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

 Download zip file   Auto-launch 
Help downloading and running models
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;
TITLE KdrF channel for LGMD
: RBD

UNITS {
    (mV) = (millivolt)
    (mA) = (milliamp)
	(S) = (siemens)
}

NEURON {
    THREADSAFE
    SUFFIX KdrF
    USEION k READ ek WRITE ik
    RANGE gmax, g, t1, t2	: Range variables can differ across neuron (non constants)
    GLOBAL vhalf
}

PARAMETER {
    gmax= 0.008 (mho/cm2)
    
    vhalf=-39	(mV)
	vn2=-40	(mV)
	vl=-55	(mV)
	t1=2.7	(ms)
	t2=0.1	(ms)
	tns=7	(mV)
	zn=9	(mV)
	zl=-9	(mV)
}

ASSIGNED { 
    v (mV)
    ek (mV)
    
    ik (mA/cm2)
	ninf
    ntau (ms)
    g (S/cm2)
}

STATE {
    n
}

BREAKPOINT {
    SOLVE states METHOD cnexp
    g  = gmax*n
    ik  = g*(v-ek)

}

INITIAL {
    settables(v)
    n = ninf
}

DERIVATIVE states {  
    settables(v)    
    n' = (ninf - n)/ntau
}

UNITSOFF

PROCEDURE settables(v (mV)) {
	TABLE ninf, ntau DEPEND vhalf, t1
          FROM -100 TO 50 WITH 1500

	ninf = 1/(1 + exp((vhalf-v)/zn))^2
	ntau = 2*t1/(1+exp((vn2-v)/tns))/(1+ exp((vl-v)/zl))+t2
	:ntau = 4*t1/(1+exp((vn2-v)/tns))*ninf+t2

}

UNITSON



Loading data, please wait...