Brain-Inspired AI Needs New Rules As Current Checks Simply Don’t Apply

Researchers are increasingly focused on the challenges of governing NeuroAI and neuromorphic systems, a field where current regulatory approaches fall short. Afifah Kashif from the University of Cambridge, Abdul Muhsin Hameed from the University of Washington, and Asim Iqbal from Cornell University, et al., demonstrate that existing governance frameworks, designed for static artificial neural networks running on conventional hardware, are inadequate for these fundamentally different architectures. This paper highlights a critical need to reassess assurance and audit methods, advocating for a co-evolution of regulation alongside brain-inspired computation to ensure technically sound and effective oversight of NeuroAI’s unique physics, learning dynamics, and efficiency. Understanding these limitations and proposing adaptive governance is significant as NeuroAI promises substantial advancements in energy efficiency and real-time processing, but requires careful management to realise its potential safely and responsibly.

Neuromorphic systems and the inadequacy of current AI governance frameworks necessitate proactive and adaptive regulatory approaches

Scientists are redefining the boundaries of artificial intelligence governance with research into NeuroAI systems. These novel systems, built on neuromorphic hardware and utilising spiking neural networks, challenge the assumptions underpinning current regulatory benchmarks for accuracy, latency, and energy efficiency.

This work examines the limitations of existing AI governance frameworks when applied to NeuroAI, proposing that methods for assurance and audit must evolve alongside these advanced architectures. The study details how aligning traditional regulatory metrics with the unique physics, learning dynamics, and inherent efficiency of brain-inspired computation is crucial for technically grounded assurance.

NeuroAI represents a convergence of neuroscience and artificial intelligence, aiming to create smarter, more efficient systems by leveraging insights from the brain. Neuromorphic computing departs from conventional von Neumann architecture by integrating memory and computation, operating asynchronously and in an event-driven manner.

At the algorithmic level, this manifests in spiking neural networks, which communicate via discrete spikes encoding information in rate and timing, often employing local learning rules like spike-timing-dependent plasticity. This contrasts with artificial neural networks, such as those powering ChatGPT, which rely on continuous activations and global error backpropagation.

Collectively, NeuroAI bridges the gap between scientific understanding and hardware implementation, signalling a paradigm shift in neural computation. Neuromorphic event-driven vision systems are now under development for implantable health monitors, capable of detecting neural or cardiovascular anomalies in real time with exceptionally low power requirements suitable for continuous edge deployment.

This research highlights the need for governance to keep pace with such advancements, ensuring safety and societal impact are embedded into the design of algorithms and hardware from the outset. Recent global efforts to regulate AI, including the EU AI Act, the U.S. NIST AI Risk Management Framework, and China’s AI Safety Governance Framework, are largely designed for static, high-compute, centrally trained models.

This work demonstrates that these frameworks struggle to capture the adaptive and event-driven behaviour of neuromorphic and NeuroAI systems. The research proposes a new approach, exemplified by frameworks like NeuroBench, which ties algorithmic performance to hardware efficiency, reframing evaluation from raw compute to a systems-level audit. This paper explores how metrics of efficiency, adaptability, and embodiment can be translated into regulatory language, facilitating a responsible transition of NeuroAI from laboratory prototypes to real-world applications.

Spiking neural network evaluation using the NeuroBench framework is now readily available to researchers

Neuromorphic computing, embodying the convergence of neuroscience and artificial intelligence, departs from traditional von Neumann architecture by co-locating memory and computation and operating asynchronously and event-driven. This shift is algorithmically realised through spiking neural networks, which utilise discrete spikes encoding information in rate and timing, often incorporating local learning rules like spike-timing-dependent plasticity.

These networks contrast with artificial neural networks, which rely on continuous activations and global error backpropagation, forming the basis of modern deep learning. The research highlights a critical need for co-evolution between AI governance and NeuroAI architectures, as current regulatory benchmarks for accuracy, latency, and energy efficiency are designed for static, centrally trained systems.

To address this, the study references NeuroBench, a device-agnostic framework evaluating performance across task, model, and platform layers. NeuroBench reports accuracy alongside latency, power consumption, and energy per sample, utilising open reference code to facilitate a systems-level audit linking algorithmic performance to hardware efficiency.

