Literature DB >> 18648849

System identification of muscle-joint interactions of the cat hind limb during locomotion.

Nalin Harischandra1, Orjan Ekeberg.   

Abstract

Neurophysiological experiments in walking cats have shown that a number of neural control mechanisms are involved in regulating the movements of the hind legs during locomotion. It is experimentally hard to isolate individual mechanisms without disrupting the natural walking pattern and we therefore introduce a different approach where we use a model to identify what control is necessary to maintain stability in the musculo-skeletal system. We developed a computer simulation model of the cat hind legs in which the movements of each leg are produced by eight limb muscles whose activations follow a centrally generated pattern with no proprioceptive feedback. All linear transfer functions, from each muscle activation to each joint angle, were identified using the response of the joint angle to an impulse in the muscle activation at 65 postures of the leg covering the entire step cycle. We analyzed the sensitivity and stability of each muscle action on the joint angles by studying the gain and pole plots of these transfer functions. We found that the actions of most of the hindlimb muscles display inherent stability during stepping, even without the involvement of any proprioceptive feedback mechanisms, and that those musculo-skeletal systems are acting in a critically damped manner, enabling them to react quickly without unnecessary oscillations. We also found that during the late swing, the activity of the posterior biceps/semitendinosus (PB/ST) muscles causes the joints to be unstable. In addition, vastus lateralis (VL), tibialis anterior (TA) and sartorius (SAT) muscle-joint systems were found to be unstable during the late stance phase, and we conclude that those muscles require neuronal feedback to maintain stable stepping, especially during late swing and late stance phases. Moreover, we could see a clear distinction in the pole distribution (along the step cycle) for the systems related to the ankle joint from that of the other two joints, hip or knee. A similar pattern, i.e., a pattern in which the poles were scattered over the s-plane with no clear clustering according to the phase of the leg position, could be seen in the systems related to soleus (SOL) and TA muscles which would indicate that these muscles depend on neural control mechanisms, which may involve supraspinal structures, over the whole step cycle.

Entities:  

Mesh:

Year:  2008        PMID: 18648849     DOI: 10.1007/s00422-008-0243-z

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  7 in total

1.  Afferent control of locomotor CPG: insights from a simple neuromechanical model.

Authors:  Sergey N Markin; Alexander N Klishko; Natalia A Shevtsova; Michel A Lemay; Boris I Prilutsky; Ilya A Rybak
Journal:  Ann N Y Acad Sci       Date:  2010-06       Impact factor: 5.691

Review 2.  The brain in its body: motor control and sensing in a biomechanical context.

Authors:  Hillel J Chiel; Lena H Ting; Orjan Ekeberg; Mitra J Z Hartmann
Journal:  J Neurosci       Date:  2009-10-14       Impact factor: 6.167

3.  A 3D Musculo-Mechanical Model of the Salamander for the Study of Different Gaits and Modes of Locomotion.

Authors:  Nalin Harischandra; Jean-Marie Cabelguen; Orjan Ekeberg
Journal:  Front Neurorobot       Date:  2010-12-16       Impact factor: 2.650

4.  Sensory feedback plays a significant role in generating walking gait and in gait transition in salamanders: a simulation study.

Authors:  Nalin Harischandra; Jeremie Knuesel; Alexander Kozlov; Andrej Bicanski; Jean-Marie Cabelguen; Auke Ijspeert; Orjan Ekeberg
Journal:  Front Neurorobot       Date:  2011-11-04       Impact factor: 2.650

5.  From the motor cortex to the movement and back again.

Authors:  Wondimu W Teka; Khaldoun C Hamade; William H Barnett; Taegyo Kim; Sergey N Markin; Ilya A Rybak; Yaroslav I Molkov
Journal:  PLoS One       Date:  2017-06-20       Impact factor: 3.240

6.  Reward Based Motor Adaptation Mediated by Basal Ganglia.

Authors:  Taegyo Kim; Khaldoun C Hamade; Dmitry Todorov; William H Barnett; Robert A Capps; Elizaveta M Latash; Sergey N Markin; Ilya A Rybak; Yaroslav I Molkov
Journal:  Front Comput Neurosci       Date:  2017-03-31       Impact factor: 2.380

7.  The interplay between cerebellum and basal ganglia in motor adaptation: A modeling study.

Authors:  Dmitrii I Todorov; Robert A Capps; William H Barnett; Elizaveta M Latash; Taegyo Kim; Khaldoun C Hamade; Sergey N Markin; Ilya A Rybak; Yaroslav I Molkov
Journal:  PLoS One       Date:  2019-04-12       Impact factor: 3.240

  7 in total

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