用大脑响应定义神经网络表征空间，揭示跨模态统一规律
Neural Functional Alignment Space: Brain-Referenced Representation of Artificial Neural Networks
- 通过动态模式分解捕捉网络深度中的表征演化轨迹
- 45个预训练模型显示视觉/语音/语言模态的聚类与融合特征
- 适合研究神经网络与大脑机制对齐的跨模态分析者
我们提出神经功能对齐空间（NFAS），一种基于大脑响应的表征框架，用于在相同功能基础上表征人工神经网络。与依赖层间特征或任务特定激活的传统对齐方法不同，NFAS通过建模刺激表征随网络深度的内在动态演化来实现。具体而言，将层间嵌入视为深度上的动态轨迹，并应用动态模式分解（DMD）提取稳定模态。该表示被投影到由分布式神经响应定义的生物锚定坐标系中。我们还引入信噪一致性指数（SNCI）以量化跨模型在模态层面的一致性。在涵盖视觉、音频和语言的45个预训练模型上，NFAS揭示了该脑参考空间中的结构化组织，包括模态特异性聚类以及整合皮层系统中的跨模态汇聚。结果表明，表征动态为构建神经网络与大脑之间可解释对齐关系提供了原则性基础。
原文摘要 · Abstract (English)
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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