Description du projet
Élucider le processus sensoriel des copépodes
Les minuscules crustacés appelés «copépodes» foisonnent dans les habitats marins malgré les courants et les turbulences océaniques. Leurs antennes sont sensibles aux informations mécaniques et chimiques et leur permettent de localiser la nourriture, les prédateurs et des partenaires. En ayant recours à des applications d’apprentissage par renforcement, le projet C0PEP0D, financé par l’UE, entend déterminer la façon dont les copépodes traitent les informations mécano et chimiosensorielles dans un environnement marin turbulent. Pour ce faire, le projet concevra un modèle d’apprentissage virtuel intégrant une signalisation hydrodynamique, mènera des expériences avec des spécimens vivants dans des environnements simulés et développera de nouveaux algorithmes d’apprentissage par renforcement qui reproduisent l’évolution et l’apprentissage des copépodes. À terme, le projet fournira des indications sur l’évolution des espèces marines et sera source de futures innovations dans le domaine de l’ingénierie biomimétique.
Objectif
Life is tough for planktonic copepods, constantly washed by turbulent flows. Yet, these millimetric crustaceans dominate the oceans in numbers. What have made them so successful? Copepod antennae are covered with hydrodynamic and chemical sensing hairs that allow copepods to detect preys, predators and mates, although they are blind. How do copepods process this sensing information? How do they extract a meaningful signal from turbulence noise? Today, we do not know.
C0PEP0D hypothesises that reinforcement learning tools can decipher how copepod process hydrodynamic and chemical sensing. Copepods face a problem similar to speech recognition or object detection, two common applications of reinforcement learning. However, copepods only have 1000 neurons, much less than in most artificial neural networks. To approach the simple brain of copepods, we will use Darwinian evolution together with reinforcement learning, with the goal of finding minimal neural networks able to learn.
If we are to build a learning virtual copepod, challenging problems are ahead: we need fast methods to simulate turbulence and animal-flow interactions, new models of hydrodynamic signalling at finite Reynolds number, innovative reinforcement learning algorithms that embrace evolution and experiments with real copepods in turbulence. With these theoretical, numerical and experimental tools, we will 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?
C0PEP0D will decipher how copepods process sensing information, but not only that. Because evolution is explicitly considered, it will offer a new perspective on marine ecology and evolution that could inspire artificial sensors. The evolutionary approach of reinforcement learning also offers a promising tool to tackle complex problems in biology and engineering.
Champ scientifique
- natural sciencesbiological sciencesecology
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programme(s)
Thème(s)
Régime de financement
ERC-ADG - Advanced GrantInstitution d’accueil
13383 Marseille Cedex 13
France