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 cAMP influx based on cai
COMMENT

	Ca++ binding on second messenger
		p0 (inactive) + nca Ca <-> p1 (active)	; rate cst k1,k2

ENDCOMMENT

UNITS {
    (molar) = (1/liter)
    (mM) = (millimolar)
}

NEURON {
	THREADSAFE
    SUFFIX CN
    USEION ca READ cai
	USEION cn WRITE cni VALENCE 1
    GLOBAL tau
}

PARAMETER {
	: all values can be adjusted in hoc files
	tau_ca = 1500	(ms)
	tau = 6000		(ms)
	cn_init = 2e-5	(mM)
	kD = 0.05		(1)
	minca = 1.5e-4	(mM)
}

ASSIGNED {
    cai (mM)
    cinf (mM)
}

STATE {
	cni (mM)
}

INITIAL {
    cni = cn_init
}


BREAKPOINT {
	SOLVE state METHOD derivimplicit
}


DERIVATIVE state { LOCAL dcn

	influx(cai)
	:dcn = cinf/tau_ca - cni/tau
	cni' = cinf/tau_ca - cni/tau
}


PROCEDURE influx( cai(mM) ) {
	:TABLE cinf DEPEND minca, kD
	:      FROM 0 TO 0.02 WITH 1000
    
	if (cai < minca) {
		cinf = 0
	} else {
		cinf = (cai-minca)*kD
	}
}






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