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];

strdef fileSt1, fileSt2, fileName

v_init = -65 // used for initial conditions
tstop = 650 // in msec, stop time
Vleak=-65
seed = 300

{
	load_file("nrngui.hoc")
}

// LOAD MORPHOLOGY.
{
	load_file("loadMorph.hoc") // loads cell morphology from swc file
}

segL = 2		// the length (in microns) of each dendritic segment
forall { ns=int(L/segL+0.5)
        if (ns==0) {
             ns=1
        }
        if ((ns-int(ns/2)*2)==0) {
             ns=ns+1
        }
        nseg = ns
}

same_inp = 0 // logical flag. Relevant for the moving bar

fileSt1 = "vm_trace_flash"

zero_inhib = 0// logical flag. Determines whether inhibitory synapses would be active
// if 1 inhibition is zeroed. Used in setting_synapse_parameters_mov
if (zero_inhib == 1) {
	fileSt2 = "noI_T.dat"
} else {
	fileSt2 = "noI_F.dat"
}

sprint(fileName, "%s-%s", fileSt1, fileSt2)

// creating time vector
objref tvec
vecsize = tstop

tvec = new Vector( vecsize , 0 )
for i=0, vecsize-2 {
    tvec.x[i+1] = tvec.x[i] + 1

}



objref params, f2
params = new Vector()
f2 = new File()
f2.ropen("parameters.dat")
params.scanf(f2)
f2.close()

// parameters.dat contains the 12 model parameters that are described in Supp. Table 1

taur_e = params.x[0] // first time constant for excitatory conductance
taud_e = params.x[1] // second time constant for excitatory conductance
taur_i = params.x[2] // first time constant for inhibitory conductance
taud_i = params.x[3] // second time constant for inhibitory conductance
a_e = params.x[4] // gaussian amplitude for excitatory synapse weights
mu_e = params.x[5] // gaussian center for excitatory synapse weights
sigma_e = params.x[6] // gaussian spread for excitatory synapse weights
a_i = params.x[7] // gaussian amplitude for inhibitory synapse weights
mu_i = params.x[8] // gaussian center for inhibitory synapse weights
sigma_i = params.x[9] // gaussian spread for inhibitory synapse weights
Rm = params.x[10] // membrane resistivity
global_ra = params.x[11] // axial resistivity

t0 = 0

forall {
	insert pas  g_pas=1/(Rm)  Ra=global_ra  e_pas=Vleak

}

{

	// function that is used to create the conductance vectors at each synapse
	load_file("create_cond_vec.hoc")
	// defines the relevant dendritic axis and generates a value for each
	// section based on its projected location along that axis
	load_file("define_dend_axis.hoc")
	// divides the dendrite into equal intervals. All the synapses within an
	// interval are activated when a simulated bar is flashed in that position
	load_file("setting_x_intervals.hoc")

}


objref vm_trace, time_trace
vm_trace = new Vector()
time_trace = new Vector()



// single flash durations (used to generate conductance vectors)
objref t_stim
t_stim = new Vector(4)
t_stim.x[0] = 20
t_stim.x[1] = 40
t_stim.x[2] = 80
t_stim.x[3] = 160


objref f1, ftime1
f1 = new File()
f1.wopen(fileName)
ftime1 = new File()
ftime1.wopen("time_trace_flash.dat")



for ind_t_stim = 0,3 { // over all durations
	t1 = t0 + t_stim.x[ind_t_stim]

	for ind_x_stim = 0,10 { // over all positions

		load_file(1,"setting_synapse_parameters_flash.hoc") //"1" forces file reloading
		vm_trace.record( &Cell[0].soma[0].v(0.5) )
		time_trace.record( &t )

		{
			dt = 0.1
			steps_per_ms = 10

			init()

			run()
		}
		// concatenates the results from each iteration
		vm_trace.printf(f1)
		time_trace.printf(ftime1)
	}

}
ftime1.close()
f1.close()

quit()

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