Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : GIANTSYN (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Institute of Science and Technology Austria — Pays : Autriche — Rôle : PI
Acronyme de l'entité : ISTA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2677500.0 €

A fundamental question in neuroscience is how the biophysical properties of synapses shape higher network computations. The hippocampal mossy fiber synapse, formed between axons of dentate gyrus granule cells and dendrites of CA3 pyramidal neurons, is the ideal synapse to address this question. This synapse is accessible to presynaptic recording, due to its large size, allowing a rigorous investigation of the biophysical mechanisms of transmission and plasticity. Furthermore, this synapse is placed in the center of a memory circuit, and several hypotheses about its network function have been generated. However, even basic properties of this key communication element remain enigmatic. The ambitious goal of the current proposal, GIANTSYN, is to understand the hippocampal mossy fiber synapse at all levels of complexity. At the subcellular level, we want to elucidate the biophysical mechanisms of transmission and synaptic plasticity in the same depth as previously achieved at peripheral and brainstem synapses, classical synaptic models. At the network level, we want to unravel the connectivity rules and the in vivo network function of this synapse, particularly its role in learning and memory. To reach these objectives, we will combine functional and structural approaches. For the analysis of synaptic transmission and plasticity, we will combine direct preand postsynaptic patch-clamp recording and high-pressure freezing electron microscopy. For the analysis of connectivity and network function, we will use transsynaptic labeling and in vivo electrophysiology. Based on the proposed interdisciplinary research, the hippocampal mossy fiber synapse could become the first synapse in the history of neuroscience in which we reach complete insight into both synaptic biophysics and network function. In the long run, the results may open new perspectives for the diagnosis and treatment of brain diseases in which mossy fiber transmission, plasticity, or connectivity are impaired.

Mots-clés : hippocampus,mossy fiber,synaptic transmission,synaptic plasticity,presynaptic terminal,exocytosis,pattern completion,neuronal networks
Projet ERC : QuantumNet (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Technical University of Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

A future quantum network will consist of quantum processors that are connected by quantum channels, just like conventional computers are wired up to form the Internet. In contrast to classical devices, however, the entanglement and non-local correlations available in a quantum-controlled system facilitate novel fundamental tests of quantum theory and numerous applications in distributed quantum information processing, quantum communication, and precision measurement. While pioneering experiments have demonstrated the entanglement of two quantum nodes separated by up to 1.3 km, accessing the full potential of quantum networks requires scaling of these prototypes to more nodes and larger distances. To this end, a new technology that overcomes the bottlenecks of existing physical systems has to be developed. Here, I propose to harness the exceptional properties of individual Erbium ions embedded in Yttrium crystals to increase the size of quantum networks via implementation of the seminal quantum repeater proposal, which is one of the most intensively pursued research topics in current quantum science. The key proposed steps to this goal are (I) implementation of a quantum spin-photon interface at a telecommunication wavelength, (II) multiplexing of many quantum bits in one device via frequency-selective addressing, and (III) implementation of remote entanglement swapping and purification to increase the range of quantum-secure communication beyond its current fundamental limit. These goals have been out of reach for any experimental platform until now. They become feasible by combining the powerful concepts developed in cavity quantum electrodynamics using cold atoms with the exceptional coherence of spins in specific host crystals. Successful implementation will demonstrate the feasibility of quantum networks over global distances, a milestone advancement for quantum communication and quantum science in general.

Mots-clés : quantum networks,quantum repeaters,spin based quantum information processing,cavity quantum electrodynamics,remote entanglement,quantum communication
Projet ERC : VIS-A-VIS (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : Humboldt-Universität zu Berlin — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

