  Continual learning is the ability to acquire new knowledge without forgetting
the previously learned one, assuming no further access to past training data.
Neural network approximators trained with gradient descent are known to fail in
this setting as they must learn from a stream of data-points sampled from a
stationary distribution to converge. In this work, we replace the standard
neuron by a meta-learned neuron model whom inference and update rules are
optimized to minimize catastrophic interference. Our approach can memorize
dataset-length sequences of training samples, and its learning capabilities
generalize to any domain. Unlike previous continual learning methods, our
method does not make any assumption about how tasks are constructed, delivered
and how they relate to each other: it simply absorbs and retains training
samples one by one, whether the stream of input data is time-correlated or not.

Machine Learning;Artificial Intelligence;Neural and Evolutionary Computing