Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford, OX1 3TH, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
Max Planck Institute for Empirical Aesthetics, Frankfurt Am Main, 60322, Germany; Department of Biomedical Sciences, City University of Hong Kong, Hong Kong.
Prog Neurobiol. 2020 Sep;192:101821. doi: 10.1016/j.pneurobio.2020.101821. Epub 2020 May 21.
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,单一的海马体编码是情景记忆和预测加工的基础,但在预测编码框架内,伴随这两种现象的海马-新皮层相互作用是不同的和相反的。也就是说,在情景回忆期间,海马体被认为对新皮层施加兴奋性影响,以在皮质回路中恢复活动模式。这与关于预测加工的实证和理论工作形成对比,在预测加工中,下行预测通过皮质抑制来“消除”上行输入的预测误差。在这篇假说文章中,我们试图化解这个以前被忽视的辩证法。我们考虑海马体如何通过抑制新皮层的预测误差或增加其增益来分别促进预测和记忆。我们提出,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制增益(或精度)。在这个框架内,记忆回忆被视为源自虚构的预测误差,为优化世界生成模型提供训练信号,而无需感官数据。