Project acronym 3D-BioMat
Project Deciphering biomineralization mechanisms through 3D explorations of mesoscale crystalline structure in calcareous biomaterials
Researcher (PI) VIRGINIE CHAMARD
Host Institution (HI) CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Country France
Call Details Consolidator Grant (CoG), PE3, ERC-2016-COG
Summary The fundamental 3D-BioMat project aims at providing a biomineralization model to explain the formation of microscopic calcareous single-crystals produced by living organisms. Although these crystals present a wide variety of shapes, associated to various organic materials, the observation of a nanoscale granular structure common to almost all calcareous crystallizing organisms, associated to an extended crystalline coherence, underlies a generic biomineralization and assembly process. A key to building realistic scenarios of biomineralization is to reveal the crystalline architecture, at the mesoscale, (i. e., over a few granules), which none of the existing nano-characterization tools is able to provide.
3D-BioMat is based on the recognized PI’s expertise in the field of synchrotron coherent x-ray diffraction microscopy. It will extend the PI’s disruptive pioneering microscopy formalism, towards an innovative high-throughput approach able at giving access to the 3D mesoscale image of the crystalline properties (crystal-line coherence, crystal plane tilts and strains) with the required flexibility, nanoscale resolution, and non-invasiveness.
This achievement will be used to timely reveal the generics of the mesoscale crystalline structure through the pioneering explorations of a vast variety of crystalline biominerals produced by the famous Pinctada mar-garitifera oyster shell, and thereby build a realistic biomineralization scenario.
The inferred biomineralization pathways, including both physico-chemical pathways and biological controls, will ultimately be validated by comparing the mesoscale structures produced by biomimetic samples with the biogenic ones. Beyond deciphering one of the most intriguing questions of material nanosciences, 3D-BioMat may contribute to new climate models, pave the way for new routes in material synthesis and supply answers to the pearl-culture calcification problems.
Summary
The fundamental 3D-BioMat project aims at providing a biomineralization model to explain the formation of microscopic calcareous single-crystals produced by living organisms. Although these crystals present a wide variety of shapes, associated to various organic materials, the observation of a nanoscale granular structure common to almost all calcareous crystallizing organisms, associated to an extended crystalline coherence, underlies a generic biomineralization and assembly process. A key to building realistic scenarios of biomineralization is to reveal the crystalline architecture, at the mesoscale, (i. e., over a few granules), which none of the existing nano-characterization tools is able to provide.
3D-BioMat is based on the recognized PI’s expertise in the field of synchrotron coherent x-ray diffraction microscopy. It will extend the PI’s disruptive pioneering microscopy formalism, towards an innovative high-throughput approach able at giving access to the 3D mesoscale image of the crystalline properties (crystal-line coherence, crystal plane tilts and strains) with the required flexibility, nanoscale resolution, and non-invasiveness.
This achievement will be used to timely reveal the generics of the mesoscale crystalline structure through the pioneering explorations of a vast variety of crystalline biominerals produced by the famous Pinctada mar-garitifera oyster shell, and thereby build a realistic biomineralization scenario.
The inferred biomineralization pathways, including both physico-chemical pathways and biological controls, will ultimately be validated by comparing the mesoscale structures produced by biomimetic samples with the biogenic ones. Beyond deciphering one of the most intriguing questions of material nanosciences, 3D-BioMat may contribute to new climate models, pave the way for new routes in material synthesis and supply answers to the pearl-culture calcification problems.
