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A lifelong learning agent is able to continually learn from potentiallyinfinite streams of pattern sensory data. One major historic difficulty inbuilding agents that adapt in this way is that neural systems struggle toretain previously-acquired knowledge when learning from new samples. Thisproblem is known as catastrophic forgetting (interference) and remains anunsolved problem in the domain of machine learning to this day. Whileforgetting in the context of feedforward networks has been examined extensivelyover the decades, far less has been done in the context of alternativearchitectures such as the venerable self-organizing map (SOM), an unsupervisedneural model that is often used in tasks such as clustering and dimensionalityreduction. Although the competition among its internal neurons might carry thepotential to improve memory retention, we observe that a fixed-sized SOMtrained on task incremental data, i.e., it receives data points related tospecific classes at certain temporal increments, experiences significantforgetting. In this study, we propose the continual SOM (c-SOM), a model thatis capable of reducing its own forgetting when processing information.

Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay