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 L motor neuron alpha;
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 L motor neuron alpha; 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 *
                            
TITLE KCNQ potassium channel for GPe neuron

COMMENT
 modeled by Gunay et al., 2008
 implemented in NEURON by Kitano, 2011
ENDCOMMENT

UNITS {
 (mV) = (millivolt)
 (mA) = (milliamp)
}

NEURON {
 SUFFIX KCNQ
 USEION k READ ek WRITE ik
 RANGE gmax, iKCNQ
}

PARAMETER {
 v (mV)
 dt (ms)
 gmax  = 0.001 (mho/cm2)
 iKCNQ  = 0.0 (mA/cm2)
 ek (mV)

 theta_m = -61.0 (mV)
 k_m = 19.5 (mV)
 tau_m0 = 6.7 (ms)
 tau_m1 = 100.0 (ms)
 phi_m = -61.0 (mV)
 sigma_m0 = 35.0 (mV)
 sigma_m1 = -25.0 (mV)
}

STATE {
 m
}

ASSIGNED { 
 ik (mA/cm2)
 minf
 taum (ms)
}

BREAKPOINT {
 SOLVE states METHOD cnexp
 ik  = gmax*m*m*m*m*(v-ek)
 iKCNQ = ik
}

UNITSOFF

INITIAL {
 settables(v)
 m = minf
}

DERIVATIVE states {  
 settables(v)
 m' = (minf - m)/taum
}

PROCEDURE settables(v) {
        TABLE minf, taum FROM -100 TO 100 WITH 400

	minf = 1.0 / (1.0 + exp((theta_m - v)/k_m))
	taum = tau_m0 + (tau_m1 - tau_m0)/(exp((phi_m - v)/sigma_m0) + exp((phi_m - v)/sigma_m1))
}

UNITSON

Loading data, please wait...