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];
proc celldef() {
  topol()
  subsets()
  geom()
  biophys()
  geom_nseg()
}

create soma, dend[4]


proc topol() { local i
  connect dend(0), soma(1)
  for i = 1, 3 connect dend[i](0), soma(1)
  basic_shape()
}
proc shape3d_1() {
  soma {pt3dclear()
	pt3dadd(0, 0, -15.3147, 8.72205)
	pt3dadd(0, 0, 0, 8.72205)
  }
  dend {pt3dclear()
	pt3dadd(0, 0, 0, 4.00)
	pt3dadd(-100, 0, 0, 0.30)
	pt3dadd(-500, 0, 0, 0.30)
  }
  dend[1] {pt3dclear()
	pt3dadd(0, 0, 0, 4.00)
	pt3dadd(100, 0, 0, 0.30)
	pt3dadd(500, 0, 0, 0.30)
  }
  dend[2] {pt3dclear()
	pt3dadd(0, 0, 0, 4.00)
	pt3dadd(100*cos(0.15), 00, 100*sin(0.15), 0.30) 
	pt3dadd(500*cos(0.15), 0, 500*sin(0.15), 0.30)
  }
  dend[3] {pt3dclear()
	pt3dadd(0, 0, 0, 4.00)
	pt3dadd(-100*cos(0.15), 00, 100*sin(0.15), 0.30) 
	pt3dadd(-500*cos(0.15), 0, 500*sin(0.15), 0.30)
  }

}
proc basic_shape() {
  shape3d_1()
}

objref all
proc subsets() { local i
  objref all
  all = new SectionList()
    soma all.append()
    for i=0, 3 dend[i] all.append()

}
proc geom() {
}
proc geom_nseg() {
}
proc biophys() {
}
access soma

celldef()

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