预测编码模型显示新皮层可存特定记忆，但仅限少量样本。
Semantic and episodic memories in a predictive coding model of the neocortex
- 用预测编码模拟新皮层学习机制
- 小样本训练下能回忆具体事件，但泛化差
- 支持新皮层需依赖海马体进行海量记忆编码
互补学习系统理论认为智能体需要两种记忆系统：语义记忆在新皮层以密集重叠表征存储结构化知识，而情景记忆在海马体以稀疏分离表征快速学习个体经验。近期，预测编码这一生物合理的新皮层神经网络模型在自关联记忆任务中表现出类似海马体的能力，挑战了该二元性。本文提出一种预测编码的新皮层模型，探索其情景记忆能力。结果表明，该模型仅在少量样本训练下可回忆具体实例，但出现过拟合且泛化能力差；当训练样本增多时，回忆能力消失。这表明新皮层虽可通过密集重叠表征逐步编码个别实例，但数量受限，因此仍需海马体的稀疏分离表征来支持大量情景记忆。
原文摘要 · Abstract (English)
Complementary Learning Systems theory holds that intelligent agents need two learning systems. Semantic memory is encoded in the neocortex with dense, overlapping representations and acquires structured knowledge. Episodic memory is encoded in the hippocampus with sparse, pattern-separated representations and quickly learns the specifics of individual experiences. Recently, this duality between semantic and episodic memories has been challenged by predictive coding, a biologically plausible neural network model of the neocortex which was shown to have hippocampus-like abilities on auto-associative memory tasks. These results raise the question of the episodic capabilities of the neocortex and their relation to semantic memory. In this paper, we present such a predictive coding model of the neocortex and explore its episodic capabilities. We show that this kind of model can indeed recall the specifics of individual examples but only if it is trained on a small number of examples. The model is overfitted to these exemples and does not generalize well, suggesting that episodic memory can arise from semantic learning. Indeed, a model trained with many more examples loses its recall capabilities. This work suggests that individual examples can be encoded gradually in the neocortex using dense, overlapping representations but only in a limited number, motivating the need for sparse, pattern-separated representations as found in the hippocampus.
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