Max ERC Funding
1 966 429 €
Duration
Start date: 2017-03-01, End date: 2022-02-28
Project acronym BactRNA
Project Bacterial small RNAs networks unravelling novel features of transcription and translation
Researcher (PI) Maude Audrey Guillier
Host Institution (HI) CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Country France
Call Details Consolidator Grant (CoG), LS2, ERC-2018-COG
Summary Regulation of gene expression plays a key role in the ability of bacteria to rapidly adapt to changing environments and to colonize extremely diverse habitats. The relatively recent discovery of a plethora of small regulatory RNAs and the beginning of their characterization has unravelled new aspects of bacterial gene expression. First, the expression of many bacterial genes responds to a complex network of both transcriptional and post-transcriptional regulators. However, the properties of the resulting regulatory circuits on the dynamics of gene expression and in the bacterial adaptive response have been poorly addressed so far. In a first part of this project, we will tackle this question by characterizing the circuits that are formed between two widespread classes of bacterial regulators, the sRNAs and the two-component systems, which act at the post-transcriptional and the transcriptional level, respectively. The study of sRNAs also led to major breakthroughs regarding the basic mechanisms of gene expression. In particular, we recently showed that repressor sRNAs can target activating stem-loop structures located within the coding region of mRNAs that promote translation initiation, in striking contrast with the previously recognized inhibitory role of mRNA structures in translation. The second objective of this project is thus to draw an unprecedented map of non-canonical translation initiation events and their regulation by sRNAs.
Overall, this project will greatly improve our understanding of how bacteria can so rapidly and successfully adapt to many different environments, and in the long term, provide clues towards the development of anti-bacterial strategies.
Summary
Regulation of gene expression plays a key role in the ability of bacteria to rapidly adapt to changing environments and to colonize extremely diverse habitats. The relatively recent discovery of a plethora of small regulatory RNAs and the beginning of their characterization has unravelled new aspects of bacterial gene expression. First, the expression of many bacterial genes responds to a complex network of both transcriptional and post-transcriptional regulators. However, the properties of the resulting regulatory circuits on the dynamics of gene expression and in the bacterial adaptive response have been poorly addressed so far. In a first part of this project, we will tackle this question by characterizing the circuits that are formed between two widespread classes of bacterial regulators, the sRNAs and the two-component systems, which act at the post-transcriptional and the transcriptional level, respectively. The study of sRNAs also led to major breakthroughs regarding the basic mechanisms of gene expression. In particular, we recently showed that repressor sRNAs can target activating stem-loop structures located within the coding region of mRNAs that promote translation initiation, in striking contrast with the previously recognized inhibitory role of mRNA structures in translation. The second objective of this project is thus to draw an unprecedented map of non-canonical translation initiation events and their regulation by sRNAs.
Overall, this project will greatly improve our understanding of how bacteria can so rapidly and successfully adapt to many different environments, and in the long term, provide clues towards the development of anti-bacterial strategies.
Max ERC Funding
1 999 754 €
Duration
Start date: 2019-09-01, End date: 2024-08-31
Project acronym BigFastData
Project Charting a New Horizon of Big and Fast Data Analysis through Integrated Algorithm Design
Researcher (PI) Yanlei DIAO
Host Institution (HI) ECOLE POLYTECHNIQUE
Country France
Call Details Consolidator Grant (CoG), PE6, ERC-2016-COG
Summary This proposal addresses a pressing need from emerging big data applications such as genomics and data center monitoring: besides the scale of processing, big data systems must also enable perpetual, low-latency processing for a broad set of analytical tasks, referred to as big and fast data analysis. Today’s technology falls severely short for such needs due to the lack of support of complex analytics with scale, low latency, and strong guarantees of user performance requirements. To bridge the gap, this proposal tackles a grand challenge: “How do we design an algorithmic foundation that enables the development of all necessary pillars of big and fast data analysis?” This proposal considers three pillars:
1) Parallelism: There is a fundamental tension between data parallelism (for scale) and pipeline parallelism (for low latency). We propose new approaches based on intelligent use of memory and workload properties to integrate both forms of parallelism.
2) Analytics: The literature lacks a large body of algorithms for critical order-related analytics to be run under data and pipeline parallelism. We propose new algorithmic frameworks to enable such analytics.
3) Optimization: To run analytics, today's big data systems are best effort only. We transform such systems into a principled optimization framework that suits the new characteristics of big data infrastructure and adapts to meet user performance requirements.
