A fast model of voltage-dependent NMDA Receptors (Moradi et al. 2013)

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These are two or triple-exponential models of the voltage-dependent NMDA receptors. Conductance of these receptors increase voltage-dependently with a "Hodgkin and Huxley-type" gating style that is also depending on glutamate-binding. Time course of the gating of these receptors in response to glutamate are also changing voltage-dependently. Temperature sensitivity and desensitization of these receptor are also taken into account. Three previous kinetic models that are able to simulate the voltage-dependence of the NMDARs are also imported to the NMODL. These models are not temperature sensitive. These models are compatible with the "event delivery system" of NEURON. Parameters that are reported in our paper are applicable to CA1 pyramidal cell dendrites.
1 . Moradi K, Moradi K, Ganjkhani M, Hajihasani M, Gharibzadeh S, Kaka G (2013) A fast model of voltage-dependent NMDA receptors. J Comput Neurosci 34:521-31 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism: Neocortex; Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Gap Junctions:
Receptor(s): NMDA; Glutamate;
Gene(s): NR2B GRIN2B;
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Ion Channel Kinetics; Simplified Models; Long-term Synaptic Plasticity; Methods;
Implementer(s): Moradi, Keivan [k.moradi at gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; NMDA; Glutamate; Glutamate;
/* In this experiment we want to compare the simulation speed of our model with other classical models of NMDA

//fully activates cache efficiency

tstop = 4000
dt = .025
celsius = 23	//room temperature in Clarke08 experiment = ?
v_init = -65

// ------------------
create soma
access soma

//------------Voltage Clamp---------------------------	
objref vc

vc = new VClamp(.5)
	vc.dur[0] = tstop
	vc.amp[0] = 40
objref sNMDA, stim, nc
{sNMDA = new Exp5NMDA(.5) SynWeight = 0.18532 } // Our Model 2012
//{sNMDA = new NMDA10_1(.5) SynWeight = 0.039071} // Kampa et al, 2004 model
//{sNMDA = new NMDA10_2(.5) SynWeight = 0.018831} // Vargas-Caballero & Robinson, 2004 model
//{sNMDA = new NMDA16(.5) SynWeight = 0.018831} // Vargas-Caballero & Robinson, 2004 model

stim = new NetStim(.5)
	stim.interval = 200		//ms (mean) time between spikes
	stim.number = 20		//(average) number of spikes
	stim.start 	= 1.53427 - 1	//ms (most likely) start time of first spike
	stim.noise 	= 0			//---- range 0 to 1. Fractional randomness.
	//0 deterministic, 1 intervals have negexp distribution.

nc = new NetCon(stim, sNMDA)

proc init_NMDA() {
	nc.weight = SynWeight
	nc.delay = 1
objref FinNMDA
FinNMDA = new FInitializeHandler(3,"init_NMDA()")

// objref iNMDA, vSoma
// iNMDA = new Graph()
// iNMDA.size(0,tstop,-5.5,2.5)
// iNMDA.addvar("sNMDA.i",3,0)
// iNMDA.save_name("graphList[0].")
// graphList[0].append(iNMDA)

// vSoma = new Graph()
// vSoma.size(0,tstop,-100,50)
// vSoma.addvar("soma.v(.5)",3,0)
// vSoma.save_name("graphList[0].")
// graphList[0].append(vSoma)

x = startsw()
for i=1, 10 {
print startsw() - x

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