NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910184668921856 |
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| author | 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 II, Alexander G. Paton, Joe Richards, Blake Sandamirskaya, Yulia Sengupta, Abhronil Shamma, Shihab Stryker, Michael P. Yoo, Seong Jong Zucker, Steven W. |
| author_facet | 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 II, Alexander G. Paton, Joe Richards, Blake Sandamirskaya, Yulia Sengupta, Abhronil Shamma, Shihab Stryker, Michael P. Yoo, Seong Jong Zucker, Steven W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18637 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence 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 II, Alexander G. Paton, Joe Richards, Blake Sandamirskaya, Yulia Sengupta, Abhronil Shamma, Shihab Stryker, Michael P. Yoo, Seong Jong Zucker, Steven W. Neurons and Cognition Artificial Intelligence Computers and Society 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. |
| title | NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence |
| topic | Neurons and Cognition Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2604.18637 |