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.