The scale and complexity of the proposed algorithm design makes this project high-risk, at the same time, high-gain: it will lay a solid foundation for big and fast data analysis, enabling a new integrated parallel processing paradigm, algorithms for critical order-related analytics, and a principled optimizer with strong performance guarantees. It will also broadly enable accelerated information discovery in emerging domains such as genomics, as well as economic benefits of early, well-informed decisions and reduced user payments.
Summary
This proposal addresses a pressing need from emerging big data applications such as genomics and data center monitoring: besides the scale of processing, big data systems must also enable perpetual, low-latency processing for a broad set of analytical tasks, referred to as big and fast data analysis. Today’s technology falls severely short for such needs due to the lack of support of complex analytics with scale, low latency, and strong guarantees of user performance requirements. To bridge the gap, this proposal tackles a grand challenge: “How do we design an algorithmic foundation that enables the development of all necessary pillars of big and fast data analysis?” This proposal considers three pillars:
1) Parallelism: There is a fundamental tension between data parallelism (for scale) and pipeline parallelism (for low latency). We propose new approaches based on intelligent use of memory and workload properties to integrate both forms of parallelism.
2) Analytics: The literature lacks a large body of algorithms for critical order-related analytics to be run under data and pipeline parallelism. We propose new algorithmic frameworks to enable such analytics.
3) Optimization: To run analytics, today's big data systems are best effort only. We transform such systems into a principled optimization framework that suits the new characteristics of big data infrastructure and adapts to meet user performance requirements.
The scale and complexity of the proposed algorithm design makes this project high-risk, at the same time, high-gain: it will lay a solid foundation for big and fast data analysis, enabling a new integrated parallel processing paradigm, algorithms for critical order-related analytics, and a principled optimizer with strong performance guarantees. It will also broadly enable accelerated information discovery in emerging domains such as genomics, as well as economic benefits of early, well-informed decisions and reduced user payments.
Max ERC Funding
2 472 752 €
Duration
Start date: 2017-09-01, End date: 2022-08-31
Project acronym bioSPINspired
Project Bio-inspired Spin-Torque Computing Architectures
Researcher (PI) Julie Grollier
Host Institution (HI) CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Country France
Call Details Consolidator Grant (CoG), PE3, ERC-2015-CoG
Summary In the bioSPINspired project, I propose to use my experience and skills in spintronics, non-linear dynamics and neuromorphic nanodevices to realize bio-inspired spin torque computing architectures. I will develop a bottom-up approach to build spintronic data processing systems that perform low power ‘cognitive’ tasks on-chip and could ultimately complement our traditional microprocessors. I will start by showing that spin torque nanodevices, which are multi-functional and tunable nonlinear dynamical nano-components, are capable of emulating both neurons and synapses. Then I will assemble these spin-torque nano-synapses and nano-neurons into modules that implement brain-inspired algorithms in hardware. The brain displays many features typical of non-linear dynamical networks, such as synchronization or chaotic behaviour. These observations have inspired a whole class of models that harness the power of complex non-linear dynamical networks for computing. Following such schemes, I will interconnect the spin torque nanodevices by electrical and magnetic interactions so that they can couple to each other, synchronize and display complex dynamics. Then I will demonstrate that when perturbed by external inputs, these spin torque networks can perform recognition tasks by converging to an attractor state, or use the separation properties at the edge of chaos to classify data. In the process, I will revisit these brain-inspired abstract models to adapt them to the constraints of hardware implementations. Finally I will investigate how the spin torque modules can be efficiently connected together with CMOS buffers to perform higher level computing tasks. The table-top prototypes, hardware-adapted computing models and large-scale simulations developed in bioSPINspired will lay the foundations of spin torque bio-inspired computing and open the path to the fabrication of fully integrated, ultra-dense and efficient CMOS/spin-torque nanodevice chips.
