C0PEP0D was aimed at deciphering how planktonic copepods exploit hydrodynamic and chemical sensing to detect and track targets in turbulent flows.
Copepods are millimetric crustaceans that play a crucial role in marine ecosystems. They live in all seas and oceans and are thought to be the most abundant multicellular organisms on the planet. Yet, copepods are blind. To detect prey, predators, and mates, copepods use highly developed hydrodynamic and chemical sensing. How are they able to distinguish a meaningful signal in oceanic turbulence? Copepods, being one of the greatest success stories of marine evolution, likely evolved smart algorithms to process this sensing information. But today, these algorithms are poorly understood.
The objective of C0PEP0D was to address three questions:
Q1: Mating. How do male copepods follow the pheromone trail left by females?
Q2: Finding. How do copepods use hydrodynamic signals to “see”?
Q3: Feeding. What are the best feeding strategies in turbulent flow?
We hypothesized that reinforcement learning can help reverse-engineer the algorithms used by copepods. To test this hypothesis, we built a virtual environment where copepods are trained: virtual copepods sense flow velocity and chemical concentration, and this sensing information is processed by a neural network trained by reinforcement learning. This theoretical and numerical approach is complemented by experiments on real copepods to measure how copepods react in turbulent flow.
With this project, we have discovered how hydrodynamic and chemical signalling help plankton to perceive their environment. This research opens new avenues in marine ecology, fluid mechanics, and data processing in robotics.
