The OpenSim project and the National Center for Simulation in Rehabilitation Research (NCSRR) at Stanford invite you to join our next webinar, featuring Mazen Al Borno from Stanford University.

DETAILS
Title: Computational Models of Reaching to Test Hypotheses in Motor Control
Speaker: Mazen Al Borno, Stanford University
Time: Tuesday, February 11, 2020 at 10:00 a.m. Pacific Time
Registration: Registration is free but required, as space is limited. Click here to register.

ABSTRACT
Humans have the remarkable ability to move with ease, precision, speed and versatility. Without being conscious of it, our motor system is constantly solving computationally challenging problems in ways that astonish both roboticists and neuroscientists. Different hypotheses exist on how the brain controls movements, but we have limited means of testing these ideas. One approach is with computational models of the neuromusculoskeletal system. In this webinar, Mazen Al Borno will discuss two studies that have utilized computational modeling to elicit a new understanding of 1) the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff, and also 2) the feasibility of muscle synergies, a low-dimensional controller, to produce the rich and flexible behaviors seen in everyday movements.

Underlying these studies is a computational model of reaching movements based on optimal control theory with realistic musculoskeletal dynamics. In the webinar, Dr. Al Borno will discuss the validation of the model. He will show that the speed-accuracy tradeoff as described by Fitts' law emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. Spurred by this discovery, Dr. Al Borno and his colleagues derived an alternative theory based on motor planning variability. He will share the computational results leading to this theory and describe the experimental verification using neural recordings from rhesus monkeys. Dr. Al Borno will also highlight their computational experiments to determine whether synergies introduce task performance deficits, facilitate the learning of movements, and generalize to different movements.

1. Speed-Accuracy Paper:
High-fidelity Musculoskeletal Modeling Reveals a Motor Planning Contribution to the Speed-Accuracy Tradeoff Mazen Al Borno, Saurabh Vyas, Krishna V. Shenoy, Scott L. Delp
doi: https://doi.org/10.1101/804088

Source code and data:
https://simtk.org/projects/ue-reaching

2. Synergies The Effects of Motor Modularity on Performance, Generalizability and Learning in Upper-Extremity Reaching: a Computational Analysis Mazen Al Borno, Jennifer L. Hicks, Scott L. Delp
doi: https://doi.org/10.1101/804096

Source code and data:
https://simtk.org/projects/ue-synergies