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

• URL: http://arxiv.org/abs/2602.01503v2
• Date: Wed, 04 Feb 2026 19:38:32 GMT
• Title: Governance at the Edge of Architecture: Regulating NeuroAI and Neuromorphic Systems
• Authors: Afifah Kashif, Abdul Muhsin Hameed, Asim Iqbal,
• Abstract summary: Current AI governance frameworks are built for static, centrally trained artificial neural networks on von Neumann hardware.<n>NeuroAI systems, embodied in neuromorphic hardware and implemented via spiking neural networks, break these assumptions.
• Score: 4.460583138505673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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