Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : NEUROMORPH (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : TU Wien — Pays : Autriche — Rôle : PI
Acronyme de l'entité : TUW
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1945713.0 €

Network structures arise in many applications like for biological tissues, neuron systems, and nanoelectronic devices. Neuronal network structures are inspiring novel neuromorphic computer architectures, overcoming physical scaling limits in traditional hardware. The project NEUROMORPH focuses on the interplay of emerging structures in biological neuron systems and electronic circuit models. The problems we address are formulated in terms of nonlinear partial differential systems, including stochastic and nonlocal terms. Examples include transport through ion channels, chemotaxis-fluid systems, mean-field network models, and memristor networks. The aims of this mathematics-oriented project are to explore the structure of the multiscale systems, prove their well-posedness, and devise structure-preserving numerical methods. Mathematical challenges are coming from the cross-diffusion character, the coupling of different types of equations (partially diffusive, stochastic, algebraic), the nonstandard degeneracies of the equations, and the hierarchy of scales, ranging from the molecular to the cellular to the network level. To achieve these goals, we develop new tools by combining variants of the boundedness-by-entropy method, compensated compactness, stability theory, and stochastic analysis. We build on the expertise of the PI on semiconductor device modeling, theory of cross-diffusion systems, numerical analysis, and recent work on stochastic differential equations. Concepts from thermodynamics, cell biology, and electrical engineering will be condensed into innovative mathematical theories for cross-diffusion systems and multiscale models. The project culminates in the simulation of small bio-inspired neuromorphic circuits, where memristor devices model the behavior of synapses or ion channels and mimic neuronal connectivity. The combination of bio-physical and device-circuit models is expected to make a vital progress for the design of neuromorphic structures.

Mots-clés : cross diffusion systems,ion transport,chemotaxis,network models,memristor devices,neuromorphic computing,existence analysis,numerical analysis
Projet ERC : DYNASNET (Horizon 2020, appel 2018, Synergy grants)
Entité participante : Charles University — Pays : Rép. Tchèque — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2031250.0 €

Networks define our life, being essential to cell biology, communications, social and economic systems, and impacting virtually all areas of science and technology. The aim of this proposal is to engage leading experts in network science and graph theory to build a mathematically sound theory of dynamical networks, which will be transformative to our understanding of complex systems, with applications in multiple disciplines. Both fields have made major conceptual advances in the past decade: network science has offered a data-based basic topological description of complex networks, and has started to address the inherently dynamical nature of real networks, their reconstruction and control; in mathematics we have seen major advances in graph limit theory, the local-global dichotomy in observation, and promising steps in the theory of graphs with intermediate degrees, that capture real networks. While these concepts offer different formalisms to capture the same underlying reality, there has been no conversation between the two communities, limiting our understanding of real networks. The proposed research aims to build on these advances to construct a coherent theory of dynamical networks, and to exploit its applications and predictive power to various real systems. We plan to offer a sound mathematical foundation of network science, helping us better analyze, predict and control the behavior of real networks. It will benefit mathematics in leading to an enriched, robust graph limit theory, with exciting applications in multiple areas of mathematics. To enhance the wider impact of the proposed mathematical advances, we plan to conduct a permanent conversation with experts from different domains that encounter and explore real networks, from cell biology to brain science and transportation and communication networks, inspiring with novel questions and helping the application of our advances in these domains.

Mots-clés : graph limit,dynamic network,processes on graphs,network science,complex systems,network biology
Projet ERC : NESTEDMICS (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Life Science
Panel : Integrative Biology: from Genes and Genomes to Systems
Entité participante : Centre for Genomic Regulation — Pays : Espagne — Rôle : PI
Acronyme de l'entité : CRG;CRG-CERCA
Type d'entité : Recherche
Financement ERC de l'entité : 1974541.0 €

