Thalamic Reticular Network (Destexhe et al 1994)

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Accession:3670
Demo for simulating networks of thalamic reticular neurons (reproduces figures from Destexhe A et al 1994)
Reference:
1 . Destexhe A, Contreras D, Sejnowski TJ, Steriade M (1994) A model of spindle rhythmicity in the isolated thalamic reticular nucleus. J Neurophysiol 72:803-18 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Thalamus;
Cell Type(s): Thalamus reticular nucleus GABA cell;
Channel(s): I T low threshold; I K,Ca; I CAN;
Gap Junctions:
Receptor(s): GabaA; GabaB;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Temporal Pattern Generation; Oscillations; Tutorial/Teaching; Sleep; Calcium dynamics; Spindles;
Implementer(s): Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr];
Search NeuronDB for information about:  Thalamus reticular nucleus GABA cell; GabaA; GabaB; I T low threshold; I K,Ca; I CAN;
/*----------------------------------------------------------------------------

	demo file
	---------

	Simulations of networks using the object-oriented ability of NEURON

	2 neurons reciprocally connected with GABA-B synapses

	Idem Fig.13A of the J Neurophysiol. paper



Reference:

Destexhe, A., Contreras, D., Sejnowski, T.J. and Steriade, M.  A model of
spindle rhythmicity in the isolated reticular thalamus. Journal of 
Neurophysiology 72: 803-818, 1994.

See also:

  http://www.cnl.salk.edu/~alain
  http://cns.fmed.ulaval.ca

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







//----------------------------------------------------------------------------
//  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()
	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])
}

if(ismenu==0) {
  nrnmainmenu()			// create main menu
  nrncontrolmenu()		// create control menu
  ismenu=1
}







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

dt=0.1
tstop = 7000
runStopAt = tstop
steps_per_ms = 5
celsius = 36
v_init = -70









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

load_file("RE.tem")			// load template for RE cell

ncells = 2				// sets the number of cells

objectvar RE[ncells]			// create an array of object variables

for(i=0; i<ncells; i=i+1) {
  RE[i] = new REcell()			// create RE cells from template
}










//----------------------------------------------------------------------------
//  create connections
//----------------------------------------------------------------------------


neigh = 1				// number of neighbors connected 
					// neigh=1 interconnections
					// neigh=2 idem + self synapse

objectvar syn[ncells][ncells]		// create object variables for synapses

for(i=0; i<ncells; i=i+1) {		// scan over each pair of cells
  nv = 0
  for(j=0; j<ncells; j=j+1) {
     if( (i==j) && (neigh==1) ) {
	print "<< no self synapse in cell ",i," >>"
     } else {
	print "<< GABAB1 synapse number ",nv," from cell ",i," to cell ",j," >>"
	syn[i][nv] = new GABAB1()
					// postsynaptic is soma of RE[j]
	RE[j].soma syn[i][nv].loc(0.5)

					// presynaptic  is soma of RE[i]
	setpointer syn[i][nv].pre, RE[i].soma.v(0.5)
	nv = nv + 1
     }
  }
}

print " "






//----------------------------------------------------------------------------
//  create procedure for setting the synaptic weights
//----------------------------------------------------------------------------

proc assign_synapses() {		// assign a value to all synapses
  for(i=0; i<ncells; i=i+1) {		// scan over each cell
    for(j=0; j<neigh; j=j+1) {		// scan over each connection
  	  syn[i][j].gmax = $1		// assign value of gmax
    }
  }
}


assign_synapses(0.01)			// assign value (in uS) to each synapse
					// sum of all synapses must be of 0.01uS 
					// for each cell










//----------------------------------------------------------------------------
//  insert random current pulse in each neuron
//----------------------------------------------------------------------------

objectvar RG,Pulse[ncells]			// create object variables

RG = new Random()				// create random generator

for (i=0; i<ncells; i=i+1) {
	print "defining current pulse in RE[",i,"]"

	RE[i].soma Pulse[i] = new IClamp(.5)		// create current stim
// note: for older versions of NEURON, please use PulseStim instead of IClamp

	Pulse[i].dur = 200				// fixed duration
	Pulse[i].amp = RG.uniform(0,-0.05)		// random amplitude
	Pulse[i].del = RG.uniform(0,2000)		// random latency
}







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

print " "
print " << Creating graphics ... >>"
print " "

for(i=0; i<ncells; i=i+1) {
    addgraph("RE[i].soma.v(0.5)",-100,20)
}