Descrizione del progetto
Svelare il processo sensoriale dei copepodi
I copepodi sono piccoli crostacei molto diffusi negli habitat marini, nonostante le correnti e la turbolenza oceaniche. Le loro antenne sono sensibili alle informazioni meccaniche e chimiche che consentono loro di localizzare cibo, predatori e partner con cui accoppiarsi. Grazie all’impiego di applicazioni che si avvalgono dell’apprendimento per rinforzo, il progetto C0PEP0D, finanziato dall’UE, intende determinare il modo in cui i copepodi elaborano le informazioni sensoriali di tipo meccanico e chimico in un ambiente marino turbolento. Questo obiettivo sarà raggiunto mediante la creazione di un modello di apprendimento virtuale con segnalazione idrodinamica, la sperimentazione con esemplari vivi in ambienti simulati e lo sviluppo di nuovi algoritmi di apprendimento per rinforzo che imitano l’evoluzione e l’apprendimento dei copepodi. In definitiva, il progetto offrirà informazioni sull’evoluzione delle specie marine e potrebbe risultare di ispirazione per le innovazioni future nel campo dell’ingegneria biomimetica.
Obiettivo
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.
Campo scientifico
- 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
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-ADG - Advanced GrantIstituzione ospitante
13383 Marseille Cedex 13
Francia