Google DeepMind, Mountain View, CA 94043, USA.
Sci Adv. 2024 Aug 2;10(31):eadm8470. doi: 10.1126/sciadv.adm8470. Epub 2024 Jul 31.
Fascinating phenomena such as landmark vector cells and splitter cells are frequently discovered in the hippocampus. Without a unifying principle, each experiment seemingly uncovers new anomalies or coding types. Here, we provide a unifying principle that the mental representation of space is an emergent property of latent higher-order sequence learning. Treating space as a sequence resolves numerous phenomena and suggests that the place field mapping methodology that interprets sequential neuronal responses in Euclidean terms might itself be a source of anomalies. Our model, clone-structured causal graph (CSCG), employs higher-order graph scaffolding to learn latent representations by mapping aliased egocentric sensory inputs to unique contexts. Learning to compress sequential and episodic experiences using CSCGs yields allocentric cognitive maps that are suitable for planning, introspection, consolidation, and abstraction. By explicating the role of Euclidean place field mapping and demonstrating how latent sequential representations unify myriad observed phenomena, our work positions the hippocampus in a sequence-centric paradigm, challenging the prevailing space-centric view.
在海马体中经常会发现一些引人入胜的现象,如地标矢量细胞和分裂细胞。由于缺乏统一的原理,每个实验似乎都揭示了新的异常或编码类型。在这里,我们提供了一个统一的原理,即空间的心理表示是潜在高阶序列学习的涌现属性。将空间视为一个序列可以解决许多现象,并表明以欧几里得术语解释序列神经元反应的位置场映射方法本身可能是异常的来源。我们的模型,克隆结构因果图(CSCG),通过将混淆的自我中心感觉输入映射到独特的上下文中,使用高阶图支架来学习潜在的表示。使用 CSCG 学习压缩序列和情景体验会产生适合规划、内省、巩固和抽象的非中心认知地图。通过阐明欧几里得位置场映射的作用,并展示潜在的序列表示如何统一众多观察到的现象,我们的工作将海马体置于序列中心范式中,挑战了占主导地位的空间中心观点。