Computational Principles of Sensorimotor Control - Lecture 1
Explore the computational principles of sensorimotor control in this comprehensive lecture by Daniel Wolpert, part of the ICTP-ICTS Winter School on Quantitative Systems Biology. Delve into topics such as the complexity of human movement, optimal control theory, state estimation, motor prediction, and Bayesian decision theory. Examine various models of motor control, including the Kalman filter and forward models, and understand how they relate to real-world applications like eye movements and arm trajectories. Learn about different types of motor learning, impedance control, and loss functions in movement. Gain insights into the normative approach to human movement control and how it can be applied to reverse-engineer sensorimotor systems. This lecture provides a solid foundation for understanding the computational principles underlying how organisms sense the world and generate behaviors.