Brain-Inspired Perspective on Configurations: Unsupervised Similarity and Early Cognition

2025-10-22

AI Summary

This work addresses three core challenges in machine learning—unsupervised category discovery, novelty detection, and adaptation to dynamic environments—drawing inspiration from infant early cognition. We propose a unified clustering framework grounded in finite-resolution representation and attraction-repulsion dynamics. A single resolution parameter governs hierarchical organization, novelty awareness, and online evolution, enabling a single configurable model to jointly tackle all three tasks. We introduce two novel technical contributions: (i) *mheatmap*, a multi-resolution visualization method for interpretable cluster dynamics, and (ii) a redistribution-based evaluation algorithm ensuring fair, quantitative assessment of both multi-resolution behavior and temporal adaptation. Experiments demonstrate competitive performance on standard clustering benchmarks, an 87% AUC for novelty detection, and a 35% improvement in stability under dynamic category evolution. Our framework provides an interpretable, scalable computational paradigm for modeling early cognitive development.

Technology Category

• Machine Learning: Clustering
• Computer Vision: Representation Learning for Vision
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Unsupervised learning for category discovery and novelty detection
• Developing brain-inspired clustering with hierarchical organization and adaptation
• Creating computational models for early cognitive categorization processes

Innovation

• Unsupervised clustering with attraction-repulsion dynamics
• Single resolution parameter enabling hierarchical organization
• Proportional heatmaps algorithm for multi-resolution evaluation