Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : BrainPlay (Horizon 2020, appel 2018, Synergy grants)
Entité participante : Charité - Universitätsmedizin Berlin — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 4781250.0 €

Our goal is to uncover the learning algorithms that subserve biological intelligence and to discover how they are implemented in the brain. We take for granted that biological intelligence results from neural information processing, that neural information processing is based on the transmission of action potentials through synapses, and that learning is realized through synaptic plasticity. We are inspired by two key observations: Firstly, we know that biological learning unfolds in ways different from mainstream machine learning that relies on learning from large labeled datasets. Second, we discovered that the engagement of the brain during play can result in unexpected and profound cognitive benefits. This proposal describes an untravelled route to the learning algorithms of the brain that runs through the no-man’s-land between synaptic physiology, systems neuroscience, cognitive neuroscience, theoretical neuroscience and machine learning. Our approach focuses on the self-teaching abilities of the mammalian brain and covers and connects four major topics: (1) the objective functions that govern synaptic plasticity, (2) the teaching signals through which learning is steered, (3) behavioral mechanisms of self-teaching, in particular play behaviors, (4) the brain states that engage self-teaching behaviors, in particular the brain state of play. The BrainPlay grant will study self-teaching abilities from synapses to brains, from computational theory to action video games. As gaming has been shown to be highly beneficial for human brain function, we are intrigued by how little we know about what is going on in playing brains and how the brain state of play shapes learning. Engaging the latest theoretical and technological breakthroughs, BrainPlay will reach far beyond mainstream neuroscience and embrace and elucidate playfulness and self-teaching as important components of the brain's learning algorithms.

Mots-clés : play,supervised learning,unsupervised learning,synaptic plasticity
Projet ERC : pre-FAB (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: Biological Mechanisms, Structures and Functions
Entité participante : University of Manchester — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2494328.75 €

Our group has recently discovered a new type of cofactor: a prenylated-flavin that has azomethine ylide properties. This cofactor is an integral part of the widespread ubiD/ubiX system. The latter is implicated in the non-oxidative reversible decarboxylation of aromatic substrates, and plays a pivotal role in bacterial ubiquinone biosynthesis or microbial biodegradation of aromatic compounds. We established UbiX acts as a novel flavin prenyltransferase, linking a dimethylallyl moiety to the flavin N5 and C6 atoms. Formation of the holo-UbiD enzyme involves oxidative maturation of the new cofactor, creating the novel azomethine ylide moiety. The dipolarophile substrate binds directly above the azomethine ylide group, and our data strongly suggests 1,3-dipolar cycloaddition chemistry supports reversible decarboxylation in these enzymes. While 1,3-dipolar cycloaddition is commonly used in organic chemistry, this presents the first example of an enzymatic 1,3-dipolar cycloaddition reaction. Our model for UbiD catalysis hints at new routes in alkene hydrocarbon production or aryl (de)carboxylation. The current application builds ambitiously on these results and takes the project altogether to another level: we seek to investigate structure/function of relationships of the wider UbiD family, ultimately including the multi-subunit enzymes that couple ATP-hydrolysis to benzene or naphthalene carboxylation. Furthermore, we will explore and harness the unusual properties of the prenylated flavin, through targeted evolution of (monoxygenase) flavoenzymes to create artificial prFMN-dependent self-sufficient monoxygenases. Our approach seeks to harness both the UbiD and the artificial prFMN-dependent enzymes in novel green routes to commodity chemicals.

