生物合理性神经网络研究：脑启发机制在持续学习中的作用与交互
A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning
AI总结：
本文研究生物合理性神经网络中稀疏表征、赫布学习、突触巩固与激活回放等脑启发机制的协同作用，证明多机制互补架构可有效缓解人工神经网络的灾难性遗忘，实现持续学习。
摘要：
人类擅长在不断变化的环境中持续获取、巩固和保留信息，而人工神经网络（ANNs）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、信息处理和学习机制上存在显著差异，这可能解释了二者在性能上的不匹配。我们考虑一个生物合理性框架，该框架由分别遵循戴尔原则的纯兴奋性神经元群和纯抑制性神经元群构成，并且兴奋性锥体神经元被增强了类树突结构，用于对刺激进行上下文相关处理。随后，我们对受大脑启发的不同机制的作用和交互进行了全面研究，这些机制包括稀疏非重叠表征、赫布学习、突触巩固，以及伴随学习事件发生的过去激活回放。我们的研究表明，在类似于大脑的生物合理性架构中采用多种互补机制，可能有效实现人工神经网络中的持续学习。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
