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.

翻译：婴儿能够在无监督的情况下发现类别、检测新奇事物并适应新环境——这对当前机器学习构成了挑战。本文提出一种大脑启发的配置视角，这是一种有限分辨率聚类框架，通过单一分辨率参数和吸引-排斥动力学实现层次化组织、新奇敏感性和灵活适应性。为评估这些特性，我们引入mheatmap方法，其提供比例热力图和重分配算法，以公平评估多分辨率与动态行为。在多个数据集上的实验表明，该配置方法在标准聚类指标上具有竞争力，在新奇检测中达到87%的AUC值，并在动态类别演化过程中展现出35%更优的稳定性。这些结果确立了配置方法作为早期认知分类的原理性计算模型，并为实现大脑启发的AI迈出重要一步。