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In this article, we propose a novel form of unsupervised learning, continualcompetitive memory (CCM), as well as a computational framework to unify relatedneural models that operate under the principles of competition. The resultingneural system is shown to offer an effective approach for combatingcatastrophic forgetting in online continual classification problems. Wedemonstrate that the proposed CCM system not only outperforms other competitivelearning neural models but also yields performance that is competitive withseveral modern, state-of-the-art lifelong learning approaches on benchmarkssuch as Split MNIST and Split NotMNIST. CCM yields a promising path forward foracquiring representations that are robust to interference from data streams,especially when the task is unknown to the model and must be inferred withoutexternal guidance.

Continual Competitive Memory: A Neural System for Online Task-Free Lifelong Learning