Prioritizing replay when future goals are unknown.
Prioritizing replay when future goals are unknown.
复制标题
当未来目标未知时优先考虑重播。
DOI：
10.1101/2024.02.29.582822
复制
发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Daw,NathanielD
中科院分区：
文献类型：
--
作者：
Sagiv,Yotam;Akam,Thomas;Witten,IlanaB;Daw,NathanielD
虽然海马位置细胞重放非局部轨迹，这些事件的计算功能仍然存在争议。在一个著名的强化学习账户中正式提出的一个假设认为，重播计划通往当前目标的路线。然而，最近令人困惑的数据似乎与这一观点相矛盾，显示重播的目的地落后于当前的目标。这些结果可能支持另一种假设，即重播更新路线信息以构建“认知地图”。然而，还没有类似的理论来形式化这种观点，目前还不清楚如何表示这样的地图或重放在计算它扮演什么角色，我们通过引入重放的理论，学习到候选目标的路线图，在奖励可用或当它的位置可能会改变，以解决这些差距。我们的工作扩展了规划帐户，以捕获一个通用的地图构建功能，用于重放，将其与数据相协调，并揭示了看似不同的假设之间的意想不到的关系。
Although hippocampal place cells replay nonlocal trajectories, the computational function of these events remains controversial. One hypothesis, formalized in a prominent reinforcement learning account, holds that replay plans routes to current goals. However, recent puzzling data appear to contradict this perspective by showing that replayed destinations lag current goals. These results may support an alternative hypothesis that replay updates route information to build a “cognitive map.” Yet no similar theory exists to formalize this view, and it is unclear how such a map is represented or what role replay plays in computing it. We address these gaps by introducing a theory of replay that learns a map of routes to candidate goals, before reward is available or when its location may change. Our work extends the planning account to capture a general map-building function for replay, reconciling it with data, and revealing an unexpected relationship between the seemingly distinct hypotheses.