Neural Dynamics and Control Group

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.

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