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)
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.
March 2019
He will be giving a perspective talk about the use of control-theoretic methods in neuroscience (slides) at the workshop on data, dynamics and computation, and a talk on the role of inhibition in motor control at the workshop on inhibitory microcircuits
October 2018
Commentary in TICS on recent brain-computer interface results by the Baptista/Yu/Chase groups at CMU
See full text here
September 2018
Motor primitives in space and time via targeted gain modulation in cortical networks
September 2018
Come and join us for an inspiring workshop on “Emergent function in non-random neural networks”, ahead of the main Bernstein Conference