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The encoding of uncertainty in the Drosophila compass system

果蝇罗盘系统中不确定性的编码

基本信息

批准号：
10298651
负责人：
Jan Drugowitsch
金额：
$ 76.17万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-15 至 2024-07-31
项目状态：
已结题

项目摘要

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.

总结战略行为往往考虑到不确定性。例如，如果我们面对两个相互冲突的信息片段，我们给更不确定的信息来源的权重较小-即，的来源导致整体准确度较低的信息。值得注意的是，即使是昆虫，自己的不确定性。重要的是，不确定性的神经相关性本质上是未知的。在这合作项目，我们将使用建模和神经成像来识别不确定性的神经相关性。我们将聚焦于果蝇大脑中的“指南针”。罗盘的内在神经元（EPG神经元）形成一个掘进方向地形图在任何给定的时刻，EPG中都有一个神经活动的“碰撞”，种群就像罗盘针一样随着苍蝇的转动而转动。凸块的位置受内部自我运动线索、外部视觉线索和外部风向线索。在以前的理论工作中，EPG 合奏已被建模为一个环吸引网络。一般来说，环吸引子并不代表不确定性在他们编码的变量中。大多数表征罗盘神经元活动的实验都是或者在极端确定性的条件下执行（例如，明亮的视觉提示），或极端的不确定性（例如，完全黑暗）。因此，目前还不清楚系统在中等不确定性下的行为，如果，在这种条件下，它仍然可以很好地描述标准环吸引子网络。理想情况下，指南针网络将不仅代表苍蝇的估计航向，而且还代表与之相关的不确定性。这样行为策略就可以相应地调整。在这个项目中，我们将研究（1）不确定性是如何表现的，以及（2）它如何影响空间学习。我们将使用一种算法组合，建模、网络建模和结合虚拟现实环境的体内成像实验。