Distinct current modules shape cellular dynamics in model neurons (Alturki et al 2016)

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Accession:223649
" ... We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. ..."
Reference:
1 . Alturki A, Feng F, Nair A, Guntu V, Nair SS (2016) Distinct current modules shape cellular dynamics in model neurons. Neuroscience 334:309-331 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Hippocampus; Amygdala;
Cell Type(s): Abstract single compartment conductance based cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Simplified Models; Activity Patterns; Oscillations; Methods; Olfaction;
Implementer(s):
/
AlturkiEtAl2016
2_Pospischil
Original
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 "rebound bursts" in pyramidal
        neurons (type of cell very common in association areas of
	cortex).  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

  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("sPYb_template")		// read geometry file

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

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









//----------------------------------------------------------------------------
//  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 = 1.7e-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)
}


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