NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence

Abstract: Neuroscience and 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 paper identifies key AI limitations including lack of physical embodiment, inflexible learning, and high energy consumption.
• The paper proposes neuroscience-inspired strategies such as sensorimotor co-design, multi-scale memory systems, and sparse, event-driven computation.
• The paper outlines a decadal research roadmap with institutional support and neuromorphic hardware to foster robust, continual, and efficient AI systems.

NeuroAI and Beyond: Bridging Advances in Neuroscience and Artificial Intelligence

Introduction

"NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence" (2604.18637) formulates a comprehensive research and development roadmap advocating for the systematic integration of contemporary neuroscience principles into AI system design. The authors argue that although AI, particularly deep learning, has drawn inspiration from neuroscience, recent progress in both fields has not led to deep structural integration. They identify three fundamental limitations of current AI—lack of physical embodiment, brittle and inflexible learning, and unsustainable energy and data inefficiency—that stem from architectural divergences with biological intelligence. The paper delineates plausible neuroscientific principles that directly address these limitations and postulates institutional, infrastructural, and educational changes critical for progress.

Characterization of Current AI Capability Gaps

The paper provides a precise taxonomy of AI's principal limitations:

1. Embodiment and Physical Interaction Deficit: Current AI excels in symbolic and language-based cognition but fails to generalize skills or interact robustly in the physical world. Unlike animals, even the most advanced robots lack adaptive sensorimotor proficiency, exposing a dissociation between computational advances and real-world utility.
2. Inflexible, Non-continual Learning: State-of-the-art models are unable to update their knowledge through ongoing experience without catastrophic forgetting. Post-deployment, their learning is effectively static, with mechanisms such as fine-tuning or RAG only offering superficial, non-architectural solutions. These systems also lack mechanisms for calibrated self-assessment of uncertainty and competence.
3. Energy and Data Inefficiency: Modern AI consumes orders of magnitude more power and data than biological brains. The prevailing von Neumann architecture and dense activation patterns in deep networks are fundamentally mismatched with the sparse, event-driven, and highly localized computation observed in nervous systems.

Neuroscience-Inspired Architectural Principles

For each identified gap, the paper outlines explicit neuroscientific paradigms with direct technical implications for AI systems:

Embodiment

• Co-design of Body and Controller: Biological evolution shapes neural architectures in concert with the physical bodies they control. Embodiment confers adaptive behaviors by offloading and distributing computation across morphological and neural substrates. Robotic and AI architectures should similarly integrate sensor, actuator, and control design to exploit physical priors and environmental affordances.
• Prediction Through Interaction: The brain's hierarchical predictive processing, exemplified by predictive coding theories, enables efficient perception, causality inference, and motor control. Training AI agents via action-conditioned predictive modeling—rather than passive dataset-based learning—supports robust generalization and causal reasoning.

Robust, Continual Learning

• Multi-scale, Complementary Memory Systems: Biological learning spans multiple timescales, from short-term to lifelong and evolutionary. Hippocampal-cortical interactions, sleep-driven consolidation, and neuromodulatory signals orchestrate a spectrum of plasticity, enabling both stability and adaptability. Complementary learning systems offer an explicit architectural solution to the stability-plasticity dilemma, critical for continual learning in artificial agents.
• Neuromodulatory Control: Brain systems utilize reward prediction errors, novelty detection, and context-dependent gating (dopamine, norepinephrine, acetylcholine), dynamically modulating learning rates and exploration. Integration of such modulatory control in AI can realize environment- and state-dependent plasticity, overcoming the rigidity of current optimization paradigms.
• Hierarchical, Layered Control Architectures: The brain implements layered control, with fast, hardwired reflexes at lower levels and flexible, learned plans at higher levels. Robust AI systems must formalize this hybrid design, embedding formal safety guarantees at the lowest level and data-driven adaptation at higher levels, rather than relying on brittle end-to-end solutions.

Efficiency

• Sparse, Event-Driven Computation: The nervous system's reliance on spikes, hierarchical feedback, and extremely sparse activity is central to its energy efficiency. Neuromorphic hardware—event-driven processors, spiking neural networks, co-located memory and computation—should become the substrate of next-generation AI, instead of retrofitting dense models onto energy-inefficient, synchronous digital hardware.
• Developmental and Evolutionary Priors: Biological networks are initialized with rich priors, shaped by evolution and development, not random weights. AI models should similarly exploit developmental programs, embedding strong inductive biases suited for the target domain and continual adaptation.

Research Roadmap and Institutional Recommendations

The authors propose a decadal, milestone-driven roadmap, emphasizing:

• Embodied Digital Twins: Near-term (0–5 years) goals include simulations and neuromorphically implemented digital twins of small animal brains, which serve as architectural testbeds for embodied sensorimotor intelligence. Medium- and long-term goals target scalable primate and human brain twins with direct translational value.
• Real-world Robotic Integration: Progressive advancement from fine-tuned manipulation and event-based sensing (short-term) to fleet learning, standardized robotic operating systems (mid-term), and autonomous, ethical, socially interactive robots (long-term).
• Continual, Hierarchical AI Learning: Pushing modules for multi-timescale memory, neuromodulatory-inspired learning, and developmental initialization into benchmarked AI systems, and scaling toward self-organizing agents with lifelong, fleet-based knowledge accumulation.
• Scalable, Efficient Hardware: From leveraging commercially available NPUs for edge NeuroAI to custom heterogeneous 3D neuromorphic architectures, culminating in sub-kilowatt AI supercomputers.
• Institutional Support: Multidisciplinary training at all educational stages, open-access hardware/interfaces akin to MOSIS, robust research infrastructure, and common standards for models and data formats. The paper emphasizes the necessity for investment in “translational” researchers fluent in both neuroscience and engineering.

Implications and Future Directions

The position that the next substantive advances in AI architectures will require a turn toward mechanistic neuroscientific insights is strongly argued. The programmatic stance is that computational neuroscience is now sufficiently mature to offer implementable, quantitatively specified algorithmic and architectural priors—in contrast with earlier eras where inspiration was limited to loose analogies.

If realized, NeuroAI will enable agents capable of robust online adaptation, grounding in physical realities, and operation across diverse, resource-constrained environments. Practically, this unlocks new applications in assistive robotics, embodied agents, and edge AI; theoretically, it promises a deeper understanding of the computational bases of natural intelligence. The roadmap also positions NeuroAI as critical for the long-term sustainability and democratization of AI, challenging the current trajectory of centralized, resource-intensive LLMs.

Conclusion

This paper articulates a rigorous, practical agenda for bridging neuroscience and AI. By specifying concrete neuroscientific mechanisms—embodiment, prediction, multi-scale memory, neuromodulation, sparse event-driven computation—as the foundation of future AI architectures, it shifts the research focus from data-centric scaling toward architectural innovation. The institutional and technical recommendations, if adopted, would reprioritize academic research as the source of foundational advances, opening new directions beyond the language-centric, transformer-dominant mainstream. The proposed NeuroAI initiative thereby aims to drive both the theoretical deepening and practical expansion of artificial intelligence (2604.18637).