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 *
nap.mod *
NMDA.mod
rubin.mod
skkca.mod
stim.mod *
vecevent.mod
test_input.py
test_vecstim.py
                            
TITLE Submembrane calcium dynamics for N, P/Q, R calcium pool for NAcb cell

: Rubin, Gerken, Bi, Chow J Neurophys (2005) 93: 2600-2613.

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	SUFFIX rubin
	USEION ca READ cai 
	USEION cal READ cali 
	RANGE P, V, A, B, D, W
}

UNITS {
	(molar) = (1/liter)	
	(mM)	= (millimolar)
	(uM)	= (micromolar)
}

PARAMETER {
	scale = 1	
	pHC = 100		(uM)	:4
	pHN = 100
	aHC = 28 		(uM)	:0.6
	aHN = 3
	
	vtheta = 35		(uM)	:2
	dtheta = 0.36 			:2.6
	btheta = 0.375 			:0.55
	
	sigmav = -0.05	(uM)
	sigmad = -0.01
	sigmab = -0.02
	
	tp = 50 	(ms)
	ta = 2		(ms)
	tv = 10		(ms)
	td = 250	(ms)
	tb = 20		(ms)
	tw = 500 	(ms)

	kp = -0.1
	kd = -0.002

	av = 2
	ad = 1.0
	ab = 5.0
	aw = 0.8
	
	bw = 0.6
	
	cp = 1
	cd = 500
	
	p = 0.3
	d = 0.01	
}

STATE {
	A
	V
	B
	D
	P
	W
	
	cai		(mM) 
	cali		(mM) 
}

INITIAL {
	W = 0
	scale_it()
}

ASSIGNED {
	asig
	vsig
	psig
	dsig
	bsig

	w1
	w2
	catot (mM)
}
	
BREAKPOINT {
	catot = cai + cali
	settables(catot)
	SOLVE state METHOD derivimplicit
}

DERIVATIVE state { 

	A' = (asig - A)/ta
	V' = (vsig - V)/tv
	bsig = ab/( 1 + exp((A-btheta)/sigmab) ) 
	B' = (bsig - B - cd*B*V)/tb
	dsig = ad/( 1 + exp((B-dtheta)/sigmad) ) 
	D' = (dsig - D)/td
	P' = (psig - cp*A*P)/tp

	w1 = aw / (1 + exp((P-p)/kp) )
	w2 = bw / (1 + exp((D-d)/kd) )
	W' = (w1 - w2 - W) / tw
}

PROCEDURE settables( cai (mM) ) {
	TABLE asig, vsig, psig DEPEND aHC, aHN, pHC, pHN, vtheta, sigmav, av
		FROM 0.0005 TO 0.1 WITH 200

		asig = ( (cai*(1000)/aHC)^aHN )/ (1 + (cai*(1000)/aHC)^aHN ) 
		vsig = av/( 1 + exp((cai*(1000)-vtheta)/sigmav) ) 
		psig = 10 * ( (cai*(1000)/pHC)^pHN ) / (1 + (cai*(1000)/pHC)^pHN ) 
}

PROCEDURE scale_it() {
	pHC = 4 (uM) * scale 
	aHC = 0.6 (uM) * scale
	
	vtheta = 2 (uM) * scale
	dtheta = 2.6 * scale
	btheta = 0.55 * scale
}

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