A key component of visual perception is our ability to move: In a flick of the eye, we see the time on the watch, and we quickly turn around if we hear our name in the crowd. Whereas other movements aim to change the state of the world, visual actions shift our eyes, heads, and bodies to align the retina with currently relevant parts of the world. Although they vitally extend the scope of high-acuity vision, their immediate sensory consequences have challenged scientists for centuries: How do we not experience the brisk motion of the entire scene on the retina every time the eyes move (perceptual omission)? How does the brain keep track of objects’ changing retinal locations across consecutive glances (object continuity). And how do we routinely attribute retinal motion to our own movements rather than to motion in the world (sense of agency). To explain these phenomena, research and theories across disciplines have focused on how the brain—using its knowledge about ongoing movement plans—predicts and compensates for undesirable side effects of visual actions. I pursue a radically new perspective based on a key insight: Visual actions follow distinct kinematic rules, and as every visual action translates directly into a movement of the world on the retinal image, these rules also directly govern the sensory input. Their sensory consequences can thus be distinguished from motion in the world based on the rules they follow. In embracing this idea, I challenge the long-standing idea that visual actions are a nuisance to sensory processing and propose instead that they support core functions in active vision. In an interdisciplinary team, we will leverage innovative technology, state-of-the-art psychophysical tools and robust experimental protocols to find out if and how the active visual system learns and exploits the lawful relation between visual actions and their sensory consequences, to establish perceptual omission, object continuity, and the sense of agency.

Mots-clés : active vision,sensorimotor learning,sense of agency
Projet ERC : MultiCellSysBio (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Life Science
Panel : Integrative Biology: from Genes and Genomes to Systems
Entité participante : Delft University of Technology — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

A key question in biology is how cells at different locations communicate through signalling molecules so that cells at the right place and time turn on the right genes. Such coordination is vital for many processes including the development of all embryos. A common set of strategies that cells from distinct organisms use to coordinate their gene expression has long been elusive. Finding it requires quantifying how two key factors, the spatial locations of cells and the genetic circuits that control the cells’ secretion of signalling molecules, each affects cells' gene expressions. This has been challenging because their effects are often intertwined in complex ways. To overcome this challenge, I will assemble budding yeasts into multicellular structures component-by-component in a bottom-up manner, from building genetic circuits to arranging cells in space. I will build genetic circuits whose motifs commonly occur in natural systems. The yeasts will use the genetic circuits to control secretion and sensing of three distinct signalling molecules for communication. Using adhesive proteins and light-inducible genes, I will assemble multiple yeast strains, each with a unique genetic circuit, into a single two- and three-dimensional multicellular structure. The structures will mimic various sizes, shapes, and arrangements of cells found in nature. I will then switch on the circuits in these cells to initiate communication between cells. Different amounts of signalling molecules will cause the cells to make different amounts of fluorescent proteins. By measuring the fluorescence of cells at different locations over time and then correlating them, I will infer the degree of cell-cell coordination. I will build mathematical models to guide the experiments. By finding which combinations of genetic circuits and spatial arrangements of cells enable cell-cell coordination of gene expressions, I will reveal design principles of multicellular systems that have been elusive.

Mots-clés : design principles of genetic circuits,quantitative biology,biophysics,network motifs,design principles of multicellular systems,collective behaviours,cell circuits
Projet ERC : COFBMIX (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Hebrew University of Jerusalem — Pays : Israël — Rôle : PI
Acronyme de l'entité : HUJI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

A key question in neuroscience is how information is processed by sensory systems to guide behavior. Most of our knowledge about sensory processing is based on presentation of simple isolated stimuli and recording corresponding neural activity in relevant brain areas. Yet sensory stimuli in real life are never isolated and typically not simple. How the brain processes complex stimuli, simultaneously arising from multiple objects is unknown. Our daily experience (as well as well-controlled experiments) shows that only parts of a complex sensory scene can be perceived - we cannot listen to more than one speaker in a party. Importantly, one can easily choose what is important and should be processed and what can be ignored as background. These observations lead to the prevalent hypothesis that feedback projections from ‘higher’ brain areas to more peripheral sensory areas are involved in processing of complex stimuli. However experimental analysis of signals conveyed by feedback projections in behaving animals is scarce. The nature of these signals and how they relate to behavior is unknown. Here I propose a cutting edge approach to directly record feedback signals in the olfactory system of behaving mice. We will use chronically implanted electrodes to record the modulation of olfactory bulb (OB) principal neurons by task related context. Additionally, we will record from piriform cortical (PC) neurons that project back to the OB. These will be tagged with channelrhodopsin-2 and identified by light sensitivity. Finally, we will express the spectrally distinct Ca++ indicators GCaMP6 and RCaMP2 in PC neurons and in olfactory sensory neurons, respectively, and use 2-photon microscopy to analyze the spatio-temporal relationship between feedforward and feedback inputs in the OB. This comprehensive approach will provide an explanation of how feedforward and feedback inputs are integrated to process complex stimuli.

