NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence

2026-04-18

AI Summary

This work addresses three critical bottlenecks in contemporary artificial intelligence: limited physical interaction capabilities, fragile learning mechanisms, and poor energy and data efficiency. It proposes a novel framework that systematically translates core principles from neuroscience into AI design, integrating co-design of body and controller, predictive interaction, multi-scale neuromodulatory learning, hierarchical distributed architectures, and sparse event-driven computation. By unifying these biologically inspired strategies, the framework offers a viable pathway to overcome current AI limitations while simultaneously advancing our understanding of biological neural computation. This approach establishes both theoretical and practical foundations for the emerging field of NeuroAI and fosters interdisciplinary talent development and institutional innovation.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• physical interaction
• brittle learning
• energy inefficiency
• data inefficiency
• AI limitations

Innovation

• NeuroAI
• neuromodulatory control
• event-driven computation
• multi-scale learning
• embodied intelligence