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Neuroscience for human movement

Welcome to my blog series on neuroscience for human movement! If the title of this posting attracted your attention, we probably have something in common: We are fascinated by graceful yet powerful human movements like what is demonstrated by the Capoeiristas in the following video (go to 1:33 for the most relevant part).

How do they throw deadly kicks at each other so rapidly while successfully avoiding a collision? Usually, their duel is not necessarily rehearsed. One Capeirista moves reflexively according to another's movement. What allows them to make such a precise and adaptive movement? We can all agree that it would require years of training. But what exactly are trained? Of course, muscles and bones are trained. But more importantly, the nervous system's ability to control each part of the body has to be trained. This seemingly obvious mechanism is not that simple to understand. Nevertheless, understanding neural control of movement is mandatory if you are interested in helping people move better and perform better skills. I am not just talking about athletic movement training, It is also very relevant to injury rehabilitation or training the elderly population as neural adaptation inevitably occurs in both cases.

Perhaps one of the simplest ways of understanding the neural control of movement is to consider the human brain as software that controls a robot's hardware. In fact, the most remarkable advancement in robotics was made as the researchers discovered ways to design software that mimics the function of the human brain. Here is the video of miserable failures shown by the robots which participated in the contest held by The Defense Advanced Research Project Agency (DARPA) of the United States in 2015.. We can see that the major reason for the failure is their inability to adapt their movements to the changing environment.

Within only two years, the engineers and researchers came up with a much more athletic robot that also demonstrates highly adaptable movement. It is shown in the video below.

The hardware is not that different between the robots shown in the two different videos. What is significantly different is the algorithm used for developing their software. The software of the advanced robot has a better ability to self-learn important parameters and limitations of the default program it has. This allows the software to establish certain expectations as to the consequence of the robot's action. Having such an expectation renders a huge advantage as the comparison between the expectation and actual consequences is a precious learning opportunity for the robot. Such an ability to predict and learn is exactly the mechanism of the human brain that the robot engineers are continuously trying to mimic when developing the software. Here is the simplified version of that mechanism.

A simplified concept map that explains how human nervous system predicts the consequence of the motor commands before the movement happens and how this mechanism facilitates human movement adaptation.

Although it is fascinating to watch a robot doing a backflip, I am more interested in applying neuroscience to further benefit human movement. The series of postings that follow this post are the product of my effort to understand the human brain's mechanism for prediction, adaptation, and learning. Just like robots do, the human brain uses this mechanism to overcome the limitations of our own default system including our sensory receptors and musculoskeletal system. So the follow-up postings will explain what it means by such limitations and how the brain solves the problem related to the limitations.

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The posts will be under the category titled, "Neuroscience for human movement"



Duhamel, J. R., Colby, C. L., & Goldberg, M. E. (1992). The updating of the representation of visual space in parietal cortex by intended eye movements. Science, 255(5040), 90.

Wolpert, D. M., & Flanagan, J. R. (2001). Motor prediction. Current biology, 11(18), R729-R732.

Sommer, M. A., & Wurtz, R. H. (2006). Influence of the thalamus on spatial visual processing in frontal cortex. Nature, 444(7117), 374-377

–Mulliken, G. H., & Andersen, R. A. (2009). Forward models and state estimation in posterior parietal cortex. The cognitive neurosciences IV, 599-611.

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