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
                            
: Calcium activated K channel.
: From Moczydlowski and Latorre (1983) J. Gen. Physiol. 82
: Model 3. (Scheme R1 page 523)

UNITS {
	(molar) = (1/liter)
	(mV) =	(millivolt)
	(mA) =	(milliamp)
	(mM) =	(millimolar)
	FARADAY = (faraday)  (kilocoulombs)
	R = (k-mole) (joule/degC)
}

NEURON {
    THREADSAFE
	SUFFIX skkca
	USEION ca READ cai
	USEION k READ ek WRITE ik
	RANGE gkbar, ik, qfact, abar, bbar, stau
	GLOBAL oinf, tau
}

PARAMETER {
	stau = 1
	qfact = 1
	celsius_sk	= 35	(degC) : 35
	v		(mV)
	gkbar=0.175	(mho/cm2)	: Maximum Permeability
	cai		(mM) 
	ek		(mV)

	d1 = .84	      :page 527 Table II channel A
	d2 = 1.0			:our index 2 is the paper's subscript 4
	k1 = .18	(mM)
	k2 = .011	(mM)
	abar = .48	(/ms)
	bbar = .28	(/ms) :page 524. our bbar is the paper's alpha
}

ASSIGNED {
	ik		(mA/cm2)
	oinf
	tau		(ms)
}

STATE {	o }		: fraction of open channels

BREAKPOINT {
	SOLVE state METHOD cnexp
	ik = gkbar*o*(v - ek)
}

DERIVATIVE state {
	rate(v, cai)
	o' = (oinf - o)/(tau/qfact)
}

INITIAL {
	rate(v, cai)
	o = oinf
:	VERBATIM
:		printf("R = %f\n",R);
:		printf("F = %f\n",FARADAY);
:	ENDVERBATIM
}

: From R1 page 523. beta in the paper is the rate from closed to open
: and we call it alp here.

FUNCTION alp(v (mV), ca (mM)) (1/ms) { :callable from hoc
	alp = abar/(1 + exp1(k1,d1,v)/ca)
}

FUNCTION bet(v (mV), ca (mM)) (1/ms) { :callable from hoc
	bet = bbar/(1 + ca/exp1(k2,d2,v))
}

FUNCTION exp1(k (mM), d, v (mV)) (mM) { :callable from hoc
	exp1 = k*exp(-2*d*FARADAY*v/R/(273.15 + celsius_sk))
}

PROCEDURE rate(v (mV), ca (mM)) { :callable from hoc
	LOCAL a
	a = alp(v,ca)
	tau = stau/(a + bet(v, ca))
	oinf = a*tau
}


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