“Neural control of movement: How we move fast, why we fail, and how we can design interventions”
Thursday, Jan. 23 @1:00 pm
LAR 330

Abstract

Animals (including humans) have a remarkable ability to effortlessly perform complex and fast movements. However, how the brain coordinates with muscles to execute movements at different speeds is not well understood. Furthermore, neural limitations on our ability to make agile movements in health and disease are also poorly characterized.

In the first part of my talk, I will focus on how the brain generates movements at different speeds. We analyze muscle and motor cortex data from macaque monkeys trained to perform a cycling task at varying speeds. By leveraging insights from a recurrent neural network (RNN) model, we generate specific predictions for the dynamical structure of neural activity, which we then confirm in the data. Both the model and neural data support the surprising view that the dominant features of motor cortex activity are not involved with the minutiae of creating complex muscle movements. Rather, these can be explained by considering how network activity should be structured to remain noise-robust.

In the second part of my talk, I will focus on how the ability to accurately track fast-moving objects is fundamentally constrained by the biophysics of neurons and dynamics of the muscles involved. Using a biophysically based model of neuronal dynamics, we predict undesirable phenomena that occur when tracking fast or high frequency sinusoidal inputs, including skipped cycles, overshoot and undershoot. Notably, these specific errors are well documented for humans and monkeys. We derive an analytical bound on the highest frequency that we can track without producing such undesirable phenomena, as a function of the neural computation and muscle dynamics. This theoretical analysis can be used to guide the design of therapies for movement disorders caused by neural damage using assistive neuroprosthetic devices. I will end by providing insights into how we can leverage data-driven models of large-scale cortical activity to restore impaired movements.

Biography

Shreya Saxena is broadly interested in the neural control of coordinated, complex movements. She did her PhD in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology studying the closed-loop control of fast movements from a control theory perspective. Shreya received a B.S. in Mechanical Engineering from the Swiss Federal Institute of Technology (EPFL), and an M.S. in Biomedical Engineering from Johns Hopkins University. She is currently a Swiss National Science Foundation Postdoctoral Fellow at Columbia University’s Zuckerman Mind Brain Behavior Institute focusing on data-driven modeling of high-dimensional neural activity and the ensuing behavior. Her current research focuses on analyzing how global cortical activity leads to a variety of task-related as well as spontaneous movements, and exploring how population activity in the motor cortex flexibly controls movements at a continuum of speeds. Shreya was honored to have been selected as a Rising Star in both Electrical Engineering (2019) and Biomedical Engineering (2018).