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

 Download zip file   Auto-launch 
Help downloading and running models
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]
Citations  Citation Browser
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):
Implementer(s): Powers, Randy [rkpowers at u.washington.edu];
Search NeuronDB for information about:  Spinal cord lumbar motor neuron alpha ACh cell;
/
RPCH_JNP17
FivecomptcMG
FivecomptcMG_3
FivecomptcMG_3.hoc
                            
//uses dpath of 700 from M1   alternative atten fact for ht (more atten)
soma.diam = 22.031491471059923
soma.L = 2954.8066773582154
soma.g_pas = 8.225353112698644e-05
soma.e_pas = -71.00393643388249
soma.gbar_na3rp = 0.010047237206589883
soma.gbar_naps = 2.5976381396705057e-05
soma.sh_na3rp = 1.0
soma.sh_naps = 5.0
soma.ar_na3rp = 1.0
soma.ar_naps = 1.0
soma.gMax_kdrRL = 0.01501968216941245
soma.gcamax_mAHP = 6.400402366787434e-06
soma.gkcamax_mAHP = 0.0004505904650823735
soma.taur_mAHP = 59.80317830587549
soma.ek = -80.0
soma.Ra = 0.001
soma.cm = 1.3575190304709142
soma.ghbar_gh = 3.078728677649803e-05
soma.half_gh = -77.0
forsec dend{
L = 1795.8336098556642
diam = 8.743207350925791
g_pas = 7.972139583029595e-05
e_pas = -71.00393643388249
gcabar_L_Ca_inact = 9.51220294503572e-05
Ra = 50.99752165038635
cm = 0.8678307014142004
ghbar_gh = 3.078728677649803e-05
half_gh = -77.0
}
d1.gcabar_L_Ca_inact = 8.512990231812217e-05
d2.gcabar_L_Ca_inact = 9.512990231812217e-05
d3.gcabar_L_Ca_inact = 0.00010014958448753463
d4.gcabar_L_Ca_inact = 0.00011514958448753463
qinf_na3rp = 8.0
thinf_na3rp = -50.0
vslope_naps = 5.0
asvh_naps = -90.0
bsvh_naps = -22.0
mvhalfca_mAHP = -20.0
mtauca_mAHP = 2.0
celsius = 37.0
theta_m_L_Ca_inact = -39.98819069835253
tau_m_L_Ca_inact = 40.0
theta_h_L_Ca_inact = 1.905263157894737
tau_h_L_Ca_inact = 2500.0
kappa_h_L_Ca_inact = 5.0
htau_gh = 30.0
mVh_kdrRL = -21.0
tmin_kdrRL = 0.8
taumax_kdrRL = 20.0
V0 = -14.901589152937746