Layer V pyramidal cell functions and schizophrenia genetics (Mäki-Marttunen et al 2019)

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Accession:249463
Study on how GWAS-identified risk genes of shizophrenia affect excitability and integration of inputs in thick-tufted layer V pyramidal cells
Reference:
1 . Mäki-Marttunen T, Devor A, Phillips WA, Dale AM, Andreassen OA, Einevoll GT (2019) Computational modeling of genetic contributions to excitability and neural coding in layer V pyramidal cells: applications to schizophrenia pathology Front. Comput. Neurosci. 13:66
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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: Neocortex;
Cell Type(s):
Channel(s): I A; I M; I h; I K,Ca; I Calcium; I A, slow; I Na,t; I Na,p; I L high threshold; I T low threshold;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON; Python;
Model Concept(s): Schizophrenia; Dendritic Action Potentials; Action Potential Initiation; Synaptic Integration;
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  AMPA; NMDA; Gaba; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I A, slow; Gaba; Glutamate;
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l5pc_scz
almog
cells
README.html
BK.mod *
ca_h.mod
ca_r.mod
cad.mod *
epsp.mod *
ih.mod *
kfast.mod
kslow.mod
na.mod
ProbAMPANMDA2.mod *
ProbUDFsyn2.mod *
SK.mod *
best.params *
calcifcurves2.py
calcifcurves2_comb_one.py
calcnspikesperburst.py
calcsteadystate.py
calcupdownresponses.py
cc_run.hoc *
coding.py
coding_comb.py
coding_nonprop_somaticI.py
coding_nonprop_somaticI_comb.py
collectifcurves2_comb_one.py
collectthresholddistalamps.py
combineppicoeffs_comb_one.py
drawfigcomb.py
drawnspikesperburst.py
findppicoeffs.py
findppicoeffs_merge.py
findppicoeffs_merge_comb_one.py
findthresholdbasalamps_coding.py
findthresholddistalamps.py
findthresholddistalamps_coding.py
findthresholddistalamps_comb.py
main.hoc *
model.hoc *
model_withsyns.hoc
mosinit.hoc *
mutation_stuff.py
myrun.hoc *
myrun_withsyns.hoc
mytools.py
params.hoc *
protocol.py
savebasalsynapselocations_coding.py
savesynapselocations_coding.py
scalemutations.py
scalings_cs.sav
setparams.py
synlocs450.0.sav
                            
TITLE BK-type Purkinje calcium-activated potassium current

COMMENT

NEURON implementation of a BK-channel in Purkinje cells
Kinetical Scheme: Hodgkin-Huxley (m^3*z^2*h)

Modified from Khaliq et al., J.Neurosci. 23(2003)4899
 
Laboratory for Neuronal Circuit Dynamics
RIKEN Brain Science Institute, Wako City, Japan
http://www.neurodynamics.brain.riken.jp

Reference: Akemann and Knoepfel, J.Neurosci. 26 (2006) 4602
Date of Implementation: May 2005
Contact: akemann@brain.riken.jp

Modified by Tuomo Maki-Marttunen: Moved CONSTANT block contents
to PARAMETER block to allow mutation-specific changes to ion
channels

ENDCOMMENT

NEURON {
       SUFFIX bk
       USEION k READ ek WRITE ik
       USEION ca READ cai
       RANGE gbar, gk,  ik, minf, taum, hinf, tauh, zinf, tauz
       GLOBAL zhalf
}

UNITS {
	(mV) = (millivolt)
	(mA) = (milliamp)
	(nA) = (nanoamp)
	(pA) = (picoamp)
	(S)  = (siemens)
	(nS) = (nanosiemens)
	(pS) = (picosiemens)
	(um) = (micron)
	(molar) = (1/liter)
	(mM) = (millimolar)		
}

PARAMETER {
	v (mV)
	celsius (degC)

	gbar = 40 (pS/um2)

	ek (mV)
	cai (mM)

	zhalf = 0.01 (mM)

	q10 = 3
	
	offm = -28.9 (mV)
	slom = 6.2 (mV)

	ctm = 0.000505 (s)
	ctmmax = 1.0 (s)
	offmt1 = -86.4 (mV)
	slomt1 = 10.1 (mV)
	offmt2 = 33.3 (mV)
	slomt2 = 10 (mV)

	ctauz = 1 (ms)

	ch = 0.085
	offh = -32 (mV)
	sloh = 5.8 (mV)
	cth = 0.0019 (s)
	cthmax = 1.0 (s)
	offht1 = -48.5 (mV)
	sloht1 = 5.2 (mV)
	offht2 = 54.2 (mV)
	sloht2 = 12.9 (mV)
}

ASSIGNED {
	ik (mA/cm2)
	qt
	gk (pS/um2)   
	minf
	taum (ms)
	hinf
	tauh (ms)
	zinf
	tauz (ms)
}

STATE {
	m   FROM 0 TO 1
	z   FROM 0 TO 1
	h   FROM 0 TO 1
}

INITIAL {
	qt = q10^((celsius-22 (degC))/10 (degC))
	rates(v)
	m = minf
	z = zinf
	h = hinf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	gk = gbar * m^3 * z^2 * h      
	ik = (1e-4)* gk * (v - ek)
}

DERIVATIVE states {
	rates(v)
	m' = (minf-m)/taum
	z' = (zinf-z)/tauz
	h' = (hinf-h)/tauh
}

PROCEDURE rates( v (mV) ) {
	v = v + 5 (mV)
	minf = 1 / ( 1+exp((offm-v)/slom) )
	taum = (1e3) * ( ctm + ctmmax / ( exp(-(offmt1-v)/slomt1) + exp((offmt2-v)/slomt2) ) ) / qt
	
	zinf = 1 /(1 + zhalf/cai)
	tauz = ctauz/qt

	hinf = ch + (1-ch) / ( 1+exp(-(offh-v)/sloh) )
	tauh = (1e3) * ( cth + cthmax / ( exp(-(offht1-v)/sloht1) + exp((offht2-v)/sloht2) ) ) / qt
}