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
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vecevent.mod
test_input.py
test_vecstim.py
                            
TITLE Nahc - Fast sodium current for nucleus accumbens (from hippocampal pyramidal cell)

COMMENT
Martina M, Jonas P (1997). "Functional differences in na+ channel gating between fast-
spiking interneurons and principal neurons of rat hippocampus." J Phys, 505(3): 593-603.

recorded at 22C - Q10 of 3 to convert to 35C

Jason Moyer 2004 - jtmoyer@seas.upenn.edu

ENDCOMMENT

UNITS {
        (mA) = (milliamp)
        (mV) = (millivolt)
        (S)  = (siemens)
}
 
NEURON {
        SUFFIX naf
        USEION na READ ena WRITE ina
        RANGE  gnabar, ina, mshift, hshift
}
 
PARAMETER {
    gnabar   =   1.5 	(S/cm2)	: 1.5 in soma, 0.0195 in all dends

	mvhalf = -23.9		(mV)	: Martina/Jonas 1997 Table 1 (Pyr. cells)
	mslope = -11.8		(mV)	: Martina/Jonas 1997 Table 1 (Pyr. cells)
	mshift = 0		(mV)	: 

	hvhalf = -62.9		(mV)	: Martina/Jonas 1997 Table 1 (Pyr. cells)
	hslope = 10.7		(mV)	: Martina/Jonas 1997 Table 1 (Pyr. cells)
	hshift = 0		(mV)	: 

	mqfact = 3
	hqfact = 3	
}
 
STATE { m h }
 
ASSIGNED {
		ena				(mV)
        v 				(mV)
        ina 				(mA/cm2)
        gna				(S/cm2)
        minf 
	hinf
}
 
BREAKPOINT {
        SOLVE state METHOD cnexp
        gna = gnabar * m * m * m  * h
        ina = gna * ( v - ena )
}
 
 
INITIAL {
	rates(v)
	
	m = minf
	h = hinf
}

FUNCTION_TABLE taumnaf (v(mV))  (ms)	: Martina/Jonas 1997 Fig 2E
FUNCTION_TABLE tauhnaf (v(mV))  (ms)	: Martina/Jonas 1997 Fig 4C

DERIVATIVE state { 
        rates(v)
        m' = (minf - m) / (taumnaf(v)/mqfact)
        h' = (hinf - h) / (tauhnaf(v)/hqfact)
}
 
PROCEDURE rates(v (mV)) {  
	TABLE minf, hinf DEPEND mshift, hshift, mslope, hslope
		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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