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.

翻译：人类擅长从不断变化的环境中持续获取、整合和保留信息，而人工神经网络则表现出灾难性遗忘。生物神经网络与其人工对应物在突触复杂性、信息处理机制和学习方式上存在显著差异，这或许可以解释两者在性能上的差距。我们构建了一个符合戴尔原则的生物合理框架，该框架包含独立的兴奋性神经元群和抑制性神经元群，其中兴奋性锥体神经元配备了树突状结构，用于根据上下文刺激进行处理。随后，我们系统研究了多种脑启发机制（包括稀疏非重叠表征、赫布学习、突触巩固以及伴随学习事件产生的过去激活回放）的作用及其交互影响。研究表明，采用类似大脑中多种互补机制协同运作的生物合理架构，可能有效促进人工神经网络实现持续学习。