Hodgkin-Huxley models of different classes of cortical neurons (Pospischil et al. 2008)

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Accession:123623
"We review here the development of Hodgkin- Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are 'fast spiking', 'regular spiking', 'intrinsically bursting' and 'low-threshold spike' cells. For each class, we fit 'minimal' HH type models to experimental data. ..."
Reference:
1 . Pospischil M, Toledo-Rodriguez M, Monier C, Piwkowska Z, Bal T, Frégnac Y, Markram H, Destexhe A (2008) Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons. Biol Cybern 99:427-41 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Channel/Receptor;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s): I Na,t; I L high threshold; I T low threshold; I K; I M;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Parameter Fitting; Simplified Models;
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; I Na,t; I L high threshold; I T low threshold; I K; I M;
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PospischilEtAl2008
README.html *
cadecay_destexhe.mod *
HH_traub.mod *
IL_gutnick.mod
IM_cortex.mod *
IT_huguenard.mod *
demo_IN_FS.hoc *
demo_PY_IB.hoc *
demo_PY_IBR.hoc *
demo_PY_LTS.hoc *
demo_PY_RS.hoc *
fig5b.jpg *
mosinit.hoc *
rundemo.hoc *
sIN_template *
sPY_template *
sPYb_template *
sPYbr_template *
sPYr_template *
                            
/*----------------------------------------------------------------------------

	Simplified model of bursting cortical neuron
	============================================

        Single-compartment model of bursting pyramidal neurons.
	The model is based on the presence of four
        voltage-dependent currents: 
        - INa, IK: action potentials
        - IM: slow K+ current for spike-frequency adaptation
        - IL: L-type calcium currents for burst generation

	Repetitive bursting with stronger IL

  Model described in:

   Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z., 
   Bal, T., Fregnac, Y., Markram, H. and Destexhe, A.
   Minimal Hodgkin-Huxley type models for different classes of
   cortical and thalamic neurons.
   Biological Cybernetics 99: 427-441, 2008.


        Alain Destexhe, CNRS, 2009
	http://cns.iaf.cnrs-gif.fr

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


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

load_file("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()
	g[ii].size(tstart,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 addtext() { local ii	// define subroutine to add a text graph
				// addtext("text")
	ngraph = ngraph+1
	ii = ngraph-1
	g[ii] = new Graph()
	g[ii].size(0,tstop,0,1)
	g[ii].xaxis(3)
	g[ii].yaxis(3)
	g[ii].label(0.1,0.8,$s1)
	g[ii].save_name("graphList[0].")
	graphList[0].append(g[ii])
	text_id = ii
}

proc addline() {		// to add a comment to the text window
				// addline("text")
	g[text_id].label($s1)
}


if(ismenu==0) {
  nrnmainmenu()			// create main menu
  nrncontrolmenu()		// crate control menu
}


//----------------------------------------------------------------------------
//  transient time
//----------------------------------------------------------------------------

trans = 0000

print " "
print ">> Transient time of ",trans," ms"
print " "









//----------------------------------------------------------------------------
//  create PY cells
//----------------------------------------------------------------------------

print " "
print "<<==================================>>"
print "<<            CREATE CELLS          >>"
print "<<==================================>>"
print " "

xopen("sPYbr_template")		// read geometry file

ncells = 1			// nb of cells in each layer <<>>

objectvar PY[ncells]
for i=0,ncells-1 {
  PY[i] = new sPYbr()
}









//----------------------------------------------------------------------------
//  Rebound parameters
//----------------------------------------------------------------------------

// L-current density:
// 0.0001 -> RS behavior
// 0.00017 -> one burst, then RS
// 0.0002 -> two bursts, then RS
// 0.00022 -> repetitive bursting

PY[0].soma {
	gcabar_ical = 2.2e-4
	gkbar_im = 3e-5
	g_pas = 1e-5
	e_pas = -85
	gnabar_hh2 = 0.05
	gkbar_hh2 = 0.005
}






//----------------------------------------------------------------------------
//  insert electrode in each PY cell
//----------------------------------------------------------------------------

if(ismenu==0) {
  load_file("electrod.hoc")	// electrode template
  ismenu = 1
}

objectvar El[ncells]			// create electrodes

CURR_AMP = 0.15

for i=0,ncells-1 {			// insert one in each cell
	PY[i].soma El[i] = new Electrode()
	PY[i].soma El[i].stim.loc(0.5)
	El[i].stim.del = 500
	El[i].stim.dur = 2000
	El[i].stim.amp = CURR_AMP
}


electrodes_present=1




//----------------------------------------------------------------------------
//  setup simulation parameters
//----------------------------------------------------------------------------

Dt = .1				// macroscopic time step <<>>
npoints = 30000

dt = 0.1			// must be submultiple of Dt
tstart = trans
tstop = trans + npoints * Dt
runStopAt = tstop
steps_per_ms = 5
celsius = 36
v_init = -84






//----------------------------------------------------------------------------
//  add graphs
//----------------------------------------------------------------------------

strdef gtxt

if(batch == 0) {
  addgraph("PY[0].soma.m_im",0,1)
//  addgraph("PY[0].soma.m_iahp",0,1)
  for i=0,ncells-1 {
	sprint(gtxt,"PY[%d].soma.v(0.5)",i)
	addgraph(gtxt,-120,40)
  }
}





//----------------------------------------------------------------------------
//  add text
//----------------------------------------------------------------------------

access PY[0].soma

proc text() {
  sprint(gtxt,"%d PY cells",ncells)
  addtext(gtxt)
  sprint(gtxt,"Passive: gleak=%g Eleak=%g",PY.soma.g_pas,PY.soma.e_pas)
  addline(gtxt)
  sprint(gtxt,"HH: gNa=%g, gK=%g, vtraub=%g",PY.soma.gnabar_hh2,\
  PY.soma.gkbar_hh2,PY.soma.vtraub_hh2)
  addline(gtxt)
  sprint(gtxt,"IM: g=%g, taumax=%g",PY.soma.gkbar_im,taumax_im)
  addline(gtxt)
  sprint(gtxt,"Ca++: tau=%g, depth=%g, cainf=%g",taur_cad,depth_cad,cainf_cad)
  addline(gtxt)
  sprint(gtxt,"IL: g=%g",PY.soma.gcabar_ical)
  addline(gtxt)
}