Interpretable Latent Variable Models Demonstrate Flexible Neural Control of Spinal Motor Units

Explore the development of interpretable machine learning models for analyzing neural activity data in this 24-minute webinar presented by Dr. Joshua I. Glaser, Assistant Professor at Northwestern University. Delve into the study of spinal motor control and the challenge to the canonical "rigid control" theory of motor unit function. Learn about novel experiments involving macaques generating varying forces and the creation of a latent variable model to test scientific hypotheses. Discover how this research demonstrates that, contrary to traditional beliefs, motor unit activity is flexibly controlled to meet task demands. Gain insights into the application of machine learning tools in neuroscience and their potential to advance our understanding of neural control mechanisms.

Interpretable Latent Variable Models Demonstrate Flexible Neural Control of Spinal Motor Units