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C0PEP0D SIGNED

Life and death of a virtual copepod in turbulence

Total Cost €

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EC-Contrib. €

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Partnership

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Project "C0PEP0D" data sheet

The following table provides information about the project.

Coordinator
ECOLE CENTRALE DE MARSEILLE EGIM 

Organization address
address: RUE FREDERIC JOLIOT CURIE 38 TECHNOPOLE CHATEAU GOMBERT
city: MARSEILLE CEDEX 13
postcode: 13383
website: http://www.ec-marseille.fr/

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Total cost 2˙215˙794 €
 EC max contribution 2˙215˙794 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-ADG
 Funding Scheme ERC-ADG
 Starting year 2019
 Duration (year-month-day) from 2019-09-01   to  2024-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    ECOLE CENTRALE DE MARSEILLE EGIM FR (MARSEILLE CEDEX 13) coordinator 2˙215˙794.00

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 Project objective

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.

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The information about "C0PEP0D" are provided by the European Opendata Portal: CORDIS opendata.

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