Simulations of motor unit discharge patterns (Powers et al. 2011)

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Accession:143671
" ... To estimate the potential contributions of PIC (Persistent Inward Current) activation and synaptic input patterns to motor unit discharge patterns, we examined the responses of a set of cable motoneuron models to different patterns of excitatory and inhibitory inputs. The models were first tuned to approximate the current- and voltage-clamp responses of low- and medium-threshold spinal motoneurons studied in decerebrate cats and then driven with different patterns of excitatory and inhibitory inputs. The responses of the models to excitatory inputs reproduced a number of features of human motor unit discharge. However, the pattern of rate modulation was strongly influenced by the temporal and spatial pattern of concurrent inhibitory inputs. Thus, even though PIC activation is likely to exert a strong influence on firing rate modulation, PIC activation in combination with different patterns of excitatory and inhibitory synaptic inputs can produce a wide variety of motor unit discharge patterns."
Reference:
1 . Powers RK, Elbasiouny SM, Rymer WZ, Heckman CJ (2012) Contribution of intrinsic properties and synaptic inputs to motoneuron discharge patterns: a simulation study. J Neurophysiol 107:808-23 [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;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Bursting; Action Potentials;
Implementer(s): Powers, Randy [rkpowers at u.washington.edu];
Search NeuronDB for information about:  Spinal cord lumbar motor neuron alpha ACh cell;
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PowersEtAl2012
code
Gfluctdv.mod *
ghchan.mod *
kca2.mod *
kdrRL.mod *
L_Ca.mod *
mAHP.mod *
na3rp.mod *
naps.mod *
synss.mod
ana_FI.hoc
ana_G.hoc
ana_passive.hoc
ana_vc.hoc
ana_vc_synss.hoc
AP_AHP.ses
FIgraph.hoc
FRcablepas.hoc
FRmnrampcc.ses
FRmnrampvc_synss.ses
FRMotoneuronNaHH.hoc
gramp.ses
GUI_FR_analysis.hoc
inhibdist
makebiramp.hoc *
passive.ses
RecActive.hoc
re-init.hoc
SetConductances.hoc
test.hoc
twobirampsdel.hoc *
vramp
vrampdel
vrampdel.hoc
                            
load_file("nrngui.hoc")
// load_file("../../../code/linfit.hoc")
load_file("linfit.hoc")

strdef fn, label, zz
objref mytime, myI, myf, myg, mydata, frq, prm, prmfitI, prmfitF

proc FIgraph() {
    
    fn="FRvar110_0.hoc_V0=-10_SLOPE=0.006_TR=10000.csv"
    prmslp = 2.28	// primary region slope
    prmitcp = -0.39	// primary region intercept
    
    myf = new File()
    myf.ropen(fn)
    
    myg = new Graph()
    
    if (myf.gets(label) < 0) {
	printf("fail to read label from %s\n", fn)
	return
    }
    
    count = myf.scanvar()
    print "count = ", count, "\n"
    mytime = new Vector(count+1)
    myI = new Vector(count+1)
    
    if (count < 1) {
	printf("count=%d is wrong in %s\n", count, fn)
	return
    }
    
    maxI = 0		// find the end of the ascending ramp
    i = 0
    while (!myf.eof) {
	myI.x[i] = myf.scanvar()
	mytime.x[i] = myf.scanvar()
	if(maxI < myI.x[i]) {
	    maxI = myI.x[i]
	    idxMaxI = i
	    i = i + 1
	} else {
	    break
	}
    }
    
    frq = new Vector(idxMaxI)
    prm = new Vector(idxMaxI)
    
    print "myg.begin\n"
    myg.begin()
    for (i=0; i<idxMaxI; i=i+1) {
	frq.x[i] = 1000/(mytime.x[i+1]-mytime.x[i])
    }
    
    frq.plot(myg, myI)
    
    topI = 1.12*myI.x[idxMaxI-1]
    topf = 1.12*frq.x[idxMaxI-1]
    myg.size(0, topI, 0, topf)
    
    setupprm()
    print "NOW: click on lower bound of primary region and hit a key"
}

proc setupprm() {
    
/*    
    print "click on lower bound of primary range, then hit <return>"
    getstr(zz)
    x0 = hoc_cross_x_
    print "x0 = ", x0
    print "click on upper bound of primary range, then hit <return>"
    getstr(zz)
    x1 = hoc_cross_x_
    print "x1 = ", x1
    */
    
    myg.crosshair_action("lower")
}

proc getprm() {
    print "return from lower/upper, use for primary range lower bound"
    
    j=0
    startfit=0
    for i=0, count {
	if (myI.x[i] < x0) {
	    startfit=i+1	// will be the value of i when j=0
	    continue
	}
	
	if (myI.x[i] > x1) {break}
	j=j+1
    }	
    
    num2fit = j
    
    prmfitI = new Vector(num2fit)	// primary range fit, I values
    prmfitF = new Vector(num2fit)	// primary range fit, F values
    
    for j=0, num2fit-1 {
	prmfitI.x[j] = myI.x[j+startfit]
	prmfitF.x[j] = frq.x[j+startfit]
    }
    
    // clear the linear fit parameters
    prmslp = prmitcp = 0
    linfit(prmfitI, prmfitF, &prmslp, &prmitcp)
    print "Primary region gain = ", prmslp, ", intercept = ", prmitcp
    
    FIredisplay()
}

proc lower() {
    x0 = $1
    print "x0 = ", x0
    myg.crosshair_action("upper")
    print "NOW click on upper bound of primary region and hit a key"
}

proc upper() {
    x1 = $1
    print "x1 = ", x1
    myg.crosshair_action("")
    print "Ready to calculate fit for primary range"
    getprm()
}

proc FIredisplay() {
    for (i=0; i<idxMaxI; i=i+1) {
	frq.x[i] = 1000/(mytime.x[i+1]-mytime.x[i])
	prm.x[i] = prmslp * myI.x[i] + prmitcp
    }
    prm.plot(myg, myI)
}


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