Cortical feedback alters visual response properties of dLGN relay cells (Martínez-Cañada et al 2018)

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Accession:239878
Network model that includes biophysically detailed, single-compartment and multicompartment neuron models of relay-cells and interneurons in the dLGN and a population of orientation-selective layer 6 simple cells, consisting of pyramidal cells (PY). We have considered two different arrangements of synaptic feedback from the ON and OFF zones in the visual cortex to the dLGN: phase-reversed (‘push-pull’) and phase-matched (‘push-push’), as well as different spatial extents of the corticothalamic projection pattern. This project is the result of a research work and its associated publication is: (Martínez-Cañada et al 2018). Installation instructions as well as the latest version can be found in the Github repository: https://github.com/CINPLA/biophysical_thalamocortical_system
Reference:
1 . Martínez-Cañada P, Mobarhan MH, Halnes G, Fyhn M, Morillas C, Pelayo F, Einevoll GT (2018) Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells. PLoS Comput Biol 14:e1005930 [PubMed]
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):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: LFPy; NEURON; NEST; Python;
Model Concept(s): Vision;
Implementer(s): Martínez-Cañada, Pablo [pablomc at ugr.es];
/
Biophysical_thalamocortical_system
cortex_neurons
.README.swp
README *
cadecay.mod *
hh2.mod
IM.mod *
IT.mod *
demo_IN_FS.oc *
demo_PY_LTS.oc *
demo_PY_RS.oc *
mosinit.hoc *
rundemo.hoc *
sIN_template
soma.hoc *
sPY_template
sPYr_template
                            
//----------------------------------------------------------------------------
//  define a panel to run the different demos
//----------------------------------------------------------------------------

proc make_demopanel() {
	xpanel("Simulations of cortical cells")
	xradiobutton("Regular-spiking pyramidal cell","restart(\"demo_PY_RS\")")
	xradiobutton("Bursting pyramidal cell","restart(\"demo_PY_LTS\")")
        xradiobutton("Fast-spiking interneuronl","restart(\"demo_IN_FS\")")
	xpanel(20,100)
}

proc restart() {local i

//	if (name_declared("IN") == 2) { objref IN[1] }
//	if (name_declared("PY") == 2) { objref PY[1] }
//	if (name_declared("IN") == 2) { objref IN[1] }
//	if (name_declared("El") == 2) { objref El[1] }
	ismenu = 0

	if (electrodes_present) {
		destroy_elec()
	}

	forall delete_section()

	for i=0, n_graph_lists-1 {
		graphList[i].remove_all()
	}
	flush_list.remove_all()
	fast_flush_list.remove_all()
	doNotify()
	for (i= PWManager[0].count-1; i >= pwmcnt; i -= 1) {
		PWManager[0].close(i)
		doNotify()
	}
	stoprun = 0
	cvode_active(0)

	ismenu=0
	
	sprint(tstr, "%s.oc", $s1)
	load_file(1, tstr)
}


ismenu=0

load_file("nrngui.hoc")

strdef tstr
ncells=1
objref El[ncells]

electrodes_present=0	// after electrodes are created they must be
			// destroyed if simulation restarted

proc destroy_elec() {
	execute("objref stim, vc",El[0])
}

pwmcnt = PWManager[0].count  // the initial GUIs should not be dismissed

make_demopanel()

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