#817: Neuroscience & VR: Computational Neuroscience, Perception, Machine Learning, & the Dream of Restoring Lost Senses

Joel Zylberberg has a background in physics and cosmology, but then he got interested in theoretical neuroscience and he’s been researching the nature of perception in humans and in machines through the lens of computational neuroscience. He’s the Canada Research Chair and an assistant professor at York University in Toronto as well as Canadian Institute for Advanced Research (CIFAR) Associate Fellow of Learning in Machines and Brains.

Zylberberg was an attendee to the CIFAR workshop on the Future of Neuroscience and VR, and I had a chance to debrief him after the two-day workshop. He was really impressed with how much virtual reality technology has progressed over, and he’s shared that he’s a part of the EEG in the Wild research grant from CIFAR where he’ll be collaborating with Craig Chapman and Alona Fyshe on their project to automatically label EEG data with motion & eye tracking data from VR.

I had a chance to talk to Zylberberg about the neuroscience mechanics of perception, his work in computational neuroscience in trying to understand the neuroscience of perception, back-propagation, top-down feedback connections, & the credit assignment problem, deep learning machine learning models of perception, feed forward network architectures of convolution neural networks and recurrent neural networks, optogenetics,the ambitious goal allowing people to regain sight by trying to find a way to write information directly into the brain, the challenges of the data processing inequality from information theory, and the importance for VR designers to understand the fundamental mechanics of perception.


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Music: Fatality