Multiscale simulation of the striatal medium spiny neuron (Mattioni & Le Novere 2013)

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Accession:150284
"… We present a new event-driven algorithm to synchronize different neuronal models, which decreases computational time and avoids superfluous synchronizations. The algorithm is implemented in the TimeScales framework. We demonstrate its use by simulating a new multiscale model of the Medium Spiny Neuron of the Neostriatum. The model comprises over a thousand dendritic spines, where the electrical model interacts with the respective instances of a biochemical model. Our results show that a multiscale model is able to exhibit changes of synaptic plasticity as a result of the interaction between electrical and biochemical signaling. …"
Reference:
1 . Mattioni M, Le Novère N (2013) Integration of biochemical and electrical signaling-multiscale model of the medium spiny neuron of the striatum. PLoS One 8:e66811 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism: Striatum;
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell;
Channel(s): I Na,p; I Na,t; I T low threshold; I A; I K,Ca; I CAN; I Calcium; I A, slow; I Krp; I R; I Q;
Gap Junctions:
Receptor(s):
Gene(s): Kv4.2 KCND2; Kv1.2 KCNA2; Cav1.3 CACNA1D; Cav1.2 CACNA1C; Kv2.1 KCNB1;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Synaptic Plasticity; Signaling pathways; Calcium dynamics; Multiscale;
Implementer(s): Mattioni, Michele [mattioni at ebi.ac.uk];
Search NeuronDB for information about:  Neostriatum medium spiny direct pathway GABA cell; I Na,p; I Na,t; I T low threshold; I A; I K,Ca; I CAN; I Calcium; I A, slow; I Krp; I R; I Q;
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TimeScales-master
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AMPA.mod
bkkca.mod *
cadyn.mod
caL.mod *
caL13.mod *
caldyn.mod
caltrack.mod
can.mod *
caq.mod *
car.mod *
cat.mod *
catrack.mod
GABA.mod *
kaf.mod *
kas.mod *
kir.mod *
krp.mod *
naf.mod *
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NMDA.mod
rubin.mod
skkca.mod
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vecevent.mod
test_input.py
test_vecstim.py
                            
TITLE BK KCA channel for nucleus accumbens model - old version!

: large conductance kca channel

COMMENT Equations from 
		  Shao L.R., Halvorsrud R., Borg-Graham L., Storm J.F. The role
		  of BK-type Ca2_-dependent K+ channels in spike broadening
		  during repetitive firing in rat hippocampal pyramidal cells
		  J.Physiology (1999),521:135-146
		  
		  The Krasnow Institute
		  George Mason University

Copyright	  Maciej Lazarewicz, 2001
		  (mlazarew@gmu.edu)
		  All rights reserved.
ENDCOMMENT

NEURON {
	SUFFIX bkkca
	USEION k READ ek WRITE ik
	USEION ca READ cai
	RANGE  gkbar,ik, q
}

UNITS {
	(molar) = (1/liter)
	(mM)	= (millimolar)
	(S)  	= (siemens)
	(mA) 	= (milliamp)
	(mV) 	= (millivolt)
}

PARAMETER {
	turnOffinact = 1 (1)
    gkbar	= 0.001 (S/cm2)
    q = 1
	celsius		 (degC)
}

ASSIGNED {
        v       (mV)
        cai	(mM)
	ik	(mA/cm2)
	k1	(/ms)
	k2	(/ms)
	k3	(/ms)
	k4	(/ms)
	q10	(1)
	ek 	(mV)
}

STATE { cst ost ist }

BREAKPOINT { 
	SOLVE kin METHOD sparse
	ik = gkbar * ost * ( v - ek ) 
}

INITIAL {
	SOLVE kin STEADYSTATE sparse
}

KINETIC kin {
	rates(v)
	~cst<->ost  (k3,k4)
	~ost<->ist  (k1,0.0)
	~ist<->cst  (k2,0.0)
	CONSERVE cst+ost+ist=1
}

PROCEDURE rates( v(mV)) {
	 k1=alp( 0.1, v,  -10.0,   1.0 )
	 k2=alp( 0.1, v, -120.0, -10.0 )
	 k3=alpha( 0.001, 1.0, v, -20.0, 7.0 ) *1.0e8* ( cai*1.0(/mM) )^3
	 k4=alp( 0.01, v, -44.0,  -5.0 )
}

FUNCTION alpha( tmin(ms), tmax(ms), v(mV), vhalf(mV), k(mV) )(/ms){
        alpha = q / ( tmin + 1.0 / ( 1.0 / (tmax-tmin) + exp((v-vhalf)/k)*1.0(/ms) ) )
}

FUNCTION alp( tmin(ms), v(mV), vhalf(mV), k(mV) )(/ms){
        alp = q / ( tmin + exp( (v-vhalf) / k )*1.0(ms) )
}








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