Topic: heading direction

Efficient navigation requires animals to track their position, velocity and heading direction (HD). Some animals’ behavior suggests that they also track uncertainties about these navigational variables, and make strategic use of these uncertainties, in line with a Bayesian computation. Ring-attractor networks have been proposed to estimate and track these navigational variables, for instance in the HD system of the fruit fly Drosophila. However, such networks are not designed to incorporate a notion of uncertainty, and therefore seem unsuited to implement dynamic Bayesian inference. Here, we c

The regions of the insect brain devoted to spatial navigation are beautifully orderly, with a remarkably precise pattern of synaptic connections. Thus, we can learn much about the neural mechanisms of spatial navigation by targeting identifiable neurons in these networks for in vivo patch clamp recording and calcium imaging. Our lab has recently discovered that the "compass system" in the Drosophila brain is anchored to not only visual landmarks, but also the prevailing wind direction. Moreover, we found that the compass system can re-learn the relationship between these external sensory cues

In a cross wind, the direction a fly moves through the air may differ from its heading direction, the direction defined by its body axis. I will present a model based on experimental results that reveals how a heading direction “compass” signal is combined with optic flow to compute and represent the direction that a fly is traveling. This provides a general framework for understand how flies perform vector computations.

Understanding the neural basis of animal behavior is a central challenge in biology. In this work, we establish a connection between the spatio-temporal decision-making processes of animals and the ring attractor dynamics of neurons, which determine the organism's heading direction. We formulate a neural field model within the mean field approximation to provide a microscopic description of the spontaneous formation of bifurcations along their movement trajectories towards multiple targets, as observed in recent animal experiments. The direction of movement corresponds to the position of the a

Noisy and ambiguous environments require using the arising uncertainty strategically for efficient behavior. Recent behavioral evidence confirms such strategic use during spatial navigation. Despite this, leading neural models for navigation ignore such uncertainty. I will focus on two examples for revising these models. First, I will show that common ring attractor architectures are able to track uncertainty in heading direction only if they are tuned to feature slow dynamics. Second, I will show that approximate computations that only partially track uncertainty are sufficient to perform cl