Neural microexons are a paradigmatic example of a cell type-specific transcriptomic program. Microexons are tiny exons that we revealed to have striking neuronal specificity established by their master splicing regulator Srrm4, which activates them during neuronal differentiation. However, our unpublished data challenge this on/off regulatory and functional paradigm. We found that a related paralog, Srrm3, is lowly but significantly expressed also in endocrine pancreas and, together with Srrm4, configure a 3-step switch of Srrm3/4 activity in pancreas (low), brain (mid) and retina (high). These different levels of expression activate increasingly larger subsets of microexons in the three tissues, configuring a triple-nested microexon program. Remarkably, initial results support a model in which microexon subclass inclusion is dictated largely by their sensitivity to Srrm3/4, and each subclass is differentially enriched for distinct functional categories including vesicle-mediated transport, neuronal differentiation and cilium biogenesis. This project will assess the regulatory and functional architecture of this new paradigm by answering: (1) How are the different levels of the master regulators controlled in each cell type? (2) How are the distinct sensitivities of microexons to Srrm3/4 genomically encoded? (3) What are the functional implications of the 'nestedness' of the microexon programs? (4) How does misregulation of the nested programs contribute to disease? These goals will be achieved by a combination of high-throughput methods and focused experiments using in vitro and in vivo systems. The expected results will provide a transformative multi-level portrait of microexons, from quantitative regulatory logic to organismal functions. Moreover, this novel paradigm is likely to apply to many other master regulators, expanding the impact of the project and shedding new light into how cell type-specific transcriptomes are established in embryogenesis.

Mots-clés : alternative splicing,cis regulatory code,endocrine pancreas,neurobiology
Projet ERC : NEUROCLOUD (Horizon 2020, appel 2017, Proof of concept grants)
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 : 148550.0 €

Neural network simulations have been a driving force behind our advances in the artificial intelligence filed, but also a key tool in our understanding of the human brain. However, exploring such networks usually engages substantial computational resources requiring specific programing skills and dedicated facilities. In our main project NETSIGNAL, we have developed computational environment ARACHNE aiming to facilitate high-end neural network exploration among scientific and industrial users. ARACHNE enables an investigator to construct and explore cellular networks of arbitrary biophysical and architectural complexity using a simple interface on a local computer or a mobile device. Through small-file exchange via the internet, the interface controls a pre-configured computational kernel installed on a remote computer cluster or cloud. Uniquely, ARACHNE can integrate traditional wired network connections with diffuse, volume-transmitted type of inter-cellular signalling, to reflect the emerging role of glia in brain processing. ARACHNE thus affords a flexible neural network design and incorporates latest advances in neuroscience while providing a portable console suitable for IT-untrained explorers. ARACHNE has passed a feasibility and novelty test by independent experts (Aleksin et al, PLoS Comp Biology 2017, in press), its test version is available on-line (https://github.com/LeonidSavtchenko/Arachne/). Our goal here is to determine market potential and the commercial viability of ARACHNE and its conceptual design, but also to establish industry-standard provisions for its commercial exploitation. We plan: (a) to upgrade ARACHNE technical specs, interface, support, etc. to the industry standard; (b) to assess the market and the competitors; (c) to determine IPR position and strategy, (d) to explore budgeting and further funding strategy, (e) to establish initial provisions for its distribution and/or licensing of ARACHNE.

Mots-clés : brain,synaptic circuit,neural network,astroglia
Projet ERC : NeuroAgents (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : University of Zurich — Pays : Suisse — Rôle : PI
Acronyme de l'entité : UZH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999090.0 €

Neural networks and deep learning algorithms are currently achieving impressive state-of-the-art results. In parallel computational neuroscience has made tremendous progress with both theories of neural computation and with hardware implementations of dedicated brain-inspired computing platforms. However, despite this remarkable progress, today’s artificial systems are still not able to compete with biological ones in tasks that involve processing of sensory data acquired in real-time, in complex and uncertain settings. One of the reasons is that neural computation in biological systems is very different from the way today's computers operate: it is tightly linked to the properties of their computational embodiment, to the physics of their computing elements and to their temporal dynamics. Conventional computers on the other hand operate with mainly serial and synchronous logic gates, with functions that are decoupled from their hardware implementation, and with discretized and virtual time. In this project we will combine the recent advancements in machine learning and neural computation with the latest developments in neuromorphic computing technology to design autonomous systems that can express robust cognitive behavior while interacting with the environment, through the physics of their computing substrate. To achieve this we will embed in robotic platforms microelectronic neuromorphic processors and sensors that implement biophysically realistic neural computational primitives and dynamics. We will adopt active-sensing and on-line spike-based learning strategies, context and state-dependent computation, and probabilistic inference methods for "programming" these neuromorphic cognitive agents to solve challenging tasks in real-time. Our results will lead to compact low-power intelligent sensory-motor systems that will have a large impact on service and consumer robotics, Internet of Things, as well as prosthetics and personalized medicine.