This work extends the NeuroBench foundation to explore translating metrics of efficiency, adaptability, and embodiment into regulatory language. The analysis demonstrates that existing AI governance frameworks, created for static, high-compute models, struggle to capture the adaptive and event-driven behaviour inherent in neuromorphic and NeuroAI systems.

Consequently, assurance and audit methods must align traditional regulatory metrics with the underlying physics and learning dynamics of brain-inspired computation to enable responsible transition from laboratory prototypes to real-world applications. The study considers both binding laws establishing enforceable obligations and non-binding guidelines, recognising the need for a comprehensive global understanding of AI governance.

Neuromorphic computing and the inadequacy of existing AI governance metrics necessitate novel evaluation frameworks

Current governance frameworks, including regulatory benchmarks for accuracy, latency, and energy efficiency, are designed for static, centrally trained artificial neural networks on von Neumann hardware. NeuroAI systems, implemented via spiking neural networks on neuromorphic hardware, challenge these established assumptions.

The study highlights limitations within current governance frameworks when applied to NeuroAI, asserting that assurance and audit methods must evolve alongside these architectures. A centralized results schema ensures all submissions include standardized metrics and metadata, enabling reproducible, cross-device comparison and transparent assessment of neuromorphic progress.

Compute accounting currently serves as the dominant proxy for capability and risk, with the EU AI Act defining a systemic-risk threshold of approximately 1025 FLOPs. The 2023 U.S. Executive Order mandates reporting for training runs exceeding 1026 FLOPs, and similar limits are employed in export-control regimes.

However, neuromorphic processors do not adhere to this premise, utilizing event-driven and sparse computation measured in spikes per second, rather than synchronous floating-point operations. Energy expenditure is proportional to the number of meaningful events, not clock cycles. The research demonstrates that neuromorphic hardware slips through existing AI regulations due to fundamental differences in operation.

Conventional governance assumes that more FLOPs equate to greater capability and risk, a premise invalidated by the event-driven nature of neuromorphic systems. A 3-billion-parameter transformer-decoder LLM, derived from IBM’s Granite-8B-Code-Base model and quantized to INT4 weights and activations, was used in benchmarking.

Despite using far less power, NorthPole achieves substantially higher energy efficiency and lower latency than metrics based solely on FLOPs would suggest. The study emphasizes that reconstructing network attention, such as a heatmap, requires correlating spike trains across millions of neurons and microsecond-scale timestamps, yielding dynamic, non-stationary, and difficult-to-interpret results.

Auditability in NeuroAI necessitates dynamical-systems analysis, specifically characterization of attractor landscapes, oscillatory coupling, spike synchrony, and stability margins, rather than static weight maps. Reproducing experiments within neuromorphic systems is nuanced, as microscopic weight differences can alter entire network dynamics, making traditional auditing paradigms ineffective.

Neuromorphic systems necessitate revised governance approaches to address unique ethical and security challenges

Current artificial intelligence governance frameworks, designed for conventional computing systems, are ill-suited for neuroAI systems built on neuromorphic hardware. Traditional regulatory benchmarks focusing on accuracy, latency, and energy efficiency do not align with the unique characteristics of these brain-inspired architectures.

NeuroAI’s continuous learning cycles and interactions with real-world data make established methods of dataset compartmentalisation and auditing impractical, effectively blurring the lines between model development and deployment. This misalignment stems from the fundamental differences in how these systems operate.

Unlike conventional AI, neuroAI learns continuously, adapts locally, and utilises distributed, hardware-integrated memory, creating challenges for auditing, exporting, or benchmarking. The implications are particularly significant in high-stakes applications such as healthcare devices and autonomous vehicles, where safety and accountability are paramount.

Existing AI risk assessments, including those used for export controls, may also fail to accurately evaluate neuromorphic chips due to their differing computational characteristics. The authors acknowledge that this analysis concentrates on a subset of AI governance frameworks and offers limited exploration of sector-specific regulations.

Addressing these governance gaps is becoming increasingly urgent as neuromorphic computing transitions from research labs to real-world applications. Future work must focus on developing assurance and audit methods that co-evolve with neuroAI architectures, aligning regulatory metrics with the underlying physics and learning dynamics of brain-inspired computation. This will require a shift from hypothetical discussions to practical implementation, particularly as these systems power safety-critical applications and the supply chain for neuromorphic chips matures.