In this Brains@Bay meetup, we will be discussing alternatives to backpropagation in neural networks. We’re excited to have three terrific speakers joining us!
From the neuroscience side, we will host Prof. Rafal Bogacz, from University of Oxford, discussing the viability of backpropagation in the brain, and the relationship of predictive coding networks and backpropagation. Prof. Rafal has published extensively in the field and co-authored a comprehensive review paper on Theories of Error Backpropagation in the Brain.
Sindy Löwe from University of Amsterdam will discuss her latest research on self-supervised representation learning. She is the first author of the paper Putting An End to End-to-End: Gradient-Isolated Learning of Representations, presented at last year’s Neurips, that shows networks can learn by optimizing the mutual information between representations at each layer of a model in isolation.
Jack Kendall, co-founder of RAIN Neuromorphics, will show how equilibrium propagation can be used to train end-to-end analog networks, which can guide the development of a new generation of ultra-fast, compact and low-power neural networks supporting on-chip learning.
The talks will be followed by a discussion panel where will juxtapose different points of view and address your main questions. We look forward to seeing you there!