Brain-Inspired Perspective on Configurations: Unsupervised Similarity and Early Cognition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915754572513280 |
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| author | Wang, Juntang Wang, Yihan Wu, Hao Zou, Dongmian Xu, Shixin |
| author_facet | Wang, Juntang Wang, Yihan Wu, Hao Zou, Dongmian Xu, Shixin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19229 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Brain-Inspired Perspective on Configurations: Unsupervised Similarity and Early Cognition Wang, Juntang Wang, Yihan Wu, Hao Zou, Dongmian Xu, Shixin Machine Learning 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. |
| title | Brain-Inspired Perspective on Configurations: Unsupervised Similarity and Early Cognition |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.19229 |