NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence
Summary: arXiv:2604.18637v1 Announce Type: cross
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.
The Need for Integration
As the fields of neuroscience and artificial intelligence continue to evolve, the integration of these domains has become increasingly crucial. Despite significant advancements in both fields, the synergies that could be derived from their intersection remain largely untapped. The workshop convened by the National Science Foundation highlights the pressing need to bridge this gap to enhance AI systems.
Identifying Capability Gaps
Three primary capability gaps were identified in current AI technologies:
- Inability to Interact with the Physical World: Current AI systems often function in isolated environments, lacking the ability to adapt and respond to real-world dynamics.
- Brittle Learning Systems: Many AI models struggle with generalization, leading to performance issues when faced with novel situations.
- Energy and Data Inefficiency: The demand for vast amounts of data and energy consumption in training AI systems is unsustainable.
Neuroscience Principles to Address Gaps
To address these challenges, several neuroscience principles have emerged as potential solutions:
- Co-design of Body and Controller: Developing AI systems that can physically interact with their environment, akin to biological organisms.
- Prediction through Interaction: Utilizing predictive models that learn from real-time interactions to enhance decision-making processes.
- Multi-scale Learning with Neuromodulatory Control: Implementing learning mechanisms that mimic biological neural processes, allowing for adaptability and resilience.
- Hierarchical Distributed Architectures: Creating networks that replicate the structure of biological brains, promoting efficient information processing.
- Sparse Event-driven Computation: Reducing energy consumption by mimicking the event-driven nature of neural communication.
A Roadmap for the Future
The workshop proposes a comprehensive research roadmap structured around these principles. This roadmap is categorized into near, mid, and long-term goals, each aimed at progressively addressing the identified capability gaps. Achieving these objectives hinges on fostering a new generation of researchers equipped with interdisciplinary training that spans both neuroscience and engineering.
Conclusion
In conclusion, the concept of NeuroAI presents a promising avenue for overcoming the limitations of current AI systems. By harnessing insights from neuroscience, we can not only enhance the capabilities of artificial intelligence but also deepen our understanding of biological neural computation. The successful realization of this vision will depend on the establishment of supportive institutional frameworks that promote interdisciplinary collaboration, ethical standards, and access to necessary resources.