NSF Award
2422352
The first living organisms sensed their environment (input) and responded to it (output). They computed their responses. Today, we write and execute complex programs. The fastest computers are slower, more energy-intensive, and less complex than the human brain. Harnessing neuronal computing capabilities would represent a giant leap forward in computing power, speed, and efficiency. This project will establish a three-dimensional (3D) network of neurons as a brain analog. The small clumps of neural cells, called organoids, act as the nodes of the network. This research will be organized into three distinct subjects. The first is biocomputing theory to identify functional neuronal networks. The second is to develop organoid culture and hardware interfaces to maintain stable brain organoid cultures. The instrumentation will stimulate and record neuronal activity. The third is discussion and analysis of ethical concerns identified within the research to build awareness, literacy, and reasoning capacity of the researchers and the greater scientific community. <br/><br/>This research aims to develop a mechanistic understanding of how to train neuronal networks in organoids. The structure and physiology of neurons will be related to their role in computation and learning. It will establish concrete examples of how to map computation and learning paradigms onto 3D biocomputing networks. This project leverages and extends existing technologies for culturing neurons in patterned 3D microenvironments and for recording/stimulating neurons using high density microelectrode arrays and optogenetics. There are three primary scientific hypotheses underlying the project. First, that 3D cortical organoids can be trained to respond selectively to one pattern out of many distractor stimuli. Second, that this association can be established simultaneously for many distinct patterns. Finally, that training based on closed-loop approaches incorporating mechanistic insight will be more effective than either open-loop approaches or closed-loop approaches that do not take into consideration underlying neural structure.<br/><br/>This project is jointly funded by the Emerging Frontiers in Research and Innovation Program (BEGIN OI) and the Directorate for Mathematical and Physical Sciences.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