Summary
In the bioSPINspired project, I propose to use my experience and skills in spintronics, non-linear dynamics and neuromorphic nanodevices to realize bio-inspired spin torque computing architectures. I will develop a bottom-up approach to build spintronic data processing systems that perform low power ‘cognitive’ tasks on-chip and could ultimately complement our traditional microprocessors. I will start by showing that spin torque nanodevices, which are multi-functional and tunable nonlinear dynamical nano-components, are capable of emulating both neurons and synapses. Then I will assemble these spin-torque nano-synapses and nano-neurons into modules that implement brain-inspired algorithms in hardware. The brain displays many features typical of non-linear dynamical networks, such as synchronization or chaotic behaviour. These observations have inspired a whole class of models that harness the power of complex non-linear dynamical networks for computing. Following such schemes, I will interconnect the spin torque nanodevices by electrical and magnetic interactions so that they can couple to each other, synchronize and display complex dynamics. Then I will demonstrate that when perturbed by external inputs, these spin torque networks can perform recognition tasks by converging to an attractor state, or use the separation properties at the edge of chaos to classify data. In the process, I will revisit these brain-inspired abstract models to adapt them to the constraints of hardware implementations. Finally I will investigate how the spin torque modules can be efficiently connected together with CMOS buffers to perform higher level computing tasks. The table-top prototypes, hardware-adapted computing models and large-scale simulations developed in bioSPINspired will lay the foundations of spin torque bio-inspired computing and open the path to the fabrication of fully integrated, ultra-dense and efficient CMOS/spin-torque nanodevice chips.
Max ERC Funding
1 907 767 €
Duration
Start date: 2016-09-01, End date: 2021-08-31
Project acronym BRAINandMINDFULNESS
Project Impact of Mental Training of Attention and Emotion Regulation on Brain and Behavior: Implications for Neuroplasticity, Well-Being and Mindfulness Psychotherapy Research
Researcher (PI) Antoine Lutz
Host Institution (HI) INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE
Country France
Call Details Consolidator Grant (CoG), SH4, ERC-2013-CoG
Summary Mindfulness-based therapy has become an increasingly popular treatment to reduce stress, increase well-being and prevent relapse in depression. A key component of these therapies includes mindfulness practice that intends to train attention to detect and regulate afflictive cognitive and emotional patterns. Beyond its therapeutic application, the empirical study of mindfulness practice also represents a promising tool to understand practices that intentionally cultivate present-centeredness and openness to experience. Despite its clinical efficacy, little remains known about its means of action. Antithetic to this mode of experiential self-focus are states akin to depression, that are conducive of biased attention toward negativity, biased thoughts and rumination, and dysfunctional self schemas. The proposed research aims at implementing an innovative framework to scientifically investigate the experiential, cognitive, and neural processes underlining mindfulness practice building on the current neurocognitive understanding of the functional and anatomical architecture of cognitive control, and depression. To identify these mechanisms, this project aims to use paradigms from cognitive, and affective neuroscience (MEG, intracortical EEG, fMRI) to measure the training and plasticity of emotion regulation and cognitive control, and their effect on automatic, self-related affective processes. Using a cross-sectional design, this project aims to compare participants with trait differences in experiential self-focus mode. Using a longitudinal design, this project aims to explore mindfulness-practice training’s effect using a standard mindfulness-based intervention and an active control intervention. The PI has pioneered the neuroscientific investigation of mindfulness in the US and aspires to assemble a research team in France and a network of collaborators in Europe to pursue this research, which could lead to important outcomes for neuroscience, and mental health.
