Humans excel at continually acquiring, consolidating, and retaining
information from an ever-changing environment, whereas artificial neural
networks (ANNs) exhibit catastrophic forgetting. There are considerable
differences in the complexity of synapses, the processing of information, and
the learning mechanisms in biological neural networks and their artificial
counterparts, which may explain the mismatch in performance. We consider a
biologically plausible framework that constitutes separate populations of
exclusively excitatory and inhibitory neurons that adhere to Dale's principle,
and the excitatory pyramidal neurons are augmented with dendritic-like
structures for context-dependent processing of stimuli. We then conduct a
comprehensive study on the role and interactions of different mechanisms
inspired by the brain, including sparse non-overlapping representations,
Hebbian learning, synaptic consolidation, and replay of past activations that
accompanied the learning event. Our study suggests that the employing of
multiple complementary mechanisms in a biologically plausible architecture,
similar to the brain, may be effective in enabling continual learning in ANNs.

通过生物学上的神经元网络组合中的多种互补机制，可以在人工神经网络中实现连续学习。