Mots-clés : neural networks,cognitive systems,biomimetics,computational neuroscience,embedded systems,deep netwroks,learning,neuromorphic
Projet ERC : MicroCyFly (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Universitätsmedizin Göttingen — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : UMG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 301548.36 €

Neural networks process sensory inputs into outputs that guide behavioral responses. The computations that take place can be complex, suggesting intricate circuit architecture. While we are starting to unravel the circuit components of a specific neural computation, the extraction of visual motion cues, we realize that the circuits are more complex than anticipated. Here, we aim to reveal the full microcircuitry of behaviorally relevant motion-detecting pathways with complex physiological properties. To understand the network implementation of a critical computation, we are studying motion detection in Drosophila. A hallmark is the extraction of direction-selective (DS) signals, which is achieved by spatiotemporal correlations of inputs. Neurons that are sufficient to set up DS signals have been identified, but are often not behaviorally necessary, suggesting redundant circuits at minimum. We have isolated a core visual pathway that is required for behavioral responses to motion cues, but displays physiological properties that are not in line with current models of motion detection. Recent data also show that DS neurons have complex receptive fields. Further, single neuronal inputs feed into both ON and OFF pathways, which later converge to control the behavioral output. What are the microcircuits that shape such receptive fields? And how do individual neurons, or individual synaptic connections contribute to one specific pathway and what is their specific computational role? We will dissect how complex receptive field properties and behavior are shaped by individual neurons. Further, we will develop a new tool to conditionally inactivate specific synaptic connection, Flp-TEV. This will allow to determine how individual synapses contribute to distinct downstream circuit properties, and behavior. We are thus proposing to map the circuit architecture of a behaviorally relevant visual pathway and understand how complex microcircuits perform relevant computation

Mots-clés : visual system circuitry,subcellular microcircuit dissection,motion vision,drosophila,synapse
Projet ERC : MicroCyFly (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Johannes Gutenberg University Mainz — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1196163.64 €

Neural networks process sensory inputs into outputs that guide behavioral responses. The computations that take place can be complex, suggesting intricate circuit architecture. While we are starting to unravel the circuit components of a specific neural computation, the extraction of visual motion cues, we realize that the circuits are more complex than anticipated. Here, we aim to reveal the full microcircuitry of behaviorally relevant motion-detecting pathways with complex physiological properties. To understand the network implementation of a critical computation, we are studying motion detection in Drosophila. A hallmark is the extraction of direction-selective (DS) signals, which is achieved by spatiotemporal correlations of inputs. Neurons that are sufficient to set up DS signals have been identified, but are often not behaviorally necessary, suggesting redundant circuits at minimum. We have isolated a core visual pathway that is required for behavioral responses to motion cues, but displays physiological properties that are not in line with current models of motion detection. Recent data also show that DS neurons have complex receptive fields. Further, single neuronal inputs feed into both ON and OFF pathways, which later converge to control the behavioral output. What are the microcircuits that shape such receptive fields? And how do individual neurons, or individual synaptic connections contribute to one specific pathway and what is their specific computational role? We will dissect how complex receptive field properties and behavior are shaped by individual neurons. Further, we will develop a new tool to conditionally inactivate specific synaptic connection, Flp-TEV. This will allow to determine how individual synapses contribute to distinct downstream circuit properties, and behavior. We are thus proposing to map the circuit architecture of a behaviorally relevant visual pathway and understand how complex microcircuits perform relevant computation

Mots-clés : visual system circuitry,subcellular microcircuit dissection,motion vision,drosophila,synapse
Projet ERC : ENTRAINER (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1497104.0 €

Neural oscillations are ubiquitous in the human brain and have been implicated in diverse cognitive functions to support both neural communication and plasticity. Their functional relevance is further supported by a large number of studies linking various cognitive deficits (e.g., attention deficit hyperactivity disorder, ADHD) with abnormal neural oscillations. However, this field of research faces two important problems: First, there is only correlative, but no causal evidence linking cognitive deficits to abnormal neural oscillations in humans. Second, there is virtually no theory-driven mechanistic approach that generates insights into how oscillations within and across neural networks are linked to human behavior. In this project, I propose to take decisive steps to provide a long-needed neurophysiological characterization—via (1) computational modelling, (2) electrophysiological measures, and (3) novel non-invasive manipulations of cortical rhythms—on how neural oscillations contribute to two types of cognitive processes that are fundamental for many aspects of human behavior: attention and short-term memory. I will go a step further by demonstrating that it is possible to augment performance in these cognitive functions with the design of non-invasive brain stimulation protocols individually tailored to the theory-driven neurocomputational characterizations and electrophysiological signatures of each individual. This will result in the applied goal of deriving new neuro-computational assays that can detect deviant network interactions causally related to cognitive functions, which is key for then renormalizing those functions in neuropsychological conditions such as ADHD. Thus, if successful, my proposed work will ultimately result in novel, low-cost, and painless non-invasive neural interventions for a wide range of neuropsychological disorders tied to abnormal neural oscillations.

