NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence
Anthony Zador, Jean-Marc Fellous, Terrence Sejnowski, Gina Adam, James B Aimone, Akwasi Akwaboah, Yiannis Aloimonos, Carmen Amo Alonso, Chiara Bartolozzi, Michael J. Bennington, Michael Berry, Bing W. Brunton, Gert Cauwenberghs, Hillel J. Chiel, Tobi Delbruck, John Doyle

TL;DR
This paper discusses how integrating neuroscience principles into AI can address current limitations in physical interaction, learning robustness, and efficiency, proposing a research roadmap and institutional changes.
Contribution
It identifies key neuroscience-inspired principles to enhance AI capabilities and outlines a comprehensive research roadmap for NeuroAI development across different time horizons.
Findings
Neuroscience principles can improve AI interaction with the physical world.
Hierarchical and sparse computation models can enhance AI efficiency.
A multidisciplinary approach is essential for advancing NeuroAI.
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…
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