# SatSOM: Saturation Self-Organizing Maps for Continual Learning

arXiv: https://arxiv.org/abs/2506.10680 · HTML: https://mlsanity.com/paper/2506.10680/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [CORe50: a New Dataset and Benchmark for Continuous Object Recognition](https://mlsanity.com/paper/1705.03550/) (2017)
- [Memory Efficient Experience Replay for Streaming Learning](https://mlsanity.com/paper/1809.05922/) (2018)
- [Continual Learning with Self-Organizing Maps](https://mlsanity.com/paper/1904.09330/) (2019)
- [Dendritic Self-Organizing Maps for Continual Learning](https://mlsanity.com/paper/2110.13611/) (2021)
- [Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps](https://mlsanity.com/paper/2402.12465/) (2024)

## Built on by (most cited first, 1 of 1)

- [Self-Organizing Maps with Optimized Latent Positions](https://mlsanity.com/paper/2604.13622/) (2026)

## Abstract (arXiv)

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.