Discharge hysteresis in motoneurons (Powers & Heckman 2015)

 Download zip file 
Help downloading and running models
Accession:183949
"Motoneuron activity is strongly influenced by the activation of persistent inward currents (PICs) mediated by voltage-gated sodium and calcium channels. ... It has recently been suggested that a number of factors other than PIC can contribute to delta F (firing rate differences between motoneurons) values, including mechanisms underlying spike frequency adaptation and spike threshold accommodation. In the present study, we used a set of compartmental models representing a sample of 20 motoneurons with a range of thresholds to investigate how several different intrinsic motoneuron properties can potentially contribute to variations in F values. ... Our results indicate that, although other factors can contribute, variations in discharge hysteresis and delta F values primarily reflect the contribution of dendritic PICs to motoneuron activation.
Reference:
1 . Powers RK, Heckman CJ (2015) Contribution of intrinsic motoneuron properties to discharge hysteresis and its estimation based on paired motor unit recordings. A simulation study. J Neurophysiol 114:184-198 [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): I Na,p; I Na,t; I L high threshold; I K; I M; I K,Ca; I_AHP; I Calcium; I Sodium;
Gap Junctions:
Receptor(s):
Gene(s): Kv1.2 KCNA2; Kv1.9 Kv7.1 KCNQ1;
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Spike Frequency Adaptation;
Implementer(s): Powers, Randy [rkpowers at u.washington.edu];
Search NeuronDB for information about:  Spinal cord lumbar motor neuron alpha cell; I Na,p; I Na,t; I L high threshold; I K; I M; I K,Ca; I Sodium; I Calcium; I_AHP;
/
Discharge_hysteresis
Model hoc files and output
README.txt
Gfluctdv.mod *
ghchan.mod *
kca2.mod *
KCNQ.mod *
kdrRL.mod *
km_hu.mod
kv1_gp.mod *
L_Ca.mod *
L_Ca_inact.mod *
mAHP.mod *
mAHPvt.mod
na3rp.mod *
naps.mod *
napsi.mod *
AHPlen.csv
FasterMis.csv
FR3cablepas.hoc
FRMot3dendNaHH.hoc
gramp.ses
HiDKCa.csv
init_3dend_gramp.hoc
LCai.csv
Napi.csv
pars2manyhocs.py *
ProxCa.csv
SetConductances2.hoc *
SlowM.csv
standard.csv
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()
//		

Loading data, please wait...