Topic: Multiple Brains

This talk explores how combining neural data from multiple brains with deep networks could benefit both neuroscience and AI. It presents approaches to mapping information across brains without shared observations and considers how collective neural information might guide new discoveries. The same perspective motivates alternative ways to train and interpret artificial networks.

1. Can we reconstruct images that a person saw, directly from their fMRI brain recordings? 2. Can we reconstruct the training data that a deep-network trained on, directly from the parameters of the network? The answer to both of these intriguing questions is “Yes!” In this talk I will present some of our work in both domains. I will then show how combining the power of Brains and Machines can lead to significant breakthroughs in both areas, and potentially bridge the gap between Minds and Machines. Finally, I will show how combining the power of Multiple Brains (with NO shared data) may l