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.

概括战略行为通常考虑到不确定性。例如，如果我们有两个冲突信息片段，我们给更多不确定的信息来源提供的权重较少 - 即总体上导致准确性降低的信息。值得注意的是，即使昆虫的行为也好像是战略性地使用他们自己的不确定性。重要的是，不确定性的神经相关性本质上是未知的。在这个协作项目，我们将使用建模和神经成像来确定不确定性的神经相关性。将专注于果蝇大脑中的“指南针”。指南针的固有神经元（EPG神经元）形成了标题方向的地形图。在任何给定的时刻，EPG中都有“颠簸” 当苍蝇转弯时，人口像指南针一样旋转。颠簸的位置受内部的影响自运动提示，外部视觉提示和外部风向提示。在以前的理论工作中，EPG 合奏已被建模为环网网络。通常，环吸引子不代表不确定性在变量中，它们正在编码。大多数表征罗盘神经元活动的实验是在极端确定的条件下（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，目前尚不清楚系统的行为如何在适度的不确定性下，以及是否尚不清楚在这种情况下，标准环吸引子网络仍然可以很好地描述它。理想情况下，指南针网络不仅代表苍蝇的估计标题方向，还代表与之相关的不确定性该估算值，以便可以相应地调整行为策略。在这个项目中，我们将调查（1）不确定性的表示，（2）它如何影响空间学习。我们将使用算法的组合建模，网络建模和体内成像实验与虚拟现实环境相结合。