Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : NEURO-PATTERNS (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Italian Institute of Technology — Pays : Italie — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1637500.0 €

When we see an object, hear a sound or smell an odor, precise spatial and temporal patterns of electrical activity are generated within neuronal networks located in specialized brain areas. This electrical representation of the external stimulus mediates perception and sensory experience. However, this process is highly variable, and repetition of the very same sensory experience results in distinct network activity patterns. What does this variability mean for perception? Do distinct activity patterns carry different information about the stimulus? Or rather, does the brain code the same information coming from the outside world in multiple and equivalent ways? Answering these questions and determining how patterns of activity in neuronal populations are used for behavior has not been possible because of the inability to change the activity of neurons with single cell precision over large networks in an intact mammalian brain. In this ambitious proposal we will take a multidisciplinary approach to causally address these questions and decipher the computational principles of brain networks. To achieve this goal we will develop innovative optical technologies for manipulating and monitoring brain circuits with single cell resolution in the intact mouse brain. We will combine these new techniques with novel genetic manipulations and psychophysical behavioral methods that allow precise quantification of animals’ perceptual performance. Using this unique set of tools, we will unravel how the spatial (across neurons) and temporal (across time) aspects of neuronal electrical activity patterns encode information that guides behavior. In achieving our goals we will produce a new technology for stimulating and monitoring neurons in the brains of behaving animals with single-cell specificity that can be adapted to explore cellular dynamics in highly scattering biological media.

Mots-clés : neurophotonics,wavefront engineering,circuit neuroscience,systems neuroscience,cortical networks
Projet ERC : SHAPE (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : University of Giessen — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : JLU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1950725.0 €

Whenever we look at an object, we can effortlessly infer many of its physical and functional properties from its shape and our previous experience with other objects. We can judge whether it is flexible or fragile; stable or likely to tumble; what might have happened to it in the past (e.g. a crushed can or bitten apple); and can even imagine how other members of the same object class might look. These high-level inferences are evidence of sophisticated visual and cognitive processes that derive behaviorally significant information about objects from their 3D shape—a process we call 'Shape Understanding'. Despite its obvious importance to everyday life, practically nothing is known about how the brain uses shape to infer the properties, origin or behavior of objects. The goal of this project is to develop a radically new interdisciplinary field to uncover how the brain 'makes sense of shape'. We suggest that when we view novel objects, the brain uses perceptual organization mechanisms to infer a primitive 'generative model' describing the processes that gave the shape its key characteristics. We seek to identify the psychological and computational processes that enable the brain to parse and interpret shape this way. To achieve this, we unite ideas and methods from surface perception, morphogenesis, geometry, computer graphics, naïve physics and concept learning. We will simulate physical processes that create and modify 3D forms (e.g. biological growth, fluid flow, ductile fracture). We will use the resulting shapes as stimuli in experiments in which observers must identify key shape features, recognize transformations that have been applied to shapes, or predict the likely shape of other exemplars from the same object class. We will then model subjects' performance by geometrically analyzing shapes to find cues to the underlying shape-forming processes. These cues will be combined to infer generative models using inference techniques from machine learning.

Mots-clés : perception,vision,surface perception,material perception,perceptual organization,gestalt,grouping,categorization,concepts,computer graphics,procedural geometry,shape similarity metrics
Projet ERC : EUROPIUM (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Technical University of Darmstadt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1446875.0 €

