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
mod
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 NaP - persistent sodium current for nucleus accumbens 

COMMENT
Magistretti J, Alonso A (1999). "Biophysical properties and slow
voltage-dependent inactivation of a sustained sodium current in entorhinal
cortex layer-II principal neurons." J Gen Phys, 114: 491-509.

Traub RD, Buhl EH et al (2003). "Fast rhythmic bursting can be induced in
layer 2/3 cortical neurons by enhancing persistent na+ conductance or by
blocking BK channels." J Neurophys 89: 909-921.

Jason Moyer 2004 - jtmoyer@seas.upenn.edu
ENDCOMMENT

UNITS {
        (mA) = (milliamp)
        (mV) = (millivolt)
        (S)  = (siemens)
}
 
NEURON {
        SUFFIX nap
        USEION na READ ena WRITE ina
        RANGE  gnabar, ina
}
 
PARAMETER {
	gnabar   =   4e-5 (S/cm2)	: 4e-5 in soma; 1.3802e-7 in dends

	mvhalf = -52.6		(mV)	: Magistretti 1999, Fig 4
	mslope = -4.6		(mV)	: Magistretti 1999, Fig 4

	hvhalf = -48.8		(mV)	: Magistretti 1999, Fig 4
	hslope = 10.0		(mV)	: Magistretti 1999, Fig 4

	qfact = 3
}
 
STATE { m h }
 
ASSIGNED {
	ena		(mV)
        v 		(mV)
        ina		(mA/cm2)
        gna		(S/cm2)

        minf
	hinf	

	mtau	(ms)			: Traub 2003, Table A2
   }
 
BREAKPOINT {
        SOLVE state METHOD cnexp
        gna = gnabar * m * h  
        ina = gna * ( v - ena )
:        VERBATIM
:        	printf("Ena is %g\n", ena);
:        ENDVERBATIM
}
 

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

FUNCTION_TABLE tauhnap (v(mV))  (ms)		: Magistretti 1999, Fig 8A

DERIVATIVE state { 
        rates(v)
        m' = (minf - m) / mtau
        h' = (hinf - h) / (tauhnap(v)/qfact)    
}
 
PROCEDURE rates(v (mV)) {  
	TABLE minf, hinf, mtau
		FROM -200 TO 200 WITH 201

		minf = 1 / (1 + exp( (v - mvhalf) / mslope))
		hinf = 1 / (1 + exp( (v - hvhalf) / hslope))
		
		UNITSOFF
		if (v < -40) {			: Traub 2003, Table A2
			mtau = 0.025 + 0.14 * exp( (v + 40 ) / 10)
		} else {
			mtau = 0.02 + 0.145 * exp( (-v - 40) / 10)
		}
		UNITSON
}
 
 

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