The encoding of uncertainty in the Drosophila compass system

摘要 战略行为往往考虑到不确定性。例如，如果我们看到两个相互冲突的 对于信息片段，我们不太重视更不确定的信息来源--即 导致总体准确性较低的信息。值得注意的是，即使是昆虫也表现得好像它们在战略上利用 他们自己的不确定性。重要的是，不确定性的神经关联基本上是未知的。在这 在合作项目中，我们将使用建模和神经成像来识别不确定性的神经关联。我们 将重点放在果蝇大脑中的“罗盘”上。罗盘的内源性神经元(EPG神经元)形成一个 航向地形图。在任何给定的时刻，EPG中的神经活动都会有一个“突起” 像指南针一样随着苍蝇转动而旋转的种群。凹凸的位置受内部的影响 自动提示、外部视觉提示和外部风向提示。在以前的理论工作中，EPG 系综被建模为环形吸引子网络。一般来说，环形吸引子并不代表不确定性。 在他们正在编码的变量中。大多数表征COMPASS神经元活动的实验都是 或者在极端确定(例如，明亮的视觉提示)的条件下执行，或者在极端不确定(例如， 完全黑暗)。因此，目前尚不清楚该系统在适度不确定性下的表现，以及如果， 在这样的条件下，它仍然可以用标准的环形吸引子网络来很好地描述。理想情况下，指南针 网络将不仅代表苍蝇的估计飞行方向，而且还代表与 这一估计，以便行为策略可以相应地调整。在这个项目中，我们将调查(1) 不确定性是如何表现的，以及(2)不确定性是如何影响空间学习的。我们将使用算法的组合 与虚拟现实环境相结合的建模、网络建模和活体成像实验。

Summary Strategic behaviors often take account of uncertainty. For example, if we are presented with two conflicting pieces of information, we give less weight to the more uncertain source of information – i.e., the source of information that leads to lower accuracy overall. Notably, even insects behave as if they make strategic use of their own uncertainty. Importantly, the neural correlates of uncertainty are essentially unknown. In this collaborative project, we will use modeling and neural imaging to identify the neural correlates of uncertainty. We will focus on the “compass” in the Drosophila brain. The intrinsic neurons of the compass (EPG neurons) form a topographic map of heading direction. At any given moment, there is a “bump” of neural activity in the EPG population which rotates like a compass needle as the fly turns. The position of the bump is influenced by internal self-motion cues, external visual cues, and external wind direction cues. In previous theoretical work, the EPG ensemble has been modeled as a ring attractor network. In general, ring attractors do not represent uncertainty in the variable they are encoding. Most experiments characterizing compass neuron activity have been performed either under conditions of extreme certainty (e.g., a bright visual cue), or extreme uncertainty (e.g., complete darkness). Therefore, it remains unclear how the system behaves under moderate uncertainty, and if, under such conditions, it can still be well-described by standard ring attractor networks. Ideally, the compass network would represent not only the fly's estimated heading direction, but also the uncertainty associated with that estimate, so that behavioral strategies could be adjusted accordingly. In this project, we will investigate (1) how uncertainty is represented, and (2) how it affects spatial learning. We will use a combination of algorithmic modeling, network modeling, and in vivo imaging experiments combined with virtual reality environments.