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 : mtau deduced from text (said to be 6 times faster than for NaTa)
:Comment : so I used the equations from NaT and multiplied by 6
:Reference : Modeled according to kinetics derived from Magistretti & Alonso 1999
:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21

NEURON	{
	SUFFIX Nap_Et2
	USEION na READ ena WRITE ina
	RANGE gNap_Et2bar, gNap_Et2, ina
}

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

PARAMETER	{
	gNap_Et2bar = 0.00001 (S/cm2)
}

ASSIGNED	{
	v	(mV)
	ena	(mV)
	ina	(mA/cm2)
	gNap_Et2	(S/cm2)
	mInf
	mTau
	mAlpha
	mBeta
	hInf
	hTau
	hAlpha
	hBeta
}

STATE	{
	m
	h
}

BREAKPOINT	{
	SOLVE states METHOD cnexp
	gNap_Et2 = gNap_Et2bar*m*m*m*h
	ina = gNap_Et2*(v-ena)
}

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
		mInf = 1.0/(1+exp((v- -52.6)/-4.6))
    if(v == -38){
    	v = v+0.0001
    }
		mAlpha = (0.182 * (v- -38))/(1-(exp(-(v- -38)/6)))
		mBeta  = (0.124 * (-v -38))/(1-(exp(-(-v -38)/6)))
		mTau = 6*(1/(mAlpha + mBeta))/qt

  	if(v == -17){
   		v = v + 0.0001
  	}
    if(v == -64.4){
      v = v+0.0001
    }

		hInf = 1.0/(1+exp((v- -48.8)/10))
    hAlpha = -2.88e-6 * (v + 17) / (1 - exp((v + 17)/4.63))
    hBeta = 6.94e-6 * (v + 64.4) / (1 - exp(-(v + 64.4)/2.63))
		hTau = (1/(hAlpha + hBeta))/qt
	UNITSON
}