Mots-clés : enzymology,enzyme cofactors,enzyme evolution
Projet ERC : RememberEx (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Universidad Politécnica de Madrid — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UPM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Our memory system is optimised for remembering the exceptional over the mundane. We remember better those events that violate predictions generated by the prevailing context, particularly because of surprise or emotional impact. Understanding how we form and retrieve long-term memories for important or salient events is critical for combating the rapidly growing incidence of pathologies associated with memory dysfunction with huge socio-econonomic burden. Human lesion and non-invasive functional imaging data, motivated by findings from animal models, have identified subcortical structures that are critical for upregulating hippocampal function during salient event memory. However, mechanistic understanding of these processes in humans remains scarce, and requires better experimental approaches such as direct intracranial recordings from, and focal electrical stimulation of, these subcortical structures. This project will characterise human subcortico-cortical neuronal circuit dynamics associated with enhanced episodic memory for salient stimuli by studying direct recordings from human hippocampus, amygdala, nucleus accumbens, ventral midbrain and cortex. Within this framework, I will elucidate the electrophysiological mechanisms underlying amygdala-hippocampal-cortical coupling that lead to better memory for emotional stimuli, extend the hippocampal role in detecting unpredicted stimuli to define its role in orchestrating cortical dynamics in unpredictable contexts, and discover the neuronal response profile of the human mesolimbic dopamine system during salient stimulus encoding. The predicted results, based on my own preliminary data, will offer several conceptual breakthroughs, particularly regarding hippocampal function and the role of dopaminergic ventral midbrain in memory. The knowledge gained from this project is a fundamental requirement for designing therapeutic interventions for patients with memory deficits and other neuropsychiatric disorders.

Mots-clés : memory,emotion,salience,hippocampus,amygdala,nucleus accumbens,ventral tegmental area,human intracranial recordings,functional mri  fmri,magnetoencephalography  meg
Projet ERC : BODYinTRANSIT (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Carlos III University of Madrid — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998612.0 €

Our physical body is the interface between ourselves and the world around us. The way we perceive our body, its appearance, configuration and motor abilities shapes our behaviour, emotion and social functioning. New sensor-based and bodily sensory feedback devices, such as those brought by immersive virtual reality, allow creating Body Transformation Experiences (BTE). These perceptual illusions, like being in a child’s body, lead to experience changes in one’s own body. Beyond fun experiences, the new field of BTE engineering brings a significant leap for wellbeing and health applications, as well as for the embodiment of robotic devices and virtual avatars, and smart clothing. However, the neuroscience of core sensory-driven changes in Mental Body Representations (MBR) currently represents piecemeal experiments in very controlled settings that restrict body movement. This hinders the transfer of the basic principles discovered to real-world complex environments and practical problems. Through systematic and iterative research, BODYinTRANSIT will establish a theoretical ‘BTE design framework’ for individualized sensorial manipulation of MBR with long-lasting effects in everyday use contexts. The framework will stand on four scientific pillars to induce, measure, support, personalize and preserve body transformations: 1) neuroscience of multisensory body perception, 2) data modelling of the links between MBR, behaviour, emotion and social functioning, 3) wearable-based embodied multisensory interaction design, and 4) field studies in real-life and on-the-move contexts with physically inactive users, somatic practitioners and users with body image concerns. Tangible outcomes will include design principles, data formats, models, measures and paradigms enabling and guiding BTE engineering innovations. BODYinTRANSIT envisions personalized BTE technologies able to measure MBR and adapt bodily sensory feedback to modify it – individually, online and while on-the-move.

Mots-clés : multisensory body perception,body transformation experience,sound interactive technology,multimodal interface,emotion,motor behavior,embodiment,personalized self care wearables,inner fashion
Projet ERC : Real-PIM-System (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Technion – Israel Institute of Technology — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Our project aims to develop a new computer architecture that enables true in-memory processing based on a unit that can both store and process data using the same cells. This unit, called a memristive memory processing unit (mMPU), will substantially reduce the necessity to move data in computing systems, solving the two main bottlenecks exist in current computing systems, i.e., speed ('memory wall') and energy efficiency ('power wall'). Emerging memory technologies, namely memristive devices, are the enablers of the mMPU. While memristors are naturally used as memory, these novel devices can also perform logical operations using a technique we have invented called Memristor Aided Logic (MAGIC). This combination is the basis of mMPU. The goal of this research is to design a fully functional mMPU, and by that, to demonstrate a real computing system with significantly improved performance and energy efficiency. We have identified four main research tasks which must be completed to demonstrate a full system utilizing mMPU: mMPU design, system architecture and software, modeling and evaluation, and fabrication. Both memristive memory array and mMPU control will be designed and optimized for different technologies in the first objective. The second objective will deal with the different aspects of the system, including programming model, different mMPU modes of operation and their corresponding system implications, compiler and operating systems. For system evaluation, we will develop models and tools in the third objective in order to measure the performance, area and energy and to compare them to other state-of-the-art computing systems. Lastly, we will fabricate the different parts of the system to demonstrate the full system. Encouraged from our preliminary experimental results, we expect to achieve 10X improvement in performance, and 100X improvement in energy efficiency as compared to state-of-the-art von Neumann systems when working with appropriate workloads.

