Hyperexcitability from Nav1.2 channel loss in neocortical pyramidal cells (Spratt et al 2021)

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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.
Reference:
1 . Spratt PWE, Alexander RPD, Ben-Shalom R, Sahagun A, Kyoung H, Keeshen CM, Sanders SJ, Bender KJ (2021) Paradoxical hyperexcitability from NaV1.2 sodium channel loss in neocortical pyramidal cells Cell Rep. 36(5):109483 [PubMed]
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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 [rbenshalom at ucdavis.edu]; 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
Ri Increase
.ipynb_checkpoints
mechanisms
morphology
params
Stims
Volts
README *
.provenance.json *
25% Ri Increase Figures.ipynb *
axon_utils.hoc
biophysics.hoc *
cellinfo.json *
constants.hoc *
creategui.hoc *
createsimulation.hoc *
fit.hoc *
gui.ses *
init.hoc *
LICENSE *
morphology.hoc *
mosinit.hoc *
ringplot.hoc *
run.py *
run_RmpRiTau.py *
runModel.hoc *
template.hoc *
topo_code.hoc *
                            
dend_na12 =0.026145/2
dend_k = 0.004226
soma_na12 = 0.983955/2	
soma_K = 0.303472
node_na = 2
axon_KP =0.973538
axon_KT = 0.089259
axon_K = 1.021945
ais_na16		=	7 //3.137968
ais_na12		=	5 //3.137968
ais_ca = 0.000990
ais_KCa = 0.007104




soma_na16 = soma_na12
naked_axon_na = soma_na16/5
navshift = -10
dend_na16 =dend_na12
myelin_na = naked_axon_na
myelin_K = 0.303472
myelin_scale = 10
gpas_all = 3e-5
cm_all = 1




proc tfunc(){

    dend_na12 =transvec.x(0)
	dend_k = transvec.x(1)
	soma_na12 = transvec.x(2)
	soma_K = transvec.x(3)
	node_na = transvec.x(4)
	axon_KP = transvec.x(5)
	axon_KT = transvec.x(6)
	axon_K = transvec.x(7)
	ais_na16		=	transvec.x(8)
	ais_na12		=	transvec.x(9)
	ais_ca = transvec.x(10)
	ais_KCa = transvec.x(11)
	
	
	
	
	
	
	soma_na16 = soma_na12
	naked_axon_na = soma_na16/5
	navshift = -10
	dend_na16 =dend_na12
	myelin_na = naked_axon_na
	myelin_K = 0.303472
	myelin_scale = 10
	gpas_all = 3e-5
	cm_all = 1
	
	working()
	}