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 IM channel for LGMD


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

: gmax and g are range variables (i.e., can change in different compartments
NEURON {
    THREADSAFE
    : note - every variable accessible in NEURON will have the suffix _M
    SUFFIX M

    USEION k READ ek WRITE ik

    : these variables can be accessed as compartment.rangevar_M
    RANGE vhalf, gmax, tau, g

    : these will be accessed as taumax_M, vhalf_M, s1_M, s2_M  
    GLOBAL taumax, taumin, s1, s2
}

PARAMETER {
    gmax= 0.0003 (S/cm2)

    vhalf = -50 (mV)	: half activation
    s1 = 15		(mV)	: steepness of activation
	s2 = -20	(mV)
	taumax = 80 (ms)
	taumin = 15 (ms)
	aop = 0		(1)	< 0, 1 >	: voltage independent conductance
}

ASSIGNED { 
    v	(mV)
    ek	(mV)
    
    ik	(mA/cm2)
    ninf	(1)
    tau (ms)
    g 	(S/cm2)
}

STATE {
    n
}

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

: calls the function settables below, then 
: set the steady state value of IM activation 
INITIAL {
    settables(vhalf-v)
    n = ninf
}

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


PROCEDURE settables(df (mV)) {
	: tabled relative to vhalf so one table can be used across sections with differing vhalf
    TABLE ninf, tau DEPEND s1, s2, taumax, taumin
          FROM -100 TO 100 WITH 600

    : steady-state activation of IM in mV
    ninf = 1/(1+(exp((df)/s1)))

    : steady-state IM time constant
    :tau = 4*taumax/(1+exp((df)/s2) )* ninf
    tau = 4*(taumax-taumin)/(1+exp((df)/s2))*ninf+taumin
}



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