LGMD with 3D morphology and active dendrites (Dewell & Gabbiani 2018)

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Accession:195666
This is a model of the locust LGMD looming sensitive neuron from Dewell & Gabbiani 2018. The morphology was constructed based on 2-photon imaging, and active conductances throughout the neuron were based on sharp electrode recordings in vivo.
Reference:
1 . Dewell RB, Gabbiani F (2018) Biophysics of object segmentation in a collision-detecting neuron. Elife [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Locust Lobula Giant Movement Detector (LGMD) neuron;
Channel(s): I M; I h; Ca pump; I K,Ca; I T low threshold; I_KD;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Synaptic Integration; Spatio-temporal Activity Patterns; Vision;
Implementer(s): Dewell, Richard Burkett [dewell at bcm.edu]; Gabbiani, F;
Search NeuronDB for information about:  I T low threshold; I M; I h; I K,Ca; I_KD; Ca pump;
COMMENT
 an synaptic current with alpha function conductance defined by
         i = g * (v - e)      i(nanoamps), g(microsiemens);
         where
          g = 0 for t < onset and
          g = gmax * (t - onset)/tau * exp(-(t - onset - tau)/tau)
           for t > onset
 this has the property that the maximum value is gmax and occurs at
  t = delay + tau.
 ENDCOMMENT
 					       
 NEURON {
 	POINT_PROCESS AlphaSynapseCa
 	RANGE onset, tau, gmax, e, i
    USEION ca READ ica, eca WRITE ica
 	NONSPECIFIC_CURRENT i
 }
 UNITS {
 	(nA) = (nanoamp)
 	(mV) = (millivolt)
 	(uS) = (microsiemens)
 }
 
 PARAMETER {
 	onset=0 (ms)
 	tau=0.1 (ms)	<1e-3,1e6>
 	gmax=0 	(uS)	<0,1e9>
 	cap=0.05 (1)
 	e=0	(mV)
 }
 
 ASSIGNED {
 	v (mV)
 	i (nA)
 	g (uS)
 	ica (nA)
 	eca (mV)
 }
 
 BREAKPOINT {
 	if (gmax) { at_time(onset) }
 	g = gmax * alpha( (t - onset)/tau )
 	i = g*(v - e)
 	ica = i*cap
 }
 
 FUNCTION alpha(x) {
 	if (x < 0 || x > 10) {
 		alpha = 0
 	}else{
 		alpha = x * exp(1 - x)
 	}
 }

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