Control Theory podcasts

Maxim Raginsky joins Paul Middlebrooks to examine brains, AI and biological autonomy through control theory. They discuss what behaviour reveals about a system's internal organisation, analog and digital descriptions, the relationship between control and cybernetics, active inference and the limits of computational accounts. The publisher offers a public episode and supporter access to its full archive.

Hosted by Paul Middlebrooks

Nikita Rudin discusses reinforcement learning for robot locomotion and the difficulties of transferring policies from simulation to physical robots. Topics include visual inputs, real-world data for improving simulators, imitation learning and teleoperation, hierarchical control, and trade-offs between modular and end-to-end approaches for autonomous robots.

Hosted by Sam Charrington

Xaq Pitkow describes a principles-based approach to explaining cognition. Topics include inverse rational control, strategies that are rational relative to an organism’s beliefs, probabilistic computation and graph-network inference. The conversation also considers attention, constraints on neural codes and studying cognition in ecological settings.

Hosted by Paul Middlebrooks

Dmitri Chklovskii proposes treating individual neurons as feedback controllers that predict and regulate their inputs. The discussion compares this account with older views of neurons as logical devices or passive components, considers supporting neuroscience findings, and asks how single-cell control relates to the behaviour of neural populations.

Hosted by Paul Middlebrooks