Zador, Anthony, Fellous, Jean-Marc, Sejnowski, Terrence, Adam, Gina, Aimone, James B, Akwaboah, Akwasi, Aloimonos, Yiannis, Alonso, Carmen Amo, Bartolozzi, Chiara, Bennington, Michael J, Berry, Michael, Brunton, Bing W, Cauwenberghs, Gert, Chiel, Hillel J, Delbruck, Tobi, Doyle, John, Eshraghian, Jason, Etienne-Cummings, Ralph, Fermuller, Cornelia, Jacobsen, Matthew, Minai, Ali A, Oakley, Barbara, Ororbia, Alexander G, Paton, Joe, Richards, Blake, Sandamirskaya, Yulia, Sengupta, Abhronil, Shamma, Shihab, Stryker, Michael P, Yoo, Seong Jong, Zucker, Steven W (April 2026) NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence. arXiv. ISSN 2331-8422 (Submitted)
![[thumbnail of 10.48550.arXiv.2604.18637.pdf]](https://repository.cshl.edu/style/images/fileicons/application_pdf.png) |
PDF
10.48550.arXiv.2604.18637.pdf - Submitted Version Available under License Creative Commons Public Domain Dedication. Download (297kB) |
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.
| Item Type: | Paper |
|---|---|
| Subjects: | neurobiology neurobiology > neuroscience |
| CSHL Authors: | |
| Communities: | CSHL labs > Zador lab |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 19 April 2026 |
| Date Deposited: | 01 May 2026 14:50 |
| Last Modified: | 01 May 2026 14:50 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/42184 |
Actions (login required)
 |
Administrator's edit/view item |