Mots-clés : olfaction,figure background segregation,attention
Projet ERC : mlQuDyn (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : University of Augsburg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1058271.16 €

A key scope of quantum many-body theory is the identification of universal behavior in quantum matter, where macroscopic properties become independent of microscopic details. In recent years the quest for phases with novel universal properties has been revolutionized by forcing systems out of equilibrium, which has opened up a universe of unexplored phenomena and new dynamical paradigms. These developments not only hold the promise to theoretically uncover unrecognized universal dynamical behavior, but are also driven by the enormous advances in quantum simulators such as ultra-cold atoms, which have nowadays achieved unique capabilities in generating and probing such nonequilibrium quantum states. Still, their theoretical description is facing severe challenges. It is the aim of this proposal to take the theoretical understanding and predictive power of quantum many-body theory to a new level by an crossdisciplinary approach at the interface between quantum dynamics and machine learning. The central element of this approach is to encode time-evolved quantum states into artificial neural networks, which have been remarkably successful in storing and recognizing complex structures in computer science. In order to reach the main goal we have identified three main challenges which form the core of the program: (i) to design efficient artificial network structures based on fundamental principles of quantum many-body systems such as locality and causality; (ii) to utilize concepts of many-body theory and statistical physics to understand the physical properties of artificial neural networks; (iii) to explore fundamental but yet inaccessible dynamical quantum phenomena and universal behavior in quantum dynamics. The successfully conducted research program will lift the description and understanding of quantum many-body dynamics to a new level, impacting significantly both quantum theory as well as future experiments.

Mots-clés : quantum many body dynamics,nonequilibrium physics,quantum simulators,machine learning,artificial neural networks
Projet ERC : mlQuDyn (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 433217.84 €

A key scope of quantum many-body theory is the identification of universal behavior in quantum matter, where macroscopic properties become independent of microscopic details. In recent years the quest for phases with novel universal properties has been revolutionized by forcing systems out of equilibrium, which has opened up a universe of unexplored phenomena and new dynamical paradigms. These developments not only hold the promise to theoretically uncover unrecognized universal dynamical behavior, but are also driven by the enormous advances in quantum simulators such as ultra-cold atoms, which have nowadays achieved unique capabilities in generating and probing such nonequilibrium quantum states. Still, their theoretical description is facing severe challenges. It is the aim of this proposal to take the theoretical understanding and predictive power of quantum many-body theory to a new level by an crossdisciplinary approach at the interface between quantum dynamics and machine learning. The central element of this approach is to encode time-evolved quantum states into artificial neural networks, which have been remarkably successful in storing and recognizing complex structures in computer science. In order to reach the main goal we have identified three main challenges which form the core of the program: (i) to design efficient artificial network structures based on fundamental principles of quantum many-body systems such as locality and causality; (ii) to utilize concepts of many-body theory and statistical physics to understand the physical properties of artificial neural networks; (iii) to explore fundamental but yet inaccessible dynamical quantum phenomena and universal behavior in quantum dynamics. The successfully conducted research program will lift the description and understanding of quantum many-body dynamics to a new level, impacting significantly both quantum theory as well as future experiments.

Mots-clés : quantum many body dynamics,nonequilibrium physics,quantum simulators,machine learning,artificial neural networks
Projet ERC : SYNAPLAST MR (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 1505000.0 €

A large number of psychiatric disorders (major and bipolar depression (MDD / BD), schizophrenia, obsessive-compulsive disorder (OCD), addiction, anxiety, attention deficit hyperactivity syndrome (ADHS), posttraumatic stress disorder (PTSD), autism) lack objective criteria for primary diagnosis, early differential diagnosis with regard to subtypes in treatment response and disease progression or effective therapy monitoring. Hence, the search for relevant biomarkers is of high importance. This proposal suggests the development of novel methodology for highly spatially and temporally resolved imaging of disease effects on neurotransmission, membrane processes related to synaptic plasticity and brain energy metabolism in psychiatric disorders and the acute and chronic impact of related pharmacological treatment in the human brain. To that, the advantages of a unique 9.4 T whole body human magnetic resonance imaging (MRI) system for 1H, 31P and 13C magnetic resonance spectroscopic imaging shall be exploited. Highly innovative enabling MRI technology including parallel transmission, very high order B0 shimming, a real time field stabilization and motion correction approach along with the principles of advanced encoding and non-Fourier image reconstruction shall ensure high data quality. Next to obtaining 20 novel image contrasts based on steady state metabolite concentrations, the ultimate goal of the proposed research is to enable functional spectroscopic imaging in the entire human brain in order to investigate adaptation of neurotransmission and brain metabolism to environmental stimuli as well as the impact of acute pharmacological intervention. Finally, the spatially and temporally resolved metabolic imaging technology shall be used for investigation of patients with major depressive disorder to reveal novel biomarkers relevant for diagnostics and patient stratification.

