  Neural networks are susceptible to catastrophic forgetting. They fail to
preserve previously acquired knowledge when adapting to new tasks. Inspired by
human associative memory system, we propose a brain-like approach that imitates
the associative learning process to achieve continual learning. We design a
heuristics mechanism to potentiatively stimulate the model, which guides the
model to recall the historical episodes based on the current circumstance and
obtained association experience. Besides, a distillation measure is added to
depressively alter the efficacy of synaptic transmission, which dampens the
feature reconstruction learning for new task. The framework is mediated by
potentiation and depression stimulation that play opposing roles in directing
synaptic and behavioral plasticity. It requires no access to the original data
and is more similar to human cognitive process. Experiments demonstrate the
effectiveness of our method in alleviating catastrophic forgetting on
image-to-image translation tasks.
