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.
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
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!
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!
June 2023
Guillaume Hennequin is promoted to Professor (Grade 11)
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!
