Topic: flying insects

Tracking a turbulent plume to locate its source is a complex control problem requiring robust multi-sensory integration in the face of intermittent odors, changing wind direction, and variable plume shape. This task is routinely performed by flying insects, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behavior have been studied in detail in many experimental studies. Here, we take a complementary in silico approach, using artificial agents trained with reinforcement learning to develop an integrated understanding of the behaviors and neural comput

The vast majority of natural animal behavior involves far more than passive perception and reflexive responses. Indeed, animals actively move themselves in the world and systematically collect sensory observations over time; such active sensing allows them to infer environmental states that are not directly measurable, which ultimately guide ethologically important behaviors. One clear example of such a behavior is odor plume localization in flying insects. By changing their speed and turning, insects produce variations in their multisensory inputs that, once integrated over time, can resolve