  In this article, we propose a novel form of unsupervised learning, continual
competitive memory (CCM), as well as a computational framework to unify related
neural models that operate under the principles of competition. The resulting
neural system is shown to offer an effective approach for combating
catastrophic forgetting in online continual classification problems. We
demonstrate that the proposed CCM system not only outperforms other competitive
learning neural models but also yields performance that is competitive with
several modern, state-of-the-art lifelong learning approaches on benchmarks
such as Split MNIST and Split NotMNIST. CCM yields a promising path forward for
acquiring representations that are robust to interference from data streams,
especially when the task is unknown to the model and must be inferred without
external guidance.
