Hippocampal representations that underlie spatial memory undergo continuous refinement following formation¹. Here, to track the spatial tuning of neurons dynamically during offline states, we used a new Bayesian learning approach based on the spike-triggered average decoded position in ensemble recordings from freely moving rats. Measuring these tunings, we found spatial representations within hippocampal sharp-wave ripples that were stable for hours during sleep and were strongly aligned with place fields initially observed during maze exploration. These representations were explained by a combination of factors that included preconfigured structure before maze exposure and representations that emerged during θ-oscillations and awake sharp-wave ripples while on the maze, revealing the contribution of these events in forming ensembles. Strikingly, the ripple representations during sleep predicted the future place fields of neurons during re-exposure to the maze, even when those fields deviated from previous place preferences. By contrast, we observed tunings with poor alignment to maze place fields during sleep and rest before maze exposure and in the later stages of sleep. In sum, the new decoding approach allowed us to infer and characterize the stability and retuning of place fields during offline periods, revealing the rapid emergence of representations following new exploration and the role of sleep in the representational dynamics of the hippocampus.

构成空间记忆基础的海马表征在形成¹之后经历不断的细化。在这里，为了在离线状态下动态跟踪神经元的空间调谐，我们使用了一种新的贝叶斯学习方法，该方法基于自由移动大鼠的整体记录中尖峰触发的平均解码位置。通过测量这些调谐，我们发现海马尖波波纹内的空间表征在睡眠期间稳定数小时，并且与迷宫探索期间最初观察到的位置场强烈一致。这些表征是通过多种因素来解释的，其中包括迷宫暴露前的预先配置的结构以及迷宫中 θ 振荡和清醒尖波波纹期间出现的表征，揭示了这些事件在形成集合中的贡献。引人注目的是，睡眠期间的波纹表征预测了神经元在重新暴露于迷宫时的未来位置场，即使这些场偏离了之前的位置偏好。相比之下，我们在睡眠期间和迷宫暴露前的休息期间以及睡眠后期观察到与迷宫位置场对齐不良的调整。总之，新的解码方法使我们能够推断和表征离线期间位置场的稳定性和重新调整，揭示了新探索后表征的快速出现以及睡眠在海马体表征动态中的作用。