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 neuron;
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 neuron; 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 Krp (4ap resistant, persistent) current for nucleus accumbens

COMMENT

Nisenbaum ES, Wilson CJ, Foehring RC, Surmeier DJ (1996). Isolation and
characterization of a persistent potassium current in neostriatal neurons. J
Neurophys 76(2): 1180-1194.

Recorded at 22C - corrected to 35C with qfact 3

Jason Moyer 2004 - jtmoyer@seas.upenn.edu

ENDCOMMENT

UNITS {
        (mA) = (milliamp)
        (mV) = (millivolt)
        (S)  = (siemens)
}
 
NEURON {
        SUFFIX krp
        USEION k READ ek WRITE ik
        RANGE  gkbar, ik
}
 
PARAMETER {
	gkbar   =   0.002 (S/cm2)

	mvhalf = -13.5		(mV)	: Nisenbaum 1996, Fig 6C
	mslope = -11.8		(mV)	: Nisenbaum 1996, Fig 6C
	mshift = 0		(mV)

	hvhalf = -54.7		(mV)	: Nisenbaum 1996, Fig 9D
	hslope = 18.6		(mV)	: Nisenbaum 1996, Fig 9D
 	hshift = 0		(mV)

 	a = 0.7				: matched to Nisenbaum 1996, figure 9A (with qfact = 1)
 	qfact = 3.0
}
 
STATE { m h }
 
ASSIGNED {
	ek				(mV)
        v 				(mV)
        ik 				(mA/cm2)
        gk				(S/cm2)
        minf 
	hinf
    }
 
BREAKPOINT {
        SOLVE state METHOD cnexp
        gk = gkbar * m * (a*h + (1-a)) 
        ik = gk * ( v - ek )
}
 

 
INITIAL {
	rates(v)
	
	m = minf
	h = hinf
}

FUNCTION_TABLE taumkrp (v(mV))  (ms)
FUNCTION_TABLE tauhkrp (v(mV))  (ms)

DERIVATIVE state { 
        rates(v)
        m' = (minf - m) / (taumkrp(v)/qfact)
        h' = (hinf - h) / (tauhkrp(v)/qfact)
}
 
PROCEDURE rates(v (mV)) {  
	TABLE minf, hinf DEPEND mshift, hshift
		FROM -200 TO 200 WITH 201
			minf = 1 / (1 + exp( (v - mvhalf - mshift) / mslope ))
			hinf = 1 / (1 + exp( (v - hvhalf - hshift) / hslope ))
}
 
 

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