Motoneuron pool input-output function (Powers & Heckman 2017)

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Accession:239582
"Although motoneurons have often been considered to be fairly linear transducers of synaptic input, recent evidence suggests that strong persistent inward currents (PICs) in motoneurons allow neuromodulatory and inhibitory synaptic inputs to induce large nonlinearities in the relation between the level of excitatory input and motor output. To try to estimate the possible extent of this nonlinearity, we developed a pool of model motoneurons designed to replicate the characteristics of motoneuron input-output properties measured in medial gastrocnemius motoneurons in the decerebrate cat with voltage- clamp and current-clamp techniques. We drove the model pool with a range of synaptic inputs consisting of various mixtures of excitation, inhibition, and neuromodulation. We then looked at the relation between excitatory drive and total pool output. Our results revealed that the PICs not only enhance gain but also induce a strong nonlinearity in the relation between the average firing rate of the motoneuron pool and the level of excitatory input. The relation between the total simulated force output and input was somewhat more linear because of higher force outputs in later-recruited units. ..."
Reference:
1 . Powers RK, Heckman CJ (2017) Synaptic control of the shape of the motoneuron pool input-output function. J Neurophysiol 117:1171-1184 [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 cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s):
Implementer(s): Powers, Randy [rkpowers at u.washington.edu];
Search NeuronDB for information about:  Spinal cord lumbar motor neuron alpha cell;
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RPCH_JNP17
FivecomptcMG
FivecompthTA
FivecompthTAlw
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README.html
Gfluctdv.mod *
gh.mod
kdrRL.mod *
L_Ca_inact.mod *
mAHP.mod *
na3rp.mod *
naps.mod *
avrate.png
cc_command.hoc
fwave1s.txt
fwave2s.txt
init.hoc
init_5cmpt.hoc
makebiramp.hoc *
mosinit.hoc
pars2manyhocs.py *
SetConductances2.hoc *
twobirampsdel.hoc *
                            
//Baseline values simplified; std made 60% of baseline mean
forall {if(ismembrane("Gfluctdv")){g_e0_Gfluctdv=1e-5 std_e_Gfluctdv=6e-6}}
forall {if(ismembrane("Gfluctdv")){g_i0_Gfluctdv=1e-5 std_i_Gfluctdv=6e-6}}
forall {if(ismembrane("Gfluctdv")){tau_e_Gfluctdv=0.5 tau_i_Gfluctdv=2}}


xopen("twobirampsdel.hoc")

TR=20000
SLOPE1=0.001
SLOPE2=1e-7
RSTRT1=0
RSTRT2=0
HOLD=1000

// invoke the following procedures as needed from the interpreter window
proc grampon() {

	mycmd1.play(&multex_Gfluctdv,dt)
	mycmd2.play(&multin_Gfluctdv,dt)
	print "mixed noisy synaptic input is now driven by vector mycmd"
}





// to "disconnect" the ramp from the fluctuating conductance
proc grampoff() {

	// restore mult to 0

	mycmd1.play_remove()
	mycmd2.play_remove()
	multex_Gfluctdv=0
	multin_Gfluctdv=0

	print "synaptic conductance has been released from mycmd"

}

simple2del()


//code to set up spike counter
objref apc,spiketimes,spikeout
apc=new APCount(0.5)
spiketimes=new Vector()
spikeout=new File()
strdef filename


//to save spike times, type following in Terminal window
//		apc.record(spiketimes)
//		sprint(filename,"FRramp.txt")
//		spikeout.wopen(filename)
//		spiketimes.printf(spikeout,"%8.4f\n")
//		spikeout.close()
//		

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