IU bioengineers are building the intersection of brain organoids and AI

Feng Guo, an associate professor of intelligent systems engineering at the Indiana University Luddy School of Informatics, Computing and Engineering, is addressing the technical limitations of artificial intelligence computing hardware by developing a new hybrid computing system—which has been dubbed “Brainoware”—that combines electronic hardware with human brain organoids.

Advanced AI techniques, such as machine learning and deep learning, which are powered by specialized silicon computer chips, expend enormous amounts of energy. As such, engineers have designed neuromorphic computing systems, modeled after the structure and function of a human brain, to improve the performance and efficiency of these technologies. However, these systems are still limited in their ability to fully mimic brain function, as most are built on digital electronic principles.

In response, Guo and a team of IU researchers, including graduate student Hongwei Cai, have developed a hybrid neuromorphic computing system that mounts a brain organoid onto a multielectrode assay to receive and send information. The brain organoids are brain-like 3D cell cultures derived from stem cells and characterized by different brain cell types, including neurons and glia, and brain-like structures such as ventricular zones.

“Brainoware uses 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 said. “Our approach allows for the advancement of AI computing as the organoids provide biological neural networks with certain complexity, as well as low energy consumption and fast learning.”

The team’s work appears in the newest issue of “Nature Electronics,” which features leading research from all areas of electronics.

In developing its hybrid computing system, the team demonstrated the major potential for brain organoids to advance the capabilities of reservoir computing, a type of artifical neural network based on the idea of capturing and remembering information based on a sequence of electrical stimulations. In a series of tests, Brainoware was able to quickly recognize speech patterns as well as perform complex nonlinear mathematical equations.

“Through electrical stimulation training, we were able to distinguish an individual’s vowels from a speaker pool,” Guo said. “With the training, we triggered unsupervised learning of hybrid computing systems.”

Since 2016, Guo has disclosed several inventions to the IU Innovation and Commercialization Office ranging from acoustic technologies to biomedical devices for tissue engineering. The IU ICO works closely with faculty, industry and the entrepreneurial community in Indiana to take IU innovations to market.

Guo has been awarded several major grants in recent years for his groundbreaking work on lab-on-a-chip technology with AI and an opioid overdose detection patch. His lab is currently focused on the development of intelligent biomedical systems through the innovation of AI, devices, sensors, and systems for life science and translational medicine applications.

See Also

A way to make what is called “reservoir computing” work between 33 and a million times faster

Original Article: IU bioengineers are building the intersection of brain organoids and AI

More from: Indiana University