Where in the universe are heavy elements synthesized? How are these elements produced? These are two exciting and interdisciplinary questions in nuclear astrophysics today and will be investigated in my ERC project EUROPIUM. The favored astrophysical sites are neutrino-driven winds following core-collapse supernovae and neutron star mergers, where extreme conditions enable the rapid neutron capture process (r-process). We will perform long-time multidimensional simulations of these two scenarios and combine them with nucleosynthesis calculations. In neutron star mergers, the radioactive decay of neutron-rich nuclei triggers an electromagnetic signal known as kilonova. This was potentially observed in 2013 after a short gamma ray burst, associated with a neutron star merger. We will simulate the neutrino- and viscous-driven ejecta from the disk that forms after the merger around the central compact object. In addition, we will investigate supernova neutrino-driven winds that produce lighter heavy elements from strontium to silver. We will explore the impact of rotation, improved microphysics, and magnetic fields on the wind evolution and nucleosynthesis. Because the synthesis of lighter heavy elements elements occurs closer to stability, the nuclear physics uncertainties will be reduced by experiments in the near future. This will uniquely allow us to combine observations and nucleosynthesis calculations to constrain the astrophysical conditions and gain new insights into core-collapse supernovae. In nuclear physics, a new era for extreme neutron-rich isotopes is starting with new experimental facilities. Based on our simulations, we will study the impact of the nuclear physics input (nuclear masses, beta decays, neutron captures, and fission) going beyond the state-of-the-art by providing r-process abundances with uncertainties. Comparing our results with forefront observations of the oldest stars will in turn provide new insights about the origin of heavy elements.
Projet ERC : Smart-TURB (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : University of Rome Tor Vergata — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2248875.0 €

Where is it difficult to control, predict and model a flowing system? to search and navigate inside it? to be prepared against extreme events? to tame them? It is in turbulent flows. Turbulence is ubiquitous and unsolved from the point of view of out-of-equilibrium fundamental physics, uncontrollable from the engineering aspects, and a deadlock for brute-force numerical and experimental investigations. Indeed, progress by using conventional methods has been slow. In this project, I propose to explore new avenues crossing the boundaries between Theoretical Engineering and Applied Physics using algorithms from Artificial Intelligence (AI) to study and control turbulence in an innovative way using smart Lagrangian objects in a vast array of flows. I am committed to: (i) develop original applications of AI algorithms to track and harness moving coherent structures and/or statistical turbulent fluctuations, (ii) optimise flow navigation of buoyant objects and active surface drifter, (iii) invent collective search protocols to locate emissions from fixed or floating sources, (iv) minimise turbulent dispersion of a swarm of autonomous underwater explorer and (v) perform new in-silico experiments for data-assimilation, to predict extreme-events, or to control turbulent fluctuations by novel Lagrangian injection/adsorption mechanisms. The unifying fil-rouge of my project is to gain a Deep Understanding of turbulence by performing cutting-edge Lagrangian numerical studies. The project is both methodology oriented, with the grand challenge of developing fully unconventional applications of (Deep) Reinforcement Learning for fluid dynamics, and problem driven, delivering a series of specific optimal control strategies for important realistic flow set-ups and applications to the geophysical fields. With my experience and the impact of my contributions in the discipline, I am confident that I offer the highest chances to carry out this ambitious project with success.

Mots-clés : turbulence  computational fluid dynamics  lagrangian fluid dynamics  extreme events  intermittency  data assimilation  nudging  reinforcement learning  optimal control  field taxis
Projet ERC : CAVEHEART (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499429.0 €

Whereas the human heart cannot regenerate cardiac muscle after myocardial infarction, certain fish efficiently repair their hearts. Astyanax mexicanus, a close relative of the zebrafish, is a single fish species comprising cave-dwelling and surface river populations. Remarkably, while surface fish regenerate their heart after injury, cavefish cannot and form a permanent fibrotic scar, similar to the human heart. Using transcriptomics analysis and immunohistochemistry, we have identified key differences in the scarring and inflammatory response between the surface and cavefish heart after injury. These differences include extracellular matrix (ECM) proteins, growth factors and macrophage populations present in one, but not the other population, suggesting properties unique to the surface fish scar that promote heart regeneration. The objective of the proposed project is to characterise and utilise these findings to identify therapeutic targets to heal the human heart after myocardial infarction. First, we will analyse the identified differences in scarring and immune response between the fish in detail, before testing the role of the most interesting proteins and macrophage populations during regeneration using CRISPR mutagenesis and clodronate liposomes. Next, we will link the key scarring and inflammatory differences directly to both the genome and the ability for heart regeneration using new and prior Quantitative Trait Loci analyses. This will allow to find the most fundamental molecular mechanisms directing the wound healing process towards regeneration versus scarring. Together with an in vitro and in vivo small molecule screen directed specifically at influencing scarring towards a more ‘fish-like’ regenerative phenotype in the cavefish and mouse heart after injury, this will provide targets for therapeutic strategies to maximise the endogenous regenerative potential of the mammalian heart, with the aim to find a cure for myocardial infarction.

