Neural Functional Alignment Space: Brain-Referenced Representation of Artificial Neural Networks

• URL: http://arxiv.org/abs/2603.00793v1
• Date: Sat, 28 Feb 2026 19:48:27 GMT
• Title: Neural Functional Alignment Space: Brain-Referenced Representation of Artificial Neural Networks
• Authors: Ruiyu Yan, Hanqi Jiang, Yi Pan, Xiaobo Li, Tianming Liu, Xi Jiang, Lin Zhao,
• Abstract summary: We propose a brain-referenced framework for characterizing artificial neural networks on equal functional grounds.<n>NFAS departs from conventional alignment approaches by modeling the intrinsic dynamical evolution of stimulus representations across network depth.<n>Across 45 pretrained models spanning vision, audio, and language, NFAS reveals structured organization within this brain-referenced space.
• Score: 15.491307802291836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose the Neural Functional Alignment Space (NFAS), a brain-referenced representational framework for characterizing artificial neural networks on equal functional grounds. NFAS departs from conventional alignment approaches that rely on layer-wise features or task-specific activations by modeling the intrinsic dynamical evolution of stimulus representations across network depth. Specifically, we model layer-wise embeddings as a depth-wise dynamical trajectory and apply Dynamic Mode Decomposition (DMD) to extract the stable mode. This representation is then projected into a biologically anchored coordinate system defined by distributed neural responses. We also introduce the Signal-to-Noise Consistency Index (SNCI) to quantify cross-model consistency at the modality level. Across 45 pretrained models spanning vision, audio, and language, NFAS reveals structured organization within this brain-referenced space, including modality-specific clustering and cross-modal convergence in integrative cortical systems. Our findings suggest that representation dynamics provide a principled basis for

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