Nicola Rigolli

Biological systems are surrounded by fluids and evolved spectacular adaptations to decode the sparse information brought by turbulence. My research project focuses on octopuses hunting in the ocean: they localize their prey using turbulent odor, water movement and pressure. I model octopuses and their environment using statistical fluid dynamics and decision theory. In my simulations a turbulent scalar (odor) evolves in water from a localized source (prey). Odor is an intermittent quantity that spreads from the source disgregating in fluctuating puffs. The shape of these intermittent puffs changes as they are deformed by the turbulent airflow far from the source. Detections occur within a conical volume which is the typical shape of the plume. I am currently developing algorithms to understand how can a octopus interpret this fluctuating signal to find its prey. Does it need a spatial or temporal memory for successful inference? I will show that simple inferences can be accomplished simply by averaging over the body of the octopus. However, to extract reliable information for more complex tasks, the dynamic features of this broken signal must be used.