Mots-clés : fish heart regeneration,astyanax mexicanus,fibrosis,inflammation
Projet ERC : GROWCEAN (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : Spanish National Research Council — Pays : Espagne — Rôle : PI
Acronyme de l'entité : CSIC
Type d'entité : Recherche
Financement ERC de l'entité : 1500000.0 €

Which are the most abundant microbial eukaryotic species in the world’s sunlit oceans, and what roles do they play in global marine ecology? At present, the answers to both of these questions remain largely unknown. Recently, the Tara Oceans expedition conducted a global ribosomal barcode survey of the surface oceans, revealing that, of roughly 500,000 total Operational Taxonomic Units (OTUs), only 89, which were present in every station sampled by Tara, represent half of the total global OTU abundance. Yet, of these 89 OTUs, 81 do not match any known species. The goal of this project is to characterize the biology, interspecies interactions, and ecosystem relevance of these 81 highly abundant, ubiquitous unknown microbial eukaryotes. We propose three research objectives. First, using a novel isolation approach, we will establish robust laboratory cultures and sequence their transcriptomes to produce gene catalogs. Second, we will apply time-lapse light and fluorescence microscopy to understand their life history and behavior and to build hypotheses about their individual and community metabolic potential. Third, we will characterize their ecosystem relevance by leveraging Tara Oceans metatranscriptomes to explore how their genetic toolkit is expressed across varying oceanic conditions on a global scale, and by performing laboratory manipulations to test hypotheses on a local scale. Overall, we will provide the first glimpse of the morphological, life history, behavioral and transcriptional features of currently unknown globally abundant protists. Discoveries about their biology will have immediate implications for studies of the ecology and community structure of oceanic ecosystems. Finally, we anticipate that our efforts will lead to the establishment of ecologically relevant microbial eukaryotes as new model systems whose biology can be studied intensively in the laboratory.

Mots-clés : microbial eukaryotes,marine ecology,novel species isolation,interspecies interaction,metatranscriptomics,evolution
Projet ERC : CAPTUR3D (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Scuola Normale Superiore — Pays : Italie — Rôle : PI
Acronyme de l'entité : SNS
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1860750.0 €

Which physical principles govern life regulation at the level of subcellular, membrane-enclosed nanosystems, such as transport vesicles and organelles? How do they achieve controlled movements across the crowded intracellular world? Which is the structural and functional organization of their surface and their lumen? This is only a small subset of key open questions that the biophysical approach envisaged here will allow to answer directly within living matter, for the first time. Thus far, state-of-the-art optical microscopy tools for delivering quantitative information in living matter failed to subtract the natural 3D movement of subcellular nanosystems while preserving the spatial and temporal resolution required to probe their structure and function at the molecular level. CAPTUR3D will tackle this bottleneck. An excitation light-beam will be focused in a periodic orbit around the nanosystem of interest and used to localize its position with unprecedented spatial (~10 nm) and temporal (~1000 Hz frequency response) resolution. Such privileged observation point will push biophysical investigations to a new level. For the first time, state-of-the-art imaging technologies and analytical tools (e.g. fluorescence correlation spectroscopy), will be used to perform molecular investigations on a moving, nanoscopic reference system. The insulin secretory granule (ISG) is selected as a paradigmatic case study. Key open issues at the ISG level are selected, namely: (i) ISG-environment interactions and their role in directing ISG trafficking, (ii) ISG-membrane spatiotemporal organization, (iii) ISG-lumen structural and functional organization, (iv) ISG alterations in type-2 diabetes (T2D). These issues will be tackled directly within human-derived Langherans islets. CAPTUR3D is envisioned not only to foster our knowledge on T2D physiopathology but also to concomitantly drive an unprecedented revolution in the way we address living matter at the subcellular scale.
Projet ERC : UPFLOW (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : University of London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1855419.25 €