Mots-clés : memristor,von neumann,memory,logic
Projet ERC : ConflictedPrediction (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
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 : 1683958.75 €

Our sensory receptors are bombarded with noisy, continuous streams of information. From these streams, our brains must construct percepts that are (1) veridical representations of the world, and (2) informative – i.e., highlighting what we did not already know. Cognitive science has suggested that our brain meets these challenges by using probabilistic expectations to shape our perceptual experiences. However, there are currently two broad classes of theory concerning how expectations shape perception, that are both supported by large bodies of evidence and mutually incompatible: some theories propose that we upweight what we expect to generate veridical representations, whereas others propose that we downweight what we expect to privilege the most ‘newsworthy’ information. ConflictedPrediction will test a new theory addressing and solving this paradox for the first time, to determine how perception can be rendered both veridical and informative. I propose that probabilistic knowledge pre-emptively biases perception towards what is likely, to generate largely veridical experiences rapidly. However, if the input is particularly surprising, catecholamine release – acting to aid learning – reactively enhances perception of these inputs by modulating sensory gain. This perceptual enhancement will generate a clearer estimate of these highly unexpected events to guide model-updating. To test the theory, ConflictedPrediction will use temporally- and spatially- sensitive neural measures (EEG, MEG, 7T fMRI), in combination with computationally derived parameters of perception and unexpectedness. This interdisciplinary project therefore will unify understanding of perception and learning across typically isolated scientific domains. Its findings will chart a new research frontier for understanding how the brain surmounts key computational challenges, enabling us to survive and thrive in a challenging sensory world.

Mots-clés : perception,prediction,expectation
Projet ERC : ConflictedPrediction (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : University of London — Pays : Royaume-Uni — Rôle : Partner
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Our sensory receptors are bombarded with noisy, continuous streams of information. From these streams, our brains must construct percepts that are (1) veridical representations of the world, and (2) informative – i.e., highlighting what we did not already know. Cognitive science has suggested that our brain meets these challenges by using probabilistic expectations to shape our perceptual experiences. However, there are currently two broad classes of theory concerning how expectations shape perception, that are both supported by large bodies of evidence and mutually incompatible: some theories propose that we upweight what we expect to generate veridical representations, whereas others propose that we downweight what we expect to privilege the most ‘newsworthy’ information. ConflictedPrediction will test a new theory addressing and solving this paradox for the first time, to determine how perception can be rendered both veridical and informative. I propose that probabilistic knowledge pre-emptively biases perception towards what is likely, to generate largely veridical experiences rapidly. However, if the input is particularly surprising, catecholamine release – acting to aid learning – reactively enhances perception of these inputs by modulating sensory gain. This perceptual enhancement will generate a clearer estimate of these highly unexpected events to guide model-updating. To test the theory, ConflictedPrediction will use temporally- and spatially- sensitive neural measures (EEG, MEG, 7T fMRI), in combination with computationally derived parameters of perception and unexpectedness. This interdisciplinary project therefore will unify understanding of perception and learning across typically isolated scientific domains. Its findings will chart a new research frontier for understanding how the brain surmounts key computational challenges, enabling us to survive and thrive in a challenging sensory world.

Mots-clés : perception,prediction,expectation
Projet ERC : ConflictedPrediction (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : University of Birmingham — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 78580.0 €

Our sensory receptors are bombarded with noisy, continuous streams of information. From these streams, our brains must construct percepts that are (1) veridical representations of the world, and (2) informative – i.e., highlighting what we did not already know. Cognitive science has suggested that our brain meets these challenges by using probabilistic expectations to shape our perceptual experiences. However, there are currently two broad classes of theory concerning how expectations shape perception, that are both supported by large bodies of evidence and mutually incompatible: some theories propose that we upweight what we expect to generate veridical representations, whereas others propose that we downweight what we expect to privilege the most ‘newsworthy’ information. ConflictedPrediction will test a new theory addressing and solving this paradox for the first time, to determine how perception can be rendered both veridical and informative. I propose that probabilistic knowledge pre-emptively biases perception towards what is likely, to generate largely veridical experiences rapidly. However, if the input is particularly surprising, catecholamine release – acting to aid learning – reactively enhances perception of these inputs by modulating sensory gain. This perceptual enhancement will generate a clearer estimate of these highly unexpected events to guide model-updating. To test the theory, ConflictedPrediction will use temporally- and spatially- sensitive neural measures (EEG, MEG, 7T fMRI), in combination with computationally derived parameters of perception and unexpectedness. This interdisciplinary project therefore will unify understanding of perception and learning across typically isolated scientific domains. Its findings will chart a new research frontier for understanding how the brain surmounts key computational challenges, enabling us to survive and thrive in a challenging sensory world.

