When reaching for an object, the brain prepares neural commands sent to the target muscles to minimize energy expenditure, according to a study published in PLoS Computational Biology by neuroscientists and mathematicians from the INSERM and ENSTA.
How the human brain organizes and controls our actions is a crucial question in life sciences. In recent decades, an important theoretical advance has been the use of computational models and the assumption that the brain behaves like an optimal controller. In most studies, an optimality criterion is chosen a priori and assumed to produce smooth and harmonious movements, as those recorded experimentally. Most existing models, however, fail to explain how our interactions with the external environment are integrated into optimization processes.
In particular, gravity is one of the constraints that permanently act upon the movements of living organisms. The simple observation of vertical arm movements reveals that muscle activity when moving upwards differs from when moving downwards. This led the authors to surmise that the brain takes advantage of gravitational force during movement, trying to optimize energy consumption. The discovery of this biological rule has resulted from the use of a hypothetical-deductive mathematical method which predicted short periods of muscle inactivation and direction-dependent hand kinematics. These predictions have been verified experimentally using human volunteers. Moreover, they have demonstrated a necessary and sufficient condition of optimal control for arm movements which is a novelty in motor control studies.
The authors explain how the brain plans movements by integrating biological and environmental constraints and the method may be of potential value for understanding motor dysfunction and guiding subsequent rehabilitation programs. Moreover, it opens the prospect of studying brain functions by a cooperative interaction of mathematicians and neuroscientists. Interestingly, the paper is a clear demonstration that mathematical principles and theories, formerly used for understanding the non-living world, are now used for understanding how biological organisms integrate these laws.
PLEASE ADD THIS LINK TO THE PUBLISHED ARTICLE IN ONLINE VERSIONS OF YOUR REPORT: http://dx.
CITATION: Berret B, Darlot C, Jean F, Pozzo T, Papaxanthis C, et al. (2008) The Inactivation Principle: Mathematical Solutions Minimizing the Absolute Work and Biological Implications for the Planning of Arm Movements. PLoS Comput Biol 4(10): e1000194. doi:10.1371/journal.pcbi.1000194
PRESS-ONLY PDF: http://www.
Dr. Bastien Berret
Université de Bourgogne
This press release refers to an upcoming article in PLoS Computational Biology. The release is provided by journal staff [or by the article authors and/or their institutions. Any opinions expressed in this release or article are the personal views of the journal staff and/or article contributors, and do not necessarily represent the views or policies of PLoS. PLoS expressly disclaims any and all warranties and liability in connection with the information found in the releases and articles and your use of such information.
About PLoS Computational Biology
PLoS Computational Biology (www.ploscompbiol.org) features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLoS Computational Biology are open access. Everything is immediately available subject only to the condition that the original authorship and source are properly attributed. Copyright is retained by the authors. The Public Library of Science uses the Creative Commons Attribution License.
About the Public Library of Science
The Public Library of Science (PLoS) is a non-profit organization of scientists and physicians committed to making the world's scientific and medical literature a freely available public resource. For more information, visit http://www.
To update your subscriptions, or to update your account preferences visit: http://lists.
If you need assistance, please send an email to firstname.lastname@example.org.
Public Library of Science
185 Berry Street, Suite 3100
San Francisco, CA 94107