Saturation Self-Organizing Map
This addresses the problem of catastrophic forgetting in neural systems for continual learning, but it is incremental as it extends existing SOM methods.
The paper tackles catastrophic forgetting in continual learning for Self-Organizing Maps by introducing Saturation Self-Organizing Maps (SatSOM), which uses a saturation mechanism to reduce learning rates and neighborhood radii for well-trained neurons, improving knowledge retention.
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.