Functions of awake hippocampal replay

我们的目标是研究海马体中的神经元群体如何支持导航学习和决策- 制作。在“清醒重放”期间，海马神经元以反映空间信息的加速序列活跃。 轨迹这些轨迹是高度非局部的，从某种意义上说，它们激活了远距离空间的表征。 从当前位置到整个环境。因此，重放是一种独特的有吸引力的机制， 至少在目前的神经科学理解中， 在本地，例如发现奖励，或者发现阻塞的路线，并且广播 信息通过大脑对周围环境的表征传递出去。这是一个特定的假设 我们的目标是在这个提案中进行测试。首先，我们想了解可能导致奖励的电路机制 影响海马体的再生我们之前已经证明，奖励的增加会导致 重放发生的速率，特别是反向重放。现在我们打算测试这背后的电路 直接，通过抑制腹侧被盖区多巴胺能神经元的活动，因为这些 已知神经元用信号发送重要的与奖励相关的量，该量被称为奖励预测误差。模型 提出，这应该调节重放，我们的初步数据支持这一假设。其次，它仍然 不知道重播如何处理环境结构的变化，例如路径的阻塞。 我们将测试环境微小变化对重播的影响。创新之处在于， 设计成对导航有或大或小的影响，这取决于它有多容易绕过。所有的 动物经历的变化需要被记住，因此，如果重放主要是记忆， 合并机制，那么就不应该区分这两种情况。然而，只有全球 重大的变化需要通过环境广播导航信息。我们 初步数据显示，全球重要的变化导致与重播相关的事件增加， 其他人则没有。第三，重播如何支持更复杂环境中的规划仍然是未知的， 比通常研究的小竞技场或赛道更真实。理论研究表明， 多个空间尺度可以支持在更复杂的环境中的导航。更腹侧的部分 海马体在越来越大尺度上代表空间，但尚未研究背外侧的重放。 我们的初步数据复制了中间海马的位置场大小增加。而且我们 第一次在那里演示重播。我们希望确定是否以及如何沿着沿着 海马体的纵向轴支持在更复杂的任务中在多个空间尺度上的规划。 总之，我们的建议将提供新的见解重放功能，通过导航学习的透镜。

We aim to study how populations of neurons in the hippocampus support navigational learning and decision- making. During "awake replay", hippocampal neurons are active in speeded-up sequences that reflect spatial trajectories. These trajectories are highly non-local, in the sense that they activate representations of space far from the current location and throughout an environment. Thus, replay is a uniquely attractive mechanism, at least within current neuroscience understanding, for taking information about something that is happening locally, such as the discovery of a reward, or the discovery of a blocked route, and broadcasting that information out through the brain's representation of the rest of the environment. This is the specific hypothesis we aim to test in this proposal. First, we want to understand the circuit mechanisms that could lead to reward having an effect on hippocampal replay. We previously showed that reward increases lead to increases in the rate at which replays occur, and specifically for reverse replay. Now we propose to test the circuitry behind this directly, by suppressing the activity of dopaminergic neurons in the ventral tegmental area, since these neurons are known to signal an important reward-related quantity called reward prediction error. Models have proposed that this should regulate replay and our preliminary data support this hypothesis. Second, it remains unknown how replay processes changes to the structure of the environment, such as the blocking of a path. We will test the effect on replay of small changes to the environment. The innovation is that each change is designed to have either a small or a big effect on navigation, depending on how easy it is to circumvent. All the changes experienced by the animal will need to be remembered, so that if replay is mainly a memory consolidation mechanism, then it should not distinguish the two circumstances. However, only the globally significant changes require the broadcasting of navigational information out through the environment. Our preliminary data show that globally important changes cause an increase in replay-associated events while others do not. Third, it remains unknown how replay supports planning in more complex environments, that are more realistic than the small arenas or tracks usually studied. Theoretical studies suggest that planning at multiple spatial scales can support navigation in more complex environments. More ventral parts of the hippocampus represent space at increasingly large scales, but replay outside of dorsal has not been studied. Our preliminary data replicate increased place field size in intermediate hippocampus. Moreover, we demonstrate replay there for the first time. We expect to establish whether and how replay along the longitudinal axis of the hippocampus supports planning at multiple spatial scales in the more complex task. Altogether, our proposal will provide new insight into replay function through the lens of navigational learning.