Gagliardi Celia M, Normandin Marc E, Keinath Alexandra T, Julian Joshua B, Lopez Matthew R, Ramos-Alvarez Manuel-Miguel, Epstein Russell A, Muzzio Isabel A
Department of Neuroscience, Development, and Regenerative Biology, University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX 78249, USA.
Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52245, USA.
Res Sq. 2023 Mar 31:rs.3.rs-2724785. doi: 10.21203/rs.3.rs-2724785/v1.
Reorientation, the process of regaining one's bearings after becoming lost, requires identification of a spatial context (context recognition) and recovery of heading direction within that context (heading retrieval). We previously showed that these processes rely on the use of features and geometry, respectively. Here, we examine reorientation behavior in a task that creates contextual ambiguity over a long timescale to demonstrate that mice learn to combine both featural and geometric cues to recover heading with experience. At the neural level, most CA1 neurons persistently align to geometry, and this alignment predicts heading behavior. However, a small subset of cells shows feature-sensitive place field remapping, which serves to predict context. Efficient heading retrieval and context recognition require integration of featural and geometric information in the active network through rate changes. These data illustrate how context recognition and heading retrieval are coded in CA1 and how these processes change with experience.
重新定向,即在迷路后重新找到方向的过程,需要识别空间背景(背景识别)并在该背景中恢复前进方向(航向检索)。我们之前表明,这些过程分别依赖于特征和几何形状的使用。在这里,我们在一个长时间尺度上产生背景模糊性的任务中研究重新定向行为,以证明小鼠学会结合特征和几何线索,随着经验的积累来恢复前进方向。在神经层面,大多数CA1神经元持续与几何形状对齐,这种对齐可预测前进行为。然而,一小部分细胞表现出对特征敏感的位置场重映射,这有助于预测背景。高效的航向检索和背景识别需要通过速率变化在活跃网络中整合特征和几何信息。这些数据说明了背景识别和航向检索在CA1中是如何编码的,以及这些过程如何随经验而变化。