Saturation Self-Organizing Map

Intelligent agents navigating real-world environments must continuously learn, adapting to new information while retaining prior knowledge [14]. This ability, known as continual or lifelong learning, poses a significant challenge in modern machine learning. Most artificial neural systems struggle with catastrophic forgetting [5], where training on new tasks or data distributions abruptly erases previously learned information. This phenomenon stems from the shared nature of representations in standard neural networks, where updating weights for new data can overwrite information critical for past tasks. Overcoming catastrophic forgetting is crucial for developing robust, adaptable systems that can learn incrementally from data streams, rather than being retrained from scratch. Numerous approaches have been proposed to mitigate catastrophic forgetting, ranging from regularization techniques and memory replay to architectural modifications. However, many state-of-the-art solutions, despite showing promising results, require substantial changes to model structure or training procedures. This often limits their compatibility with widely used and well-understood machine learning frameworks.