MyoSymposium 

• Intro to the workshop (5 min)

• MyoSuite introduction (5 min)

• MyoChallenge results and future (5 min)

• Dr. Thomas Geijtenbeek  

TITLE: How to Make Biomechanical Constraints Work in Your Favor

Biomechanical constraints are often perceived as a nuisance: they add an extra layer of complexity to an already complex motor control problem. However, the musculoskeletal and nervous systems have been evolving in conjunction for hundreds of millions of years, and are heavily optimized to work well together as a result. Comprehensive neuromusculoskeletal modeling has the potential to exploit this phenomenon, and drastically simplify and improve motor control learning. 

• Dr. Alexander Mathis 

TITLE: Modeling sensorimotor circuits with task-driven and reinforcement learning

ABSTRACT: Biological adaptive motor control relies on the integration of proprioception and hierarchical control. To illustrate our research on those topics, I will firstly, present a task-driven modeling approach to quantitatively test hypotheses about the functional role of proprioceptive neurons in the brain stem and cortex. Secondly, I will discuss DMAP, a biologically-inspired, attention-based policy network architecture that can learn to walk with changing bodies (Chiappa et al., NeurIPS 2022). 

• Break (5 min)

• Dr. Alessandro Del Vecchio

TITLE: Physiological Mapping of the Muscle Electrical Activity into Behaviour: Principles of Neuromechanics, High-Density Electromyographic Systems, and New Decoding Algorithms

One of the biggest challenges faced by neuroscience is to have robust neuro sensing methods that translate neural activity into stable movement dynamics and with minimally invasive methods. The spinal motor neurons, the muscles, and the tendons constitute a fundamental interface that allows the nervous system to communicate with the environment in a stable and reliable way. Here I will present and discuss recent data and new decoding algorithms that identify the latent embeddings controlling all of the functional degrees of freedom of the human hand through a feedforward neural network that maps the activity of hundreds of electromyographic sensors placed on the extrinsic hand muscles. This will be followed with a view on the neural control of the muscles from the firings of populations of individual human motor neurons during a vast range of hand movements and lower limb motor actions.

• Dr. Wendy Murray

TITLE: A Musculoskeletal Model of the Hand and Wrist Capable of Simulating Functional Tasks 

• Winner Boading Ball (15 min)

Team stiff_finger (Nisheet Patel, University of Geneva )

• Break (5 min)

• Co-winner Die Rotation (15 min)

Team pkumarl (Yiran Geng & Boshi An, Peking University)

• Co-winner Die Rotation (15 min)

Team IARAI-JKU (Rahul Siripurapu, Institute of Advanced Research in Artificial Intelligence)

• Panel discussion (30 min)

• Closing remark