受大脑启发的无监督聚类方法，能自动发现类别、检测新奇事物并适应变化。
Brain-Inspired Perspective on Configurations: Unsupervised Similarity and Early Cognition
- 基于吸引力与排斥力的单参数框架，实现层次化组织。
- 在新奇检测上达到87% AUC，动态演化中稳定性提升35%。
- 适合研究早期认知建模或脑启发AI的科研人员。
婴儿在无监督情况下可发现类别、检测新奇事物并适应新情境，这对当前机器学习仍是挑战。本文提出一种受大脑启发的配置视角，即一种有限分辨率的聚类框架，仅用一个分辨率参数和吸引-排斥动力学，即可实现层次化组织、对新奇事物敏感以及灵活适应。为评估这些特性，我们引入mheatmap，提供比例热图与重分配算法，公平评估多分辨率与动态行为。在多个数据集上，该方法在标准聚类指标上表现优异，在新奇检测中达87% AUC，动态类别演化中稳定性提升35%。结果表明，配置模型是早期认知分类的原理性计算模型，也是迈向脑启发人工智能的重要一步。
原文摘要 · Abstract (English)
Infants discover categories, detect novelty, and adapt to new contexts without supervision-a challenge for current machine learning. We present a brain-inspired perspective on configurations, a finite-resolution clustering framework that uses a single resolution parameter and attraction-repulsion dynamics to yield hierarchical organization, novelty sensitivity, and flexible adaptation. To evaluate these properties, we introduce mheatmap, which provides proportional heatmaps and reassignment algorithm to fairly assess multi-resolution and dynamic behavior. Across datasets, configurations are competitive on standard clustering metrics, achieve 87% AUC in novelty detection, and show 35% better stability during dynamic category evolution. These results position configurations as a principled computational model of early cognitive categorization and a step toward brain-inspired AI.
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