Neural mechanisms to incorporate visual counterevidence in self movement estimation

In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can be spuriously triggered by visual motion from external movements. Here, we show that stationary patterns on the retina, which constitute evidence against observer rotation, suppress inappropriate stabilizing rotational behavior in the fruit fly Drosophila. In silico experiments show that artificial neural networks that are optimized to distinguish observer movement from object motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly’s local motion- and optic flow-detectors. Our results show how the fly brain incorporates negative evidence to improve heading stability, exemplifying how a compact brain exploits geometrical constraints of the visual world.

Methods
This dataset contains all experimental data necessary to create figures in Tanaka et al. (2023), as well as scripts to analyze them. The scripts are written in Matlab 2019b, and uses some functions from Statistics and Machine Learning Toolbox.