ETIS - UMR 8051, Université Paris-Seine, Université de Cergy-Pontoise, ENSEA, CNRS, Cergy-Pontoise 95302, France
ETIS - UMR 8051, Université Paris-Seine, Université de Cergy-Pontoise, ENSEA, CNRS, Cergy-Pontoise 95302, France.
J Exp Biol. 2019 Feb 6;222(Pt Suppl 1):jeb186932. doi: 10.1242/jeb.186932.
Place recognition is a complex process involving idiothetic and allothetic information. In mammals, evidence suggests that visual information stemming from the temporal and parietal cortical areas ('what' and 'where' information) is merged at the level of the entorhinal cortex (EC) to build a compact code of a place. Local views extracted from specific feature points can provide information important for view cells (in primates) and place cells (in rodents) even when the environment changes dramatically. Robotics experiments using conjunctive cells merging 'what' and 'where' information related to different local views show their important role for obtaining place cells with strong generalization capabilities. This convergence of information may also explain the formation of grid cells in the medial EC if we suppose that: (1) path integration information is computed outside the EC, (2) this information is compressed at the level of the EC owing to projection (which follows a modulo principle) of cortical activities associated with discretized vector fields representing angles and/or path integration, and (3) conjunctive cells merge the projections of different modalities to build grid cell activities. Applying modulo projection to visual information allows an interesting compression of information and could explain more recent results on grid cells related to visual exploration. In conclusion, the EC could be dedicated to the build-up of a robust yet compact code of cortical activity whereas the hippocampus proper recognizes these complex codes and learns to predict the transition from one state to another.
位置识别是一个涉及内感受和外感受信息的复杂过程。在哺乳动物中,有证据表明,来自颞叶和顶叶皮质区域的视觉信息(“什么”和“哪里”的信息)在内嗅皮层(EC)水平上融合,以构建一个紧凑的位置代码。从特定特征点提取的局部视图可以提供重要信息,即使环境发生巨大变化,这些信息也对视图细胞(在灵长类动物中)和位置细胞(在啮齿类动物中)很重要。使用融合“什么”和“哪里”信息的联合细胞的机器人实验表明,它们对于获得具有强泛化能力的位置细胞具有重要作用。如果我们假设:(1)路径整合信息在 EC 之外计算;(2)由于与代表角度和/或路径整合的离散矢量场相关的皮质活动的投影(遵循模块原则),这种信息在 EC 水平上被压缩;(3)联合细胞合并不同模态的投影以构建网格细胞活动,那么这种信息的融合也可以解释内侧 EC 中网格细胞的形成。对视觉信息应用模投影可以实现有趣的信息压缩,并可以解释与视觉探索相关的网格细胞的最新结果。总之,EC 可以专门用于构建稳健但紧凑的皮质活动代码,而海马体本身则识别这些复杂的代码并学会预测从一种状态到另一种状态的转变。