The hippocampus is crucial for episodic memory, but it is also involved in online prediction. Evidence suggests that a unitary hippocampal code underlies both episodic memory and predictive processing, yet within a predictive coding framework the hippocampal-neocortical interactions that accompany these two phenomena are distinct and opposing. Namely, during episodic recall, the hippocampus is thought to exert an excitatory influence on the neocortex, to reinstate activity patterns across cortical circuits. This contrasts with empirical and theoretical work on predictive processing, where descending predictions suppress prediction errors to ‘explain away’ ascending inputs via cortical inhibition. In this hypothesis piece, we attempt to dissolve this previously overlooked dialectic. We consider how the hippocampus may facilitate both prediction and memory, respectively, by inhibiting neocortical prediction errors or increasing their gain. We propose that these distinct processing modes depend upon the neuromodulatory gain (or precision) ascribed to prediction error units. Within this framework, memory recall is cast as arising from fictive prediction errors that furnish training signals to optimise generative models of the world, in the absence of sensory data.

海马体对于情景记忆至关重要，但它也参与在线预测。有证据表明，单一的海马代码是情景记忆和预测处理的基础，但在预测编码框架内，伴随这两种现象的海马-新皮质相互作用是不同的和对立的。也就是说，在情景回忆期间，海马体被认为对新皮层产生兴奋性影响，以恢复整个皮层回路的活动模式。这与预测处理的经验和理论工作形成对比，其中下降预测通过皮质抑制抑制预测错误以“解释”上升输入。在这篇假设文章中，我们试图化解这种先前被忽视的辩证法。我们考虑海马体如何分别通过抑制新皮质预测错误或增加其增益来促进预测和记忆。我们建议这些不同的处理模式取决于归因于预测误差单元的神经调节增益（或精度）。在这个框架内，记忆召回被认为是由虚构的预测错误引起的，这些错误提供训练信号以优化世界的生成模型，在没有感官数据的情况下。