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)
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!
See Kao et al.’s work on optimal motor preparation via a thalamo-cortical loop
See Rutten et al.’s work on non-reversible Gaussian processes (oral), and Jensen et al.’s new manifold GPLVM.
Welcome to both!