Models of passive and active dendrite motoneuron pools and their differences in muscle force control.

Journal of Computational Neuroscience
Leonardo Abdala EliasAndré F Kohn

Abstract

Motoneuron (MN) dendrites may be changed from a passive to an active state by increasing the levels of spinal cord neuromodulators, which activate persistent inward currents (PICs). These exert a powerful influence on MN behavior and modify the motor control both in normal and pathological conditions. Motoneuronal PICs are believed to induce nonlinear phenomena such as the genesis of extra torque and torque hysteresis in response to percutaneous electrical stimulation or tendon vibration in humans. An existing large-scale neuromuscular simulator was expanded to include MN models that have a capability to change their dynamic behaviors depending on the neuromodulation level. The simulation results indicated that the variability (standard deviation) of a maintained force depended on the level of neuromodulatory activity. A force with lower variability was obtained when the motoneuronal network was under a strong influence of PICs, suggesting a functional role in postural and precision tasks. In an additional set of simulations when PICs were active in the dendrites of the MN models, the results successfully reproduced experimental results reported from humans. Extra torque was evoked by the self-sustained discharge of spinal MNs,...Continue Reading

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Citations

May 27, 2014·The Journal of Physiology·Dario FarinaJakob Lund Dideriksen
May 11, 2017·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·Renato Naville Watanabe, Andre Fabio Kohn
Oct 4, 2014·Journal of Applied Physiology·Sherif M Elbasiouny
Dec 4, 2019·Journal of Neural Engineering·Carina Marconi GermerLeonardo Abdala Elias
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Apr 27, 2018·Frontiers in Neuroinformatics·Hojeong Kim, Minjung Kim
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Aug 24, 2019·Frontiers in Neuroinformatics·Raghu Sesha IyengarMohan Raghavan
Jun 19, 2015·Journal of Neural Engineering·Hojeong KimC J Heckman

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