# Governance at the Edge of Architecture: Regulating NeuroAI and Neuromorphic Systems

arXiv: https://arxiv.org/abs/2602.01503 · HTML: https://mlsanity.com/paper/2602.01503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/2106.07030/) (2021)
- [The Brussels Effect and Artificial Intelligence: How EU regulation will impact the global AI market](https://mlsanity.com/paper/2208.12645/) (2022)
- [On the Limitations of Compute Thresholds as a Governance Strategy](https://mlsanity.com/paper/2407.05694/) (2024)
- [Nengo and low-power AI hardware for robust, embedded neurorobotics](https://mlsanity.com/paper/2007.10227/) (2020)
- [Hardware-Enabled Mechanisms for Verifying Responsible AI Development](https://mlsanity.com/paper/2505.03742/) (2025)
- [Data-Centric AI Governance: Addressing the Limitations of Model-Focused Policies](https://mlsanity.com/paper/2409.17216/) (2024)

## Abstract (arXiv)

Current AI governance frameworks, including regulatory benchmarks for accuracy, latency, and energy efficiency, are built for static, centrally trained artificial neural networks on von Neumann hardware. NeuroAI systems, embodied in neuromorphic hardware and implemented via spiking neural networks, break these assumptions. This paper examines the limitations of current AI governance frameworks for NeuroAI, arguing that assurance and audit methods must co-evolve with these architectures, aligning traditional regulatory metrics with the physics, learning dynamics, and embodied efficiency of brain-inspired computation to enable technically grounded assurance.