A model of slow motor unit (Kim, 2017)

 Download zip file   Auto-launch 
Help downloading and running models
Cav1.3 channels in motoneuron dendrites are actively involved during normal motor activities. To investigate the effects of the activation of motoneuron Cav1.3 channels on force production, a model motor unit was built based on best-available data. The simulation results suggest that force potentiation induced by Cav1.3 channel activation is strongly modulated not only by firing history of the motoneuron but also by length variation of the muscle as well as neuromodulation inputs from the brainstem.
1 . Kim H (2017) Muscle length-dependent contribution of motoneuron Cav1.3 channels to force production in model slow motor unit. J Appl Physiol (1985) 123:88-105 [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): Spinal cord lumbar motor neuron alpha ACh cell; Skeletal muscle cell;
Channel(s): I Calcium; I Potassium; I Sodium; I_AHP;
Gap Junctions:
Gene(s): Cav1.3 CACNA1D;
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites;
Implementer(s): Kim, Hojeong [hojeong.kim03 at gmail.com];
Search NeuronDB for information about:  Spinal cord lumbar motor neuron alpha ACh cell; I Sodium; I Calcium; I Potassium; I_AHP;
Ca_conc.mod *
CaL.mod *
CaN.mod *
KCa.mod *
KDr.mod *
module1_2.mod *
module3.mod *
Naf.mod *
Nap.mod *
Xm.mod *
add_hil_is.hoc *
add_muscle_unit.hoc *
CaL_PICs.hoc *
fixnseg.hoc *
mem_mechanism_acti.hoc *
mem_mechanism_muscle.hoc *
mem_mechanism_pass.hoc *
v_e_moto6_export.hoc *
Xm.hoc *
/* Sets nseg in each section to an odd value
   so that its segments are no longer than 
     d_lambda x the AC length constant
   at frequency freq in that section.

   Be sure to specify your own Ra and cm before calling geom_nseg()

   To understand why this works, 
   and the advantages of using an odd value for nseg,
   see  Hines, M.L. and Carnevale, N.T.
        NEURON: a tool for neuroscientists.
        The Neuroscientist 7:123-135, 2001.

// these are reasonable values for most models
freq = 100      // Hz, frequency at which AC length constant will be computed
d_lambda = 0.1

func lambda_f() { local i, x1, x2, d1, d2, lam
        if (n3d() < 2) {
                return 1e5*sqrt(diam/(4*PI*$1*Ra*cm))
// above was too inaccurate with large variation in 3d diameter
// so now we use all 3-d points to get a better approximate lambda
        x1 = arc3d(0)
        d1 = diam3d(0)
        lam = 0
        for i=1, n3d()-1 {
                x2 = arc3d(i)
                d2 = diam3d(i)
                lam += (x2 - x1)/sqrt(d1 + d2)
                x1 = x2   d1 = d2
        //  length of the section in units of lambda
        lam *= sqrt(2) * 1e-5*sqrt(4*PI*$1*Ra*cm)

        return L/lam

proc geom_nseg() {
  soma area(0.5) // make sure diam reflects 3d points
  forall { nseg = int((L/(d_lambda*lambda_f(freq))+0.9)/2)*2 + 1  }


Loading data, please wait...