Mots-clés : magnetic resonance spectroscopy,ultra high field,9 4 t,14 1 t,positron emission tomography
Projet ERC : SILCI (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : University of East Anglia — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : UEA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1198136.0 €

A low carbon transition requires disruptive innovations to challenge prevailing technologies and practices. Many disruptive low carbon innovations have been adopted, but in small numbers. Examples include car sharing networks, car-free communities, and net zero energy buildings. To mitigate climate change, these and other innovations must diffuse or spread into the mass market. In the absence of strong policy incentives, social communication from adopters to non-adopters is the means by which innovations spread. But we do not understand how the different mechanisms of social influence work for disruptive low carbon innovations, and whether they can be harnessed to accelerate change. In this project, I aim to address this critical knowledge gap by asking: what role can social influence play in driving the diffusion of disruptive innovations as part of a low carbon transition? This project will open up a new scientific field on disruptive low carbon innovations using rigorous scientific methods applied to a wide range of novel data. Objective 1. To understand the attributes of disruptive low carbon innovations valued by actual and potential users. -> By using secondary data, structured elicitation exercises, and a large-scale cross-national survey, I will evaluate innovation attributes and their potential acceptability to mass market adopters. Objective 2. To assess the strength and mechanisms of social influence in the diffusion of disruptive low carbon innovations. -> By analysing social network structures, online activity, and the spatial distribution of early adopters, I will quantify the relative strength of social influence in diffusion processes. Objective 3. To test strategies and actions for using social influence to accelerate a low carbon transition. -> By modelling social influence effects in global climate mitigation models, I will test the effectiveness of long-term strategies and actionable policies for accelerating low carbon transitions.

Mots-clés : energy transitions,disruptive innovations,social influence
Projet ERC : TWILIGHT (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : UK Research and Innovation — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : UKRI
Type d'entité : Recherche
Financement ERC de l'entité : 1499997.95 €

A major accomplishment of modern society is the extension of human life expectancy. However, this creates a new challenge for medical science, to facilitate healthy ageing. With age, the function of the immune system declines, rendering older people more susceptible to infections and less able to benefit from vaccination. Indeed, improving vaccine efficacy is key to reducing infection-related morbidity in older people. To date, the complexity of the ageing process has hindered attempts to fulfil this ambition, and thus innovative approaches are required to better understand the underlying biology. Vaccination creates protective immunity by inducing the germinal centre (GC) response, an intricate process that generates memory B cells and long-lived antibody-secreting plasma cells. However, the GC response declines with age. Strikingly, it is not B cells that are responsible for the age-dependent decline in the GC response, but the CD4+ T cells and the microenvironment of older individuals. The cellular and molecular mechanisms responsible, however, remain unknown. In the GC there are two subsets of specialised CD4+ T cells, T follicular helper (Tfh) and T follicular regulatory (Tfr) cells, which act in opposition to promote and suppress the response, respectively. I hypothesise that aberrant formation and/or function of Tfh and Tfr cells contribute to impaired GC responses during ageing, and that these cells could be targeted to improve vaccine efficacy. Furthermore, the most prominent age-dependent change in secondary lymphoid tissues is the accumulation of senescent cells, which can modify immune function and tissue structure. I hypothesise that accumulation of senescent cells alters this microenvironment, impairing the response to vaccination. I will test these hypotheses using new mouse models and innovative approaches to human research, in the expectation that the knowledge obtained will promote healthy ageing and uncover novel aspects of GC biology.

Mots-clés : germinal centre,t follicular helper cells,t follicular regulatory cells,vaccination