Background: NeuroAI is an emerging area of research allowing for the re-examining of fundamental principles of cognition, neural control, and intelligent behavior that may be shared by living organisms and AI. Convergent approaches in NeuroAI research include data-driven modeling and representational analysis of neural systems; neuromorphic engineering of computing devices and neural interfaces; and new frameworks for understanding how large neural networks learn and construct knowledge. The first decade of the BRAIN Initiative delivered revolutionary tools and launched transformative projects integrating vast amounts of complex neural, cognitive, and behavioral data. In the next decade and beyond, BRAIN’s mission to understand the brain could use those tools and data to drive reciprocal advances in NeuroAI, neuroscience, and brain-like computing technologies.

Who: The BRAIN NeuroAI Workshop will bring together senior and early-career researchers, students, and scientists and engineers who are interested in the future of brain-inspired computing, AI, and NeuroAI. Participants will include neuroscientists, computer scientists, mathematicians, and scientists and engineers from academia, industry, and government labs working in NeuroAI-related fields such as neuroscience, cognitive science, artificial intelligence, neuromorphic computing and engineering, neurorobotics, control theory, network theory, nonlinear dynamics, and beyond. The workshop welcomes attendance and participation by program officials and staff from the NIH and other science funding agencies; representatives of private foundations, not-for-profit organizations, and advocacy groups; science journalists and editorial staff of academic or industry publications; clinicians, patients, and patient advocates; and interested members of the public.

Why: AI is a critical technology undergoing a generational inflection point. The successes of large generative AI models have escaped the walls of academic and tech industry research labs. As AI technology becomes widely adopted, it is critical to understand the principles of its successes as well as its challenges, including unsustainable energy-consumption. Several converging trends highlight potential alternative paths forward. First, large pre-trained artificial neural nets have immense demonstrative power to synthesize and provide intuitive interfaces to vast amounts of data. Second, the BRAIN Initiative continues to collect and integrate vast amounts of neural, cognitive, and behavioral datasets with the potential to offer unparalleled insights into the biological basis of natural intelligence, adaptability, and resilience in humans and other animals. Third, emerging NeuroAI research is exploring alternative theories and models of the computations and behaviors of brains and artificial brain-like learning systems. Discovering fundamental principles of intelligence through BRAIN and NeuroAI could potentially transform neuroscience while innovating energy-efficient intelligent computing.