NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence
Anthony Zador, Jean-Marc Fellous, Terrence Sejnowski, Gina Adam, James B Aimone + 26 more
TLDR
This paper outlines how NeuroAI, by integrating neuroscience principles, can overcome current AI limitations in interaction, learning, and efficiency.
Key contributions
- Identifies three core AI gaps: physical interaction, brittle learning, and energy/data inefficiency.
- Proposes neuroscience principles like co-design, multi-scale learning, and sparse computation to address these gaps.
- Outlines a NeuroAI research roadmap spanning near, mid, and long-term development horizons.
- Advocates for interdisciplinary training and institutional support for future NeuroAI researchers.
Why it matters
This paper provides a critical roadmap for advancing AI by integrating neuroscience principles. It addresses fundamental AI limitations, promising more robust, efficient, and interactive intelligent systems while deepening our understanding of biological computation.
Original 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.
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