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 Ih channel for LGMD
: this channel is identical to that implemented in Matlab code
: based on Richard's current and voltage clamp data (Sept 13)
: this is a second version were the state variables are ninf and tau 
: instead of nalpha and nbeta to allow for inspection of these variables 
: during simulations
: Modified on 04/08/14 to correct the apparent mix-up between tau and tau_max
: tau_max was RANGE and tau GLOBAL when it should be the opposite. 

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

: gmax and g are range variables (i.e., can change in different compartments
: while e is global
NEURON {
    THREADSAFE
    : note - every variable accessible in NEURON will be having the suffix _h
    SUFFIX h

    NONSPECIFIC_CURRENT i

    : these variables will be accessed as compartment.rangevar_h
    : note: to make the channel constant available add the following
    : to the next line: vhalf, s1, s2, tau_max
    RANGE gmax, tau, g, taumax, i

    : this will be accessed as e_h, taumax_h, vhalf_h, s1_h, s2_h 
    GLOBAL e, taumin, vhalf, s1, s2
}

PARAMETER {
    gmax= 0.001 (S/cm2)
    e = -35 (mV)

    : note the following is only in the case we define these parameters as accessible to 
    : NEURON. Otherwise it is sufficient to initialize them in the procedure settables
    vhalf = -78 (mV)
    s1 = -13 (mV)
    s2 = 14 (mV)
    taumax = 1350 (1)
    taumin = 10 (ms)
}

ASSIGNED { 
    v (mV)

    i (mA/cm2)
    ninf
    tau (ms)
    g (S/cm2)
}

STATE {
    n
}

BREAKPOINT {
    SOLVE states METHOD cnexp
    g  = gmax*n
    i  = gmax*n*(v-e)
}

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

DERIVATIVE states { 
    settables(v)      
    n' = (ninf - n)/(taumax*tau+taumin)
}


PROCEDURE settables(v (mV)) {
    :LOCAL vhalf, s1, s2, taumax
    :local variables take units of right hand side, see below

    TABLE ninf, tau DEPEND vhalf, s1, s2
          FROM -200 TO 50 WITH 750

    : steady-state activation of Ih in mV
    :vhalf = -77.8 (mV)
    :s1 = 13.8 (mV)
    ninf = 1/(1+(exp((vhalf-v)/s1)))

    : steady-state Ih time constant
    : slope in mV and time constant in ms
    :s2 = 19.7 (mV)
    :taumax = 1071.1 (ms)
    :tau = 2*taumax/( exp((v-vhalf)/s2) + exp((vhalf-v)/s2) )
    tau = (4 (ms))/(1+exp((vhalf-v)/s2))*ninf

}



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