  Unlike the brain, artificial neural networks, including state-of-the-art deep
neural networks for computer vision, are subject to "catastrophic forgetting":
they rapidly forget the previous task when trained on a new one. Neuroscience
suggests that biological synapses avoid this issue through the process of
synaptic consolidation and metaplasticity: the plasticity itself changes upon
repeated synaptic events. In this work, we show that this concept of
metaplasticity can be transferred to a particular type of deep neural networks,
binarized neural networks, to reduce catastrophic forgetting.
