Discharge hysteresis in motoneurons (Powers & Heckman 2015)

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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 114:184-98 [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): 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 ACH cell; I Na,p; I Na,t; I L high threshold; I K; I M; I K,Ca; I Sodium; I Calcium; I_AHP;
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Discharge_hysteresis
Model hoc files and output
README.txt
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kca2.mod *
KCNQ.mod *
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km_hu.mod
kv1_gp.mod *
L_Ca.mod *
L_Ca_inact.mod *
mAHP.mod *
mAHPvt.mod
na3rp.mod *
naps.mod *
napsi.mod *
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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 *
                            
TITLE Borg-Graham like K-M channel, taken from Hu et al. J Neurosci 29:14472,2009

NEURON {
	SUFFIX km_hu
	USEION k READ ek WRITE ik
        RANGE gbar,inf,tau,m,ik
        GLOBAL vhalf,a0,zeta,gm
}
UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
        (mM) = (milli/liter)

}

PARAMETER {
	v (mV)
        ek (mV)
	tm=1.0
	celsius 	(degC)
	gbar=.0012 (mho/cm2)
        vhalf=-43   (mV)
        a0=0.004      (/ms)
        zeta=3.5  
        gm=0.5  
}



STATE {
        m
}

ASSIGNED {
	ik (mA/cm2)
        inf
        tau
}

INITIAL {
        rate(v)  
        m=inf
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	ik = gbar*m*(v-ek)

}

FUNCTION alp(v(mV)) {
  alp = a0*exp(1e-3*zeta*gm*(v-vhalf)*9.648e4/(8.315*(273.16+celsius)))
}

FUNCTION bet(v(mV)) {
  bet = a0*exp(-1e-3*zeta*(1-gm)*(v-vhalf)*9.648e4/(8.315*(273.16+celsius))) 
}

DERIVATIVE state {  
        rate(v)
        m' = (inf - m)/tau
}

PROCEDURE rate(v (mV)) { :callable from hoc
        LOCAL q10
        q10=5^((celsius-32)/10)
         inf = alp(v)/(alp(v)+bet(v))
        tau =(1/q10)*(tm+1/(alp(v)+bet(v)))
}















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