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Feng Guo, an associate professor of intelligent systems engineering at the Indiana University Luddy School of Informatics, Computing and Engineering, is spearheading the creation of a groundbreaking new hybrid computing system, “Brainoware,” to address the technical limitations of artificial intelligence computing hardware. This system combines electronic hardware with human brain organoids and is set to revolutionize AI as we know it.
We all know that advanced AI techniques, such as machine learning and deep learning, consume immense amounts of energy due to the specialized silicon computer chips they rely on. Thankfully, engineers have developed neuromorphic computing systems, inspired by the human brain’s structure and function, to enhance the performance and efficiency of these technologies. However, these systems have never reached their full potential as they are based on digital electronic principles.
To counter these limitations, Guo and his team of researchers from IU, including graduate student Hongwei Cai, have engineered a hybrid neuromorphic computing system. The innovative system is designed to house a brain organoid on a multielectrode assay, enabling it to receive and send information. These brain organoids are advanced brain-like 3D cell cultures created from stem cells and characterized by different brain cell types, including neurons and glia, as well as brain-like structures such as ventricular zones.
“Brainoware leverages a human brain organoid as an adaptive living reservoir to conduct unsupervised learning by processing spatiotemporal information through the neuroplasticity of the brain organoid,” Guo shared. “This interdisciplinary approach paves the way for AI computing advancements by providing biological neural networks with high complexity, low energy consumption, and accelerated learning.”
The team’s groundbreaking work was recently published in Nature Electronics.
Through their experiments, the team successfully showcased the significant potential of brain organoids in revolutionizing reservoir computing. This type of artificial neural network captures and retains information based on a sequence of electrical stimulations. In a series of tests, Brainoware exhibited a remarkable ability to rapidly recognize speech patterns and perform complex nonlinear mathematical equations.