Brain-Inspired Perspective on Configurations: Unsupervised Similarity and Early Cognition
This addresses the problem of building brain-inspired AI systems that can learn like infants, though it appears incremental as a specific clustering method.
The paper tackles the challenge of unsupervised learning for categorization, novelty detection, and adaptation by proposing a brain-inspired clustering framework called configurations, which achieves 87% AUC in novelty detection and 35% better stability in dynamic category evolution.
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 a 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.