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];
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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
                            
/*--------------------------------------------------------------
	TEMPLATE FILE FOR DEFINING CORTINAL INTERNEURONS
	------------------------------------------------

	SIMPLIFIED NEURONS:

	- one compartment model
	- passive
	- HH: Traub

	Alain Destexhe, Laval University, 1995

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



begintemplate sIN		// create a new template object
public soma,all
objref all

create soma[1]


proc init() { local v_potassium, v_sodium

  v_potassium = -100		// potassium reversal potential 
  v_sodium = 50			// sodium reversal potential 

  soma {
	Ra = 100		// geometry 
	nseg = 1
	diam = 67
	L = 67			// so that area is about 14000 um2
	cm = 1

	insert pas		// leak current 
	e_pas = -70
	// g_pas = 5e-5
	g_pas = 0.00015		// Rin = 48Meg

	// conversion with McC units: 
	// g(S/cm2) = g(nS)*1e-9/29000e-8
	//	    = g(nS) * 3.45e-6

	insert hh2cx		// Hodgin-Huxley INa and IK 
	ek = v_potassium
	ena = v_sodium
	vtraub_hh2cx = -55	// resting Vm, BJ was -55
	gnabar_hh2cx = 0.05	// McCormick=15 muS, thal was 0.09
//	gkbar_hh2cx = 0.007	// McCormick=2 muS, thal was 0.01
//	gkbar_hh2cx = 0.004
	gkbar_hh2cx = 0.01	// spike duration of interneurons

  }

  all = new SectionList()
  soma all.append()

}
endtemplate sIN




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