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

Juntang Wang, Yihan Wang, Hao Wu, Dongmian Zou, Shixin Xu

TL;DR

This work presents a brain-inspired finite-$\gamma$ clustering framework called configurations, unifying hierarchical organization, novelty sensitivity, and flexible adaptation under a single parameter. It defines an energy-based partitioning scheme with attraction and repulsion weights and introduces ${\bm{\Omega}}_\gamma$ to capture multi-resolution structure, along with Parallel-DT to identify stable plateaus. To evaluate dynamic, multi-resolution clustering in a cognitively aligned way, the authors deploy mheatmap, a mosaic visualization with RMS alignment that fairly compares partitions across resolutions and over time. Empirical results across several datasets show strong clustering performance and brain-like behaviors, including 87% AUC in novelty detection and 35% improved stability during category evolution, supporting the claim that configurations can serve as a principled model of early cognitive categorization and a pathway toward brain-inspired AI.

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.

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

TL;DR

This work presents a brain-inspired finite- clustering framework called configurations, unifying hierarchical organization, novelty sensitivity, and flexible adaptation under a single parameter. It defines an energy-based partitioning scheme with attraction and repulsion weights and introduces to capture multi-resolution structure, along with Parallel-DT to identify stable plateaus. To evaluate dynamic, multi-resolution clustering in a cognitively aligned way, the authors deploy mheatmap, a mosaic visualization with RMS alignment that fairly compares partitions across resolutions and over time. Empirical results across several datasets show strong clustering performance and brain-like behaviors, including 87% AUC in novelty detection and 35% improved stability during category evolution, supporting the claim that configurations can serve as a principled model of early cognitive categorization and a pathway toward brain-inspired AI.

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.
Paper Structure (13 sections, 2 theorems, 1 equation, 4 figures, 2 tables)

This paper contains 13 sections, 2 theorems, 1 equation, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Configurations: Def. and Brain-Inspired Perspective
  4. Recap of Configuration Definition
  5. Brain-Inspired Properties of Configurations
  6. The mheatmap Framework
  7. Mosaic Heatmap Visualization
  8. RMS Alignment Algorithm
  9. Implementation and Availability
  10. Experiments
  11. Discussion and Conclusion
  12. Acknowledgments.
  13. Disclosure of Interests.

Key Result

proposition thmcounterproposition

There always exist two special configurations: ${\bm{\omega}}_0 := {\bm{\Omega}}_0$ is the coarsest configuration, where all items are in the same cluster. ${\bm{\omega}}_\infty := {\bm{\Omega}}_\infty$ is the finest configuration, where each item is in a separate cluster.

Figures (4)

  • Figure 1: Configuration lineage and energy landscapes. (a)&(b) Configuration lineages: $\gamma$ controls hierarchical granularity from coarse to fine. (c)&(d) Energy landscapes: Axes show attraction $h_a$ and repulsion $h_r$.
  • Figure 2: (a) A schematic example of merge and split happens as $\gamma$ increases from $i$ to $j$. (b) Comparison of normal and mosaic heatmap for a confusion matrix. Mosaic heatmap provides more intuitive visualization with interpretable diagonal structure, when cases like a split of $1$ into $1$ and $2$ happen.
  • Figure 3: One case of clustering versus GT before and after RMS alignment.
  • Figure 4: Brain-inspired capabilities of configurations. (a) Superordinate categories emerge at low $\gamma$ (0.2--0.6), basic-level at high $\gamma$ (1.2--1.8). Plateaus show stable organizational scales. (b) Energy distributions distinguish novel from familiar stimuli (87% AUC), paralleling infant habituation. (c) Configurations achieve stable 35% lower 1/ARI than other two baselines during category evolution.

Theorems & Definitions (4)

  • definition thmcounterdefinition
  • proposition thmcounterproposition
  • definition thmcounterdefinition
  • proposition thmcounterproposition