NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence
Organizations: Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. · Institute for Neural Computation, UC San Diego, La Jolla, CA, USA. · Mathematics Department, Kalaheo High School, Kailua, HI, USA. · Dept. Neurobiology, UC San Diego, La Jolla, CA, USA. · Dept. Electrical & Computer Engineering, George Washington University, Washington, DC, USA. · Neural Exploration & Research Laboratory, Sandia National Laboratories, Albuquerque, NM, USA. · Dept. Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. · Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA. · Dept. Aeronautics & Astronautics, Stanford University, Stanford, CA, USA. · Event-Driven Perception for Robotics, Italian Institute of Technology, Genoa, Italy. · Dept. Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. · Dept. Neuroscience, Princeton University, Princeton, NJ, USA. · Dept. Biology, University of Washington, Seattle, WA, USA. · Depts. Biology, Neurosciences & Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. · Inst. of Neuroinformatics, UZH-ETH Zurich, Zurich, Switzerland. · Dept. Control & Dynamical Systems, Caltech, Pasadena, CA, USA. · Dept. Electrical & Computer Engineering, UC Santa Cruz, Santa Cruz, CA, USA. · Dept. Electrical & Computer Engineering, University of Cincinnati, Cincinnati, OH, USA. · Dept. Engineering, Oakland University, Rochester, MI, USA. · Dept. Computer Science, Rochester Institute of Technology, Rochester, NY, USA. · Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal. · School of Computer Science + Montreal Neurological Institute, McGill University & Mila, The Quebec AI Institute. · Institute of Computational Life Sciences, Zurich University of Applied Sciences, Wädenswil, Switzerland. · School of Electrical Engineering & Computer Science, Penn State University, University Park, PA, USA. · Dept. Physiology, UC San Francisco, San Francisco, CA, USA. · Dept. Computer Science and Biomedical Engineering, Yale University, New Haven, CT, USA.
Abstract
Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Science Foundation in August 2025, we identify three fundamental capability gaps in current AI: the inability to interact with the physical world, inadequate learning that produces brittle systems, and unsustainable energy and data inefficiency. We describe the neuroscience principles that address each: co-design of body and controller, prediction through interaction, multi-scale learning with neuromodulatory control, hierarchical distributed architectures, and sparse event-driven computation. We present a research roadmap organized around these principles at near, mid, and long-term horizons. We argue that realizing this program requires a new generation of researchers trained across the boundary between neuroscience and engineering, and describe the institutional conditions: interdisciplinary training, hardware access, community standards, and ethics, needed to support them. We conclude that NeuroAI, neuroscience-informed artificial intelligence, has the potential to overcome limitations of current AI while deepening our understanding of biological neural computation.