Mots-clés : perception,prediction,expectation
Projet ERC : Age Asymmetry (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Life Science
Panel : Cellular, Developmental and Regenerative Biology
Entité participante : University of Helsinki — Pays : Finlande — Rôle : PI
Acronyme de l'entité : UH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Our tissues are constantly renewed by stem cells. Over time, stem cells accumulate cellular damage that will compromise renewal and results in aging. As stem cells can divide asymmetrically, segregation of harmful factors to the differentiating daughter cell could be one possible mechanism for slowing damage accumulation in the stem cell. However, current evidence for such mechanisms comes mainly from analogous findings in yeast, and studies have concentrated only on few types of cellular damage. I hypothesize that the chronological age of a subcellular component is a proxy for all the damage it has sustained. In order to secure regeneration, mammalian stem cells may therefore specifically sort old cellular material asymmetrically. To study this, I have developed a novel strategy and tools to address the age-selective segregation of any protein in stem cell division. Using this approach, I have already discovered that stem-like cells of the human mammary epithelium indeed apportion chronologically old mitochondria asymmetrically in cell division, and enrich old mitochondria to the differentiating daughter cell. We will investigate the mechanisms underlying this novel phenomenon, and its relevance for mammalian aging. We will first identify how old and young mitochondria differ, and how stem cells recognize them to facilitate the asymmetric segregation. Next, we will analyze the extent of asymmetric age-selective segregation by targeting several other subcellular compartments in a stem cell division. Finally, we will determine whether the discovered age-selective segregation is a general property of stem cell in vivo, and it's functional relevance for maintenance of stem cells and tissue regeneration. Our discoveries may open new possibilities to target aging associated functional decline by induction of asymmetric age-selective organelle segregation.

Mots-clés : asymmetric cell division
Projet ERC : RADICOL (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Heidelberg University — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998873.0 €

Our tissues, in particular collagen as the most abundant protein in our body, are constantly exposed to mechanical loads, reaching multiples of the body weight. In artificial polymers, mechanical loads are known for a century to cause radical formation and chemical degradation processes. Mechanoradicals from bond ruptures, being highly reactive and oxidising, deteriorate the material, leading to stiffening and ageing. Ageing of organic tissue is a fundamental problem in health and disease, but a role of mechanoradicals has been a blind spot. Our simple but novel idea is to test the role of mechanoradicals for ageing of biomaterials. As a starting point, we have recently uncovered mechanoradicals in tensed tendon collagen. They readily react with water to form reactive oxygen species (ROS), key signalling molecules in a multitude of physiological processes including ageing. I hypothesise that mechanoradicals generate a feedback loop resulting in accelerated collagen ageing. Using a scale-bridging combined computational and experimental approach, I will dissect the full lifecycle of mechanoradicals in collagen, from bond scission and radical migration to ROS formation, to uncover new mechanisms of radical-mediated ageing. We will perform quantum chemical calculations and Molecular Dynamics (MD) simulations, including a new reactive Monte Carlo/MD scheme, to identify scissile bonds and subsequent radical reactions in atomistic collagen I fibril models. For validation, a combination of electron-paramagnetic resonance spectroscopy, mass spectrometry and other biophysical experiments will be employed to measure degradation pathways, radicals and ROS under varying crosslink densities and types as present in young, aged and diseased tendon tissues. RADICOL will establish protein mechanoradicals as an as yet uncovered source of oxidative stress, and as a new paradigm of biological mechanosensation and ageing.

Mots-clés : computational biophysics,protein biochemistry,material ageing,collagen