Fluctuating synaptic conductances recreate in-vivo-like activity (Destexhe et al 2001)

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Accession:8115
This model (and experiments) reported in Destexhe, Rudolh, Fellous, and Sejnowski (2001) support the hypothesis that many of the characteristics of cortical neurons in vivo can be explained by fast glutamatergic and GABAergic conductances varying stochastically. Some of these cortical neuron characteristics of fluctuating synaptic origin are a depolarized membrane potential, the presence of high-amplitude membrane potential fluctuations, a low input resistance and irregular spontaneous firing activity. In addition, the point-conductance model could simulate the enhancement of responsiveness due to background activity. For more information please contact Alain Destexhe. email: Destexhe@iaf.cnrs-gif.fr
Reference:
1 . Destexhe A, Rudolph M, Fellous JM, Sejnowski TJ (2001) Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107:13-24 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell;
Channel(s): I Na,t; I K; I M;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Simplified Models; Synaptic noise;
Implementer(s): Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; GabaA; AMPA; I Na,t; I K; I M;
/*----------------------------------------------------------------------------

	Neuron demo file to simulate fluctuating conductances
	-----------------------------------------------------

	- single-compartment with "Gfluct" point-process	
	- addition of Na/K currents for action potentials
	- spontaneous firing
	- Gfluct2 process
        - fluctuations comparable to layer6 model with correl=0.7 


Reference: 

  Destexhe, A., Rudolph, M., Fellous, J-M. and Sejnowski, T.J.  
  Fluctuating synaptic conductances recreate in-vivo--like activity in
  neocortical neurons. Neuroscience 107: 13-24 (2001).

  (electronic copy available at http://cns.iaf.cnrs-gif.fr)



	Written by A. Destexhe, 1999

----------------------------------------------------------------------------*/



//----------------------------------------------------------------------------
//  load and define general graphical procedures
//----------------------------------------------------------------------------

load_file("nrngui.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()
	g[ii].size(0,tstop,$2,$3)
	g[ii].xaxis()
	g[ii].yaxis()
	g[ii].addvar($s1,1,0)
	g[ii].save_name("graphList[0].")
	graphList[0].append(g[ii])
}

proc makegraph() { local ii	// define subroutine to add a new graph
				// makeraph("variable", xmin,xmax,ymin,ymax)
	ngraph = ngraph+1
	ii = ngraph-1
	g[ii] = new Graph()
	g[ii].size($2,$3,$4,$5)
	g[ii].xaxis()
	g[ii].yaxis()
	g[ii].addvar($s1,1,0)
	g[ii].save_name("graphList[0].")
	graphList[0].append(g[ii])
}

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



//----------------------------------------------------------------------------
//  general parameters
//----------------------------------------------------------------------------

dt=0.5
npoints = 2000

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

objectvar Exp , Pow, Theor		// create vectors of data points
Exp = new Vector(npoints)
Theor = new Vector(npoints)
Pow = new Vector(npoints/2)




//----------------------------------------------------------------------------
//  create a compartment and insert passive properties
//----------------------------------------------------------------------------

create soma

soma {
	L = 105			// size to get similar Rin as Layer VI cell
	diam = 105
}

leak_cond = 4.52e-5
leak_rev = -80			// D & P 1999
capacit = 1
axial_res = 250

forall { 			// insert passive currents
	insert pas
	g_pas = leak_cond
	e_pas = leak_rev
	cm = capacit
	Ra = axial_res
	L = L
}




//----------------------------------------------------------------------------
//  insert active properties
//----------------------------------------------------------------------------

DEBUG=0

soma {				// insert voltage-dependent currents INa, IKd, IM
	   insert inaT
	   ena = 50
	   vtraub_inaT = -63			// threshold of -55
	   gnabar_inaT = 20e-4			// density for soma

	   insert ikdT
	   ek = -90
	   vtraub_ikdT = -63			// threshold of IKd
	   gkbar_ikdT = 200e-4			// density for soma

	   insert imZ
	   ek = -90
	   gkbar_imZ = 3e-4			// density for soma
}   


//
// set the values of voltage-dependent conductances:
// - Na channels like Magee-Johnston
// - IM set to repetitive firing at the right frequency
//

corrJ = 4.3	// Johnston correction factor for Na conductance

soma {
	gnabar_inaT = corrJ*120e-4	// sodium channels
	gkbar_ikdT = 100e-4		// delayed rectifier
	gkbar_imZ = 5e-4		// M-channels
}

forall { 
  shift_inaT = -10		// inactivation around -52 mV
  vtraub_inaT = -63		// was -50 mV before
  vtraub_ikdT = -63
}






//----------------------------------------------------------------------------
//  insert Gfluct process
//----------------------------------------------------------------------------

access soma

objref fl
fl = new Gfluct2(0.5)

fl.std_e = 0.012		// 4 times larger
fl.std_i = 0.0264


proc make_Fpanel() {			// make panel
	xpanel("Fluctuating Conductance model")
	xpvalue("E_e",&fl.E_e)
	xpvalue("E_i",&fl.E_i)
	xpvalue("g_e0",&fl.g_e0)
	xpvalue("g_i0",&fl.g_i0)
	xpvalue("std_e",&fl.std_e)
	xpvalue("std_i",&fl.std_i)
	xpvalue("tau_e",&fl.tau_e)
	xpvalue("tau_i",&fl.tau_i)
	xbutton("Run","run()")
	xpanel()
}

make_Fpanel()





//----------------------------------------------------------------------------
//  create graphs
//----------------------------------------------------------------------------


addgraph("soma.v(0.5)",-80,40)

ymin = 0	// min-max values
ymax = 0.15

addgraph("fl.g_e",ymin,ymax)

addgraph("fl.g_i",ymin,ymax)




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