While downward mantle flow (subduction) is well constrained, a grand challenge in Earth sciences is to understand deep upward flow that cannot be explained by plate tectonics. The aim of UPFLOW is to develop an entirely new seismic imaging approach and to use it to constrain plume-like mantle upwellings at unprecendented resolution. Upward flow is critical for continental growth, for returning volatiles to the atmosphere and for producing Earth's largest melting events. These events coincide with major extinctions, supercontinent breakups and with changes in geodynamo behaviour. Seismology has come far by using travel-time and waveform (phase) information but has mostly ignored the amplitude information. Mantle plumes have just started to be robustly imaged, yet resolution is low and uncertainty quantification is lacking. I developed breakthrough seismic imaging methods based on big amplitude data, showing their strong resolution. With recent computing resources and explosion in datasets, it is now timely to use this unique expertise to lead a step change in observational seismology: rather than relying on pre-defined observables (e.g., travel-times), objective statistical techniques exploring the full richness of the data will be used to design observables that provide optimal constraints on Earth structure. I will lead a new off-shore experiment in the Azores-Madeira-Canary islands region, which is an under-studied natural laboratory comprising significant mantle upwellings that are poorly understood in general. The imaging technique to be developed will be applied to these new data and to existing data (Hawaii, Reunion) to obtain the sharpest ever images of mantle plumes along with their interdisciplinary interpretation. These results will have a major impact far beyond the study regions and across Earth Sciences (e.g., geology, geochemistry, geodynamics) since they will reveal ubiquitous, fundamental phenomena that control the global evolution of the planet.

Mots-clés : mantle plumes,seismic imaging
Projet ERC : ORISEM (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 2499416.0 €

While formal semantics has established itself as a rigorous field, its impact on the broader cognitive sciences has remained suboptimal. Besides the inherent complexity of formal theories, the reason might be that the data explained are narrowly linguistic. We propose to extend the breadth and explanatory depth of formal semantic theories by testing them against understudied objects. Within language, we study spoken language gestures as well as sign languages, especially in their iconic dimension. Beyond language, we investigate primate gestural and vocal communication, and semantic effects in music. Our goal is threefold. First, we investigate how the typology of semantic operations extends to these new objects. Second, we explore the cognitive underpinnings of these operations in two types of cases: when the extension of a rule to a new type of object provides new constraints on the form of this rule (e.g. the existence of a presupposition triggering mechanism in iconic constructions); and when an expression has cognitive uses beyond language (e.g. raised eyebrows, or increased loudness). Third, we explore possible evolutionary connections among operations found in speech, sign, human and non-human gestures, and music. We will (i) explore gestural counterparts of formal semantic operations found in sign language, (ii) investigate how the rich typology of inferences uncovered by semantics extends to iconic gestures, and (iii) conduct a comparative investigation of this typology for iconic vs. non-iconic inferences in sign language. We will (iv) extend the enterprise beyond humans by exploring how a particular operation, implicatures, extends in animal linguistics, and whether human gestural expressions have counterparts in apes; and we will (v) develop a generalization of iconic semantics that accounts for semantic effects in music. Empirical findings will help develop new theories of how specific inferences (e.g. presuppositions, focus) are triggered.

Mots-clés : formal semantics,pragmatics,formal semantics of sign languages,formal semantics of gestures
Projet ERC : IONOS (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Université de Lille — Pays : France — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

While our understanding of the brain have made huge progresses, we are still inefficient in interfacing biological systems with electronics, both in terms of energy and integration potential. Pushed by the need to use conventional computers for building complex systems dedicated to brain interface applications, we have mostly capitalized on technologies and architectures inherits from microelectronic that are intrinsically not adapted to interface living systems. The IONOS project will shift the brain interface paradigm by developing new technologies designed to interact intimately with biological cells and capitalizing heavily on bio-inspiration. To reach this goal, the IONOS project will explore how to sense, stimulate and compute biological signals from in-vitro neural cells’ assembly based on iono-electronic materials and devices. These emerging devices offer basics functionalities such as memory, ion-electron signal’s transduction, and amplification paving the way to a new field of device and circuit engineering that could efficiently reproduce key biological functions such as learning and spatio-temporal processing of information. This project will demonstrate how these concepts associated to the bio-inspired computing paradigm can unlock our fundamental limitations for communicating with living neural cells. Proof of concept will show how an artificial system can efficiently send, receive and compute information from a biological one, which constitutes the basic of communication.