LGMD impedance (Dewell & Gabbiani 2019)

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Accession:256024
"How neurons filter and integrate their complex patterns of synaptic inputs is central to their role in neural information processing . Synaptic filtering and integration are shaped by the frequency-dependent neuronal membrane impedance. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper, Schistocerca americana. This neuron, the lobula giant movement detector (LGMD), exhibits consistent impedance properties across frequencies and membrane potentials. Two common active conductances gH and gM, mediated respectively by hyperpolarization-activated cyclic nucleotide gated (HCN) channels and by muscarine sensitive M-type K+ channels, promote broadband integration with high temporal precision over the LGMD's natural range of membrane potentials and synaptic input frequencies. Additionally, we found that a model based on the LGMD's branching morphology increased the gain and decreased the delay associated with the mapping of synaptic input currents to membrane potential. More generally, this was true for a wide range of model neuron morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. These findings show the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration."
Reference:
1 . Dewell RB, Gabbiani F (2019) Active membrane conductances and morphology of a collision detection neuron broaden its impedance profile and improve discrimination of input synchrony. J Neurophysiol [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Locust Lobula Giant Movement Detector (LGMD) neuron;
Channel(s): I h; I M;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Synaptic Integration; Membrane Properties;
Implementer(s): Dewell, Richard Burkett [dewell at bcm.edu];
Search NeuronDB for information about:  I M; I h;
TITLE K-D channel from RBD

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

NEURON {
    THREADSAFE
    : note - every variable accessible in NEURON will be having the suffix _KD

        SUFFIX KD_ca2
        USEION ca READ cai
        USEION k READ ek WRITE ik
        RANGE gmax, g, taun, taul, kD_ca, l
        GLOBAL vhalfn, tnmax,tlmax, tnmin, tauc, cap
}

PARAMETER {
	gmax=0.01 (mho/cm2)
	vhalfn=-45	(mV)
	vn2=-60		(mV)
	zn=7.0		(mV)
	tnmax=100	(ms)
	tnmin=3.0	(ms)
	tns=-7.5	(mV)
	
	vhalfl=-66	(mV)
	zl=-2.0		(mV)
	tlmax=820	(ms)
	tlmin=20	(ms)
	tauc=30		(ms)
	vl2=-63		(mV)
	tls=17		(mV)
	kD_ca = 0.0001 (mM)
	cap = 0.6
	lcp=6
}

STATE {
        n
        lv
        lc
}

ASSIGNED {
    v (mV)
    ek (mV)
    cai (mM)
    
	ik (mA/cm2)
	l
	ninf
	linf
	lcinf
	taul (ms)
	taun (ms)
	g (S/cm2)
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	l= (1-(1-lc)*(1-lv))*cap+(1-cap)*lv
	g = gmax*n*l
	ik = g*(v-ek)
}

INITIAL {
	rates(v)
	n=ninf
	lv = linf
	lci(cai)
	lc = lcinf
	l= (1-(1-lc)*(1-lv))*cap+(1-cap)*lv
}


FUNCTION alpn(v(mV)) {
  alpn = exp((vhalfn-v)/zn)
}

FUNCTION betn(v(mV)) {
  betn = exp((vn2-v)/tns) 
}

FUNCTION alpl(v(mV)) {
  alpl = exp((vhalfl-v)/zl)
}

DERIVATIVE states {  
	rates(v)
	n' = (ninf - n)/taun
	lv' = (linf - lv)/taul
	lci(cai)
	lc' = (lcinf - lc)/tauc
}

PROCEDURE rates(v (mV)) { :callable from hoc
	LOCAL a
	TABLE ninf, taun, linf, taul DEPEND vhalfn, tlmax, tnmax, tnmin
          FROM -100 TO 50 WITH 600
    
	a = alpn(v)
	ninf = 1/(1 + a)
	:taun = tnmax/(1+betn(v))*ninf+tnmin
	taun = 4*(tnmax-tnmin)/(1+betn(v))*ninf+tnmin
	a = alpl(v)
	linf = (1/(1+ a))
	:taul = 4*(tlmax-tlmin)/(1+exp((vhalfl-v)/tls))*ninf + tlmin
	taul = 2*tlmax/( exp((v-vl2)/tls) + exp((vl2-v)/tls) ) + tlmin
}

PROCEDURE lci(cai (mM)) { :callable from hoc
	TABLE lcinf DEPEND lcp, kD_ca
          FROM 0 TO 0.01 WITH 1000
    
    lcinf = (1-(cai/(cai+kD_ca))^(lcp/2))^lcp
    
}

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