After completing a Master's degree in neuroscience with Dr. Sheena Josselyn at SickKids Hospital in Toronto, I joined the LiNC Lab (at the time based at the University of Toronto Scarborough) in order to focus my research on the emerging intersection of computational and systems neuroscience. Specifically, I am interested in understanding how new developments in AI, specifically neural networks and deep learning, could provide key insights into how learning occurs in the brain.
My current research aims to elucidate how the brain learns a model of the sensory world, through experience. In particular, we are interested in determining whether the brain uses predictive learning, i.e. trains itself to predict upcoming sensory experiences, and updates its internal model of the world when its predictions are incorrect.
The predictive learning hypothesis is broadly supported not only by extensive human and animal research, but also by observations from daily life, like how attuned toddlers are to surprising things, or our own ability to fi_l in the bl_nks, to overlook some some errors, and yet quickly notice when something is out of pLace. Our work aims to expand on this evidence, identifying which types of predictive models, if any, best account for neural activity observed during unexpected visual experiences.
Read our latest paper showing that the somata and distal apical dendrites of pyramidal neurons in visual cortex show different responses to unexpected visual events, now up on bioRxiv.