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Kinetic synaptic models applicable to building networks (Destexhe et al 1998)

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Simplified AMPA, NMDA, GABAA, and GABAB receptor models useful for building networks are described in a book chapter. One reference paper synthesizes a comprehensive general description of synaptic transmission with Markov kinetic models which is applicable to modeling ion channels, synaptic release, and all receptors. Also a simple introduction to this method is given in a seperate paper Destexhe et al Neural Comput 6:14-18 , 1994). More information and papers at and through email:
1 . Destexhe A, Mainen ZF, Sejnowski TJ (1994) Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. J Comput Neurosci 1:195-230 [PubMed]
2 . Destexhe A, Mainen ZF, Sejnowski TJ (1998) Kinetic models of synaptic transmission Methods In Neuronal Modeling, Koch C:Segev I, ed. pp.1
3 . Destexhe A, Mainen Z, Sejnowski TJ (1994) An efficient method for computing synaptic conductances based on a kinetic model of receptor binding Neural Comput 6:14-18
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA; Glutamate; Gaba;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Ion Channel Kinetics; Simplified Models; Markov-type model;
Implementer(s): Destexhe, Alain [Destexhe at]; Mainen, Zach [Mainen at];
Search NeuronDB for information about:  GabaA; GabaB; AMPA; NMDA; Glutamate; Gaba; Gaba; Glutamate;

	demo file for first order kinetic synapse mechanism

	This file provides a simulation of GABAergic IPSP's in a passive
	compartment.  A presyaptic compartment (PRE) is created and 
	contains Hodgkin-Huxley Na/K currents for generating action potentials.
	A current pulse is injected in PRE to elicit a train of spikes.
	For each spike an IPSP is generated in the postsynaptic compartment
	(POST).  The number of presynaptic spikes can be adjusted by changing
        the pulse duration (stim.dur).

	Whole-cell recorded GABA-A postsynaptic currents (Otis et al,
        J. Physiol. 463: 391-407, 1993) were used to estimate the parameters
	of the present simulations; the fit was performed using a simplex
	algorithm (see Destexhe et al., J. Neurophysiol. 72: 803-818, 1994).

	Alain Destexhe, The Salk Institute, 1994


//  load and define general graphical procedures

// xopen("$(NEURONHOME)/lib/hoc/stdrun.hoc")

objectvar g[20]			// max 20 graphs
ngraph = 0

proc addgraph() { local ii	// define subroutine to add a new graph
				// addgraph("variable", minvalue, maxvalue)
	ngraph = ngraph+1
	ii = ngraph-1
	g[ii] = new Graph()

// nrnmainmenu()			// create main menu
nrncontrolmenu()		// crate control menu

//  general parameters

tstop = 50
runStopAt = tstop
steps_per_ms = 1/dt
celsius = 36
v_init = -70

//  create compartments and insert passive properties

create PRE,POST
forall {
  insert pas

//  insert presynaptic mechanisms

access PRE		// insert Hodgk-Hux. Na+ and K+ currents for spikes
insert hh2
ek = -90
gnabar_hh2 = 0.1
gkbar_hh2 = 0.03

objectvar stim		// insert current injection

PRE stim = new IClamp(.5)	  // command for version nrn3a8 or newer
// PRE stim = new PulseStim(.5)	  // command for older NEURON versions

stim.del = 0
stim.dur = 2		// 2 ms for single psp, 10 ms for train of psps
stim.amp = 0.1

//  insert postsynaptic mechansisms

objectvar c
c = new GABAa()			// create synapse
POST c.loc(0.5)			// assign postsynaptic compartment
setpointer c.pre, PRE.v(0.5)	// assign presynaptic compartment

Cmax_GABAa	= 1	//	(mM)	 max transmitter concentration
Cdur_GABAa	= 1	//	(ms)	 transmitter duration (rising phase)
Alpha_GABAa	= 0.53	//	(/ms mM) forward (binding) rate
Beta_GABAa	= 0.18	//	(/ms)	 backward (unbinding) rate
Erev_GABAa	= -80	//	(mV)	 reversal potential
Prethresh_GABAa	= 0 	//	(mV)	 voltage level nec for release
Deadtime_GABAa	= 1	//	(ms)	 mimimum time between release events
c.gmax		= 0.001	//	(umho)	 maximum conductance

//  add graphs


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