	{
	SUFFIX K_Tst
	USEION k READ ek WRITE ik
	RANGE gK_Tstbar, gK_Tst, ik
}

UNITS	{
	(S) = (siemens)
	(mV) = (millivolt)
	(mA) = (milliamp)
}

PARAMETER	{
	gK_Tstbar = 0.00001 (S/cm2)
}

ASSIGNED	{
	v	(mV)
	ek	(mV)
	ik	(mA/cm2)
	gK_Tst	(S/cm2)
	mInf
	mTau
	hInf
	hTau
}

STATE	{
	m
	h
}

BREAKPOINT	{
	SOLVE states METHOD cnexp
	gK_Tst = gK_Tstbar*(m^4)*h
	ik = gK_Tst*(v-ek)
}

DERIVATIVE states	{
	rates()
	m' = (mInf-m)/mTau
	h' = (hInf-h)/hTau
}

INITIAL{
	rates()
	m = mInf
	h = hInf
}

PROCEDURE rates(){
  LOCAL qt
  qt = 2.3^((34-21)/10)

	UNITSOFF
		v = v + 10
		mInf =  1/(1 + exp(-(v+0)/19))
		mTau =  (0.34+0.92*exp(-((v+71)/59)^2))/qt
		hInf =  1/(1 + exp(-(v+66)/-10))
		hTau =  (8+49*exp(-((v+73)/23)^2))/qt
		v = v - 10
	UNITSON
}