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)
February 2025
Join us at Pembroke College on Saturday 1st March for our NeuroML symposium.
Attendance is free, but registration on EventBrite is mandatory.
Attendance is free, but registration on EventBrite is mandatory.
November 2024
new PhD position to develop ML tools for learning dynamics from data (see e.g. this) and apply them to C elegans brain dynamics. Interested? Get in touch with Guillaume
February 2024
Marine has successfully defended her PhD (“Models of neural circuits as optimally driven dynamical systems”), with Srdjan Ostojic and Máté Lengyel as examiners. Well done Marine!
September 2023
Kris is now Dr Kris! He successfully defended his thesis entitled “Strong and weak principles of Bayesian machine learning for systems neuroscience” (examined by Yashar Ahmadian and Jonathan Pillow – thank you both!). Congrats, Kris!
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!