More and more neural and behavioural data is being collected in increasingly complex settings, offering unique opportunities to study how brains control behaviours. A major challenge is to infer the computational and mechanistic principles underlying adaptive control from this ocean of data. Our lab tackles this puzzle in two ways:
- by building network-level theories of brain computation, with an emphasis on motor control
- by developing machine learning methodology for analysing complex datasets in light of these theories
At both levels, our work builds heavily upon engineering-related domains such as dynamical systems and control theory, probabilistic machine learning, and optimization.
Recent updates (see all)
May 2021
Calvin successfully defended his PhD thesis entitled “Optimal anticipatory control as a theory of motor preparation”. Thanks to his examiners, Máté Lengyel and Byron Yu!
April 2021
See Kao et al.’s work on optimal motor preparation via a thalamo-cortical loop
October 2020
See Rutten et al.’s work on non-reversible Gaussian processes (oral), and Jensen et al.’s new manifold GPLVM.