NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence

• URL: http://arxiv.org/abs/2604.18637v1
• Date: Sun, 19 Apr 2026 01:42:30 GMT
• Title: NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence
• Abstract summary: Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected.<n>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.<n>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.
• Score: 45.94827203099552
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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