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

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Accession:145836
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.
Reference:
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]
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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;
Channel(s):
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 I'm going to test my voltage dependent NMDA model
Its voltage dependent block provided by Mg and happens fast
Its slow unblock provided by a voltage dependent gating particle
I'm going to fit my model to the Wild Type NMDAR recordings obtained from 
Dendrites of CA1-CA3 Neurons Reported in Spruston95
*/
load_file("nrngui.hoc")
load_file("params.hoc")

//fully activates cache efficiency
cvode.cache_efficient(1)  

tstop = 522
dt = .025
celsius = 18	//room temperature
v_init = 40

Tau1	= 1.4731
Tau2	= 391.64
SynWeight = 0.22903

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

objref sNMDA, stim, nc
stim = new NetStim(.5)
	stim.interval = 20	//ms (mean) time between spikes
	stim.number = 1	//(average) number of spikes
	stim.start 	= 9	//ms (most likely) start time of first spike
	stim.noise 	= 0		//---- range 0 to 1. Fractional randomness.
	//0 deterministic, 1 intervals have negexp distribution.
sNMDA = new Exp3NMDA(.5)
nc = new NetCon(stim, sNMDA)

proc init_NMDA() {
	sNMDA.tau1	= Tau1	
    sNMDA.tau2	= Tau2	

	nc.weight = SynWeight
	nc.delay = 1
}
objref FinNMDA
FinNMDA = new FInitializeHandler(3,"init_NMDA()")
	
objref vc
vc = new VClamp(.5)
	vc.dur[0] = 56.5
	vc.amp[0] = 40
	vc.dur[1] = 48.5
	vc.amp[1] = -80
	vc.dur[2] = 500
	vc.amp[2] = 40
	
// objref iNMDA, vSoma
// iNMDA = new Graph()
// iNMDA.size(0,tstop,-.1,1)
// 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)


// xpanel("PSP amplitudes")			// displays max amplitude at soma, dend(273)
	// xvalue("Tau1")			// dend(750) when the simulation is over
	// xvalue("Tau2")
	// xvalue("SynWeight")
	// xbutton("InitRun()")
// xpanel(500,500)
// proc InitRun() {
// init()
// run()
// }

load_file("MRF-EXP3NMDA-VD.ses")

// objref iNMDA, iFile, time, tFile
// iNMDA = new Vector()
// iNMDA.record(&sNMDA.i)
// iFile = new File("iFile.dat")
// iFile.wopen("iFile.dat")

// time = new Vector()
// time.record(&t)
// tFile = new File("tFile.dat")
// tFile.wopen("tFile.dat")

// init()
// run()

// iNMDA.printf(iFile)
// iFile.close()

// time.printf(tFile)
// tFile.close()