  Catastrophic forgetting/interference is a critical problem for lifelong
learning machines, which impedes the agents from maintaining their previously
learned knowledge while learning new tasks. Neural networks, in particular,
suffer plenty from the catastrophic forgetting phenomenon. Recently there has
been several efforts towards overcoming catastrophic forgetting in neural
networks. Here, we propose a biologically inspired method toward overcoming
catastrophic forgetting. Specifically, we define an attention-based selective
plasticity of synapses based on the cholinergic neuromodulatory system in the
brain. We define synaptic importance parameters in addition to synaptic weights
and then use Hebbian learning in parallel with backpropagation algorithm to
learn synaptic importances in an online and seamless manner. We test our
proposed method on benchmark tasks including the Permuted MNIST and the Split
MNIST problems and show competitive performance compared to the
state-of-the-art methods.
