Drosophila T4 neuron (Gruntman et al 2018)

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Accession:239435
Passive, multi-compartment conductance-based model of a T4 cell. The model reproduces the neuron's response to moving stimuli via integration of spatially offset fast excitatory and slow inhibitory inputs.
Reference:
1 . Gruntman E, Romani S, Reiser MB (2018) Simple integration of fast excitation and offset, delayed inhibition computes directional selectivity in Drosophila. Nat Neurosci 21:250-257 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism: Drosophila;
Cell Type(s): Fly lobula plate T4 neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Invertebrate;
Implementer(s): Gruntman, Eyal [ gruntmane at janelia.hhmi.org]; Romani, Sandro [ romanis at janelia.hhmi.org];
//  This file generates the relevant axis along which dendritic sections are defined.
//  We chose 2 sections manually at the end of the dendrite, along what looked like
// the relevant axis, and each section in the dendrite then gets a score of its distance
// along that axis (x_star).

// coordinates of the two dendritic segments lying at the extremes of the axis of interest
// Define a given somatic section.

Cell[0].dend[229] {
n = n3d()
x0 = ( x3d(0) + x3d(n-1) ) / 2
y0 = ( y3d(0) + y3d(n-1) ) / 2
z0 = ( z3d(0) + z3d(n-1) ) / 2
}

Cell[0].dend[109] {
n = n3d()
x1 = ( x3d(0) + x3d(n-1) ) / 2
y1 = ( y3d(0) + y3d(n-1) ) / 2
z1 = ( z3d(0) + z3d(n-1) ) / 2
}


den = (x0-x1)^2 + (y0-y1)^2 + (z0-z1)^2

// select all the relevant dendritic segments (children of dend[0])
objref RelDend
RelDend = new SectionList()
Cell[0].dend[0] {RelDend.subtree()}


i = 0
forsec RelDend {
    i = i + 1
    }
numRD = i



objref x_star
x_star = new Vector( numRD )




k = 0

// defining x_star based on the physical coordinates of the current section
forsec RelDend {

	n = n3d()
	x = ( x3d(0) + x3d(n-1) ) / 2
	y = ( y3d(0) + y3d(n-1) ) / 2
	z = ( z3d(0) + z3d(n-1) ) / 2

	num = (x0-x1)*(x0-x) + (y0-y1)*(y0-y) + (z0-z1)*(z0-z)
	x_star.x[k] = num / den

	k = k + 1
}

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