Core Labs

The Daley Lab is devoted to the development of computational, and mathematical, methods and techniques for modelling, probing, and understanding, our world. Our current research activities have been focussed on the mathematical and computational modelling of genetic processes as well as analyzing and modelling macroscopic functional connectivity in the brain.

The Diedrichsen Lab studies motor control, skill learning, and the function of the human cerebellum. We develop computational approaches for the analysis of high-dimensional functional imaging, behavioral, and neural data.

The Khan Lab group develops and applies novel image processing and analytics techniques and pipelines with a focus on magnetic resonance neuroimaging with clinical applications in neurological disorders and neurosurgical planning. We are interested in modelling structure and anatomy at multiple scales, using ultra-high field (7 Tesla, 9.4 Tesla) MRI, diffusion MRI, histology, and developing methods to bridge these scales.

The Mohsenzadeh  lab works at the intersection of cognitive neuroscience and computer science closing the loop between theory and experiment. Computational principles guide our experiments into the underlying brain function, and through neuroscientific insights, we aim to develop “cognitive machines”—biologically inspired computational models that can recognize and interact with the world like humans. We use deep convolutional neural networks, neuroimaging experiments (fMRI and MEG/EEG) and advanced analytical tools for mapping the spatiotemporal dynamics of neural processing during perception and memory.

The Muller Lab develops computational tools and mathematical models to solve problems in sensory processing and memory. New image analysis algorithms inspired from computer vision and computational physics allow us to capture the fine-scale dynamics of cortical populations in next-generation, large-scale neural recordings. We then use computational models and analytical approaches from random graph theory to understand the network-level mechanisms underlying our results. We collaborate with a range of colleagues in systems, cognitive, and clinical neuroscience in this research.

The Mur Lab works at the intersection of cognitive and computational neuroscience, with a focus on visual perception and cognition. We seek to understand how the brain supports our ability to interpret and flexibly interact with the world around us. To do so, we develop tools for integrating cognitive and computational approaches in explaining neural and behavioural data. This approach allows us to understand how computations performed by populations of neurons give rise to cognition and behaviour.

The Wang Lab studies both theoretical and algorithmic aspects of machine learning, with an emphasis on knowledge transfer in learning algorithms. On the application side, we develop computational approaches for brain signal analysis.