“Brain Organoid Computing for Artificial Intelligence”

Abstract: Current silico-computing hardware and system are facing challenges related to power consumption and sustainability, particularly to train big artificial intelligence (AI) models. To address these challenges, neuromorphic hardware inspired by structure and function of the human brain is undergoing development. Here, we report a hybrid neuromorphic computing system by integrating conventional silico-computing hardware with a human brain organoid. We leverage the organoid neural network for reservoir computation and demonstrate its real-world applications such as prediction of nonlinear equations. This new approach may hold promise for advancing biocomputing, brain-machine interfaces, and translational medicine.

Bio: Dr. Feng Guo is an Associated Professor of Intelligent Systems Engineering at Indiana University Bloomington (IUB). Before joining IUB in 2017, he received his Ph.D. in Engineering Science and Mechanics at Penn State and his postdoc training at Stanford University School of Medicine. His group is developing intelligent medical devices, sensors, and systems with the support of multiple NIH and NSF awards. He is a recipient of the NIH Director’s New Innovator Award, the Outstanding Junior Faculty Award at IU, Early Career Award at Penn State, the Dean Postdoctoral Fellowship at Stanford School of Medicine, etc.