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arXiv · 2506.10680

SatSOM: Saturation Self-Organizing Maps for Continual Learning

Igor Urbanik·Paweł Gajewski
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Abstract

Continual learning poses a fundamental challenge for neural systems, which often suffer from catastrophic forgetting when exposed to sequential tasks. Self-Organizing Maps (SOMs), despite their interpretability and efficiency, are not immune to this issue. In this paper, we introduce Saturation Self-Organizing Maps (SatSOM)-an extension of SOMs designed to improve knowledge retention in continual learning scenarios. SatSOM incorporates a novel saturation mechanism that gradually reduces the learning rate and neighborhood radius of neurons as they accumulate information. This effectively freezes well-trained neurons and redirects learning to underutilized areas of the map.

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BibTeXRIS

Igor Urbanik, Paweł Gajewski. 2025-06-12. SatSOM: Saturation Self-Organizing Maps for Continual Learning. https://doi.org/10.2478/jaiscr-2026-0015

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