Mots-clés : brain stimulation,fmri,oscillations
Projet ERC : ConCorND (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : University of Bern — Pays : Suisse — Rôle : PI
Acronyme de l'entité : UB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Neurodegenerative diseases (NDs) are incurable, debilitating conditions, arise mid-late in life, represent an enormous health and socioeconomic burden and no therapies exist. An enigmatic finding in NDs is the early and selective alteration in intrinsic excitability of vulnerable neurons paralleling changes in its circuitry. However, a gap in understanding exists in ND field about the cause of these alterations and whether these modifications regulate degenerative pathomechanisms. Our recent study, examining mechanisms of Purkinje cell (PC) degeneration in Spinocerebellar ataxia type 1 (SCA1) revealed that the earliest cerebellar alterations occur in the major excitatory inputs onto PCs, the climbing fibers (CFs). Based on this, we propose a novel three-step model of neurodegeneration: First, suboptimal functioning of the presynaptic inputs initiates signaling deficits in target PCs. Second, those alterations trigger maladaptive responses such as altered intrinsic PC excitability, thus amplifying pathogenic cascades. Third, at network level progressive dysfunction triggers compensatory synaptic modifications within the cerebellar circuitry. In this proposal, we will test our new hypothesis for NDs on SCA1 and this will be the first study to test circuit-dependency in NDs by selectively silencing presynaptic inputs and examining molecular responses in the postsynaptic neuron. Specifically, we will 1) Identify the dysfunctional CF associated molecular signature in PCs. 2) Elucidate mechanisms involved in altering intrinsic PC excitability. 3) Map the connectome for a structural correlate of the pathology. Using conditional mouse models, pharmacogenetics, transcriptomics, proteomics and connectomics, we will delineate molecular alterations that govern disease from compensatory alterations. Our systematic approach will not only impact SCA related therapies but the entire spectrum of NDs and has the potential to change the conceptual approach of future studies on NDs.

Mots-clés : neurodegeneration,spinocerebellar ataxia type 1,cerebellum,purkinje cells,circuits,mouse models,pharmacogenetics,rna sequencing,mass spectrometry,adeno associated viruses,behavior
Projet ERC : CALVARIA (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Helmholtz Zentrum München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HMGU
Type d'entité : Recherche
Financement ERC de l'entité : 1996900.0 €

Neurodegenerative diseases (NDs) are labeled as the epidemic of the 21st century. The inflammation of the brain is a common pathology observed in many NDs. Here I aim to exploit our discovery of skull – meninges connections (SMCs) mediating immune cell trafficking into the brain to ease this healthcare burden. Using tissue clearing methods, we found that the skull (calvaria) bone marrow is directly connected to the brain meninges, a discovery that was also shown by an independent group around the same time. This finding suggests that the calvaria is involved in diverse brain pathologies. Easier accessibility of the calvaria compared to brain parenchyma also makes it an attractive region to target, which would alleviate hurdles of drug delivery into the brain, e.g., to control neuroinflammation. Furthermore, potential biomarkers in the calvaria reflecting pathologies of the brain could be easier and faster to detect. However, the detailed structural and cellular characteristics of the SMCs and the calvaria, and how they correlate and interact with neuropathology remain unknown. Here, we will utilize high-throughput technologies such as tissue clearing, single cell RNAseq, proteomics and unbiased analysis of data using deep learning to close this knowledge gap. We will use ischemic stroke and dementia models that are associated with acute and chronic neuroinflammation, respectively, and study both mouse and human tissues in parallel to validate potential clinical implications. This project will investigate followings: Aim 1 | Calvaria in physiological state Aim 2 | Therapeutic aspect: manipulation of calvaria to control stroke and dementia Aim 3 | Diagnostic aspect: calvaria imaging to monitor stroke and dementia Thus, the first in-depth investigation of the calvaria bone in health and disease could deliver novel diagnostic and therapeutic targets to mitigate the increasing burden of neurological diseases.

Mots-clés : novel therapeutics and diagnostics,neuroinflammation,single cell rna seq,proteomics,deep learning,tissue clearing