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;
TITLE KCa channels for LGMD SFA

UNITS {
	(mV) = (millivolt)
	(mA) = (milliamp)
	(S) = (siemens)
	(molar) = (1/liter)
	(mM) = (millimolar)
}

NEURON {
	THREADSAFE
    SUFFIX KCa

    USEION ca READ cai
    USEION k READ ek WRITE ik
    RANGE gmax, g
    GLOBAL pwr, tau, kD_ca
}

PARAMETER {
    gmax = 0.01 (S/cm2)
    kD_ca = 0.035 (mM)
    tau = 1 (ms)
    minca = 5e-4 (mM)
    pwr = 1 (1)
}

ASSIGNED {
    v (mV)
    cai (mM)
    ek (mV)
    
    ik (mA/cm2)
    g  (S/cm2)
}

STATE {
    n
}

INITIAL {
    n = (cai-minca)/(cai+kD_ca)
}

BREAKPOINT {
	SOLVE state METHOD derivimplicit
	:n = cai/(cai+kD_ca)
	:ikca = gmax*n*(v-ek)
	if (cai < minca) {
		n=0
	}
    g = n^pwr*gmax
    ik = g*(v-ek)
}

DERIVATIVE state {
	n' = ((cai-minca)/(cai+kD_ca) - n)/tau
}










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