Summary
Mindfulness-based therapy has become an increasingly popular treatment to reduce stress, increase well-being and prevent relapse in depression. A key component of these therapies includes mindfulness practice that intends to train attention to detect and regulate afflictive cognitive and emotional patterns. Beyond its therapeutic application, the empirical study of mindfulness practice also represents a promising tool to understand practices that intentionally cultivate present-centeredness and openness to experience. Despite its clinical efficacy, little remains known about its means of action. Antithetic to this mode of experiential self-focus are states akin to depression, that are conducive of biased attention toward negativity, biased thoughts and rumination, and dysfunctional self schemas. The proposed research aims at implementing an innovative framework to scientifically investigate the experiential, cognitive, and neural processes underlining mindfulness practice building on the current neurocognitive understanding of the functional and anatomical architecture of cognitive control, and depression. To identify these mechanisms, this project aims to use paradigms from cognitive, and affective neuroscience (MEG, intracortical EEG, fMRI) to measure the training and plasticity of emotion regulation and cognitive control, and their effect on automatic, self-related affective processes. Using a cross-sectional design, this project aims to compare participants with trait differences in experiential self-focus mode. Using a longitudinal design, this project aims to explore mindfulness-practice training’s effect using a standard mindfulness-based intervention and an active control intervention. The PI has pioneered the neuroscientific investigation of mindfulness in the US and aspires to assemble a research team in France and a network of collaborators in Europe to pursue this research, which could lead to important outcomes for neuroscience, and mental health.
Max ERC Funding
1 868 520 €
Duration
Start date: 2014-11-01, End date: 2020-10-31
Project acronym CARINE
Project Coherent diffrAction foR a look Inside Nanostructures towards atomic rEsolution: catalysis and interface
Researcher (PI) Marie-Ingrid RICHARD
Host Institution (HI) COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
Country France
Call Details Consolidator Grant (CoG), PE3, ERC-2018-COG
Summary Heterogeneous catalysis of nanoparticles has recently emerged as highly promising way to speed up catalytic processes due to their far higher surface area compared to bulk materials. But they face significant challenges in achieving high catalytic activity and sufficient durability. A key problem has been that all existing approaches to the characterization of atomic scale phenomena in these materials either lack structural specificity or can be employed under highly unrealistic catalytic environments. As an example, operando x-ray catalysis has often been carried out under idealized conditions and averaging information from macroscopic facets. This approach suffers from the lack of transferability to nanocrystalline systems. To tackle this problem, I am developing new state-of-the-art in situ techniques based on coherent x-ray scattering and complementary chemical characterization, with which I will optimize catalyst and reactor operations simultaneously. This is the ambition of the CARINE project to study in situ and operando the structural evolution of catalytic nanoparticles in realistic conditions during reaction by using the unique capabilities of coherent diffraction Bragg imaging (CDI). My proposed work builds on my recent exciting proof-of-concept experiments using Pt nanocrystals that demonstrate the sensitivity and spatial resolution of CDI under liquid conditions. As dedicated instruments for CDI have just reached user operation, it is only now that this new imaging technique can be applied during reaction and can probe structural changes of individual nanocrystals under conditions where up to now, no other techniques could probe the relevant parameters. My project will shed light into most relevant unsolved issues (durability, activity…) that limit the efficiency of today’s industrial processes and will open new horizons with outstanding impact in catalytic research.
Summary
Heterogeneous catalysis of nanoparticles has recently emerged as highly promising way to speed up catalytic processes due to their far higher surface area compared to bulk materials. But they face significant challenges in achieving high catalytic activity and sufficient durability. A key problem has been that all existing approaches to the characterization of atomic scale phenomena in these materials either lack structural specificity or can be employed under highly unrealistic catalytic environments. As an example, operando x-ray catalysis has often been carried out under idealized conditions and averaging information from macroscopic facets. This approach suffers from the lack of transferability to nanocrystalline systems. To tackle this problem, I am developing new state-of-the-art in situ techniques based on coherent x-ray scattering and complementary chemical characterization, with which I will optimize catalyst and reactor operations simultaneously. This is the ambition of the CARINE project to study in situ and operando the structural evolution of catalytic nanoparticles in realistic conditions during reaction by using the unique capabilities of coherent diffraction Bragg imaging (CDI). My proposed work builds on my recent exciting proof-of-concept experiments using Pt nanocrystals that demonstrate the sensitivity and spatial resolution of CDI under liquid conditions. As dedicated instruments for CDI have just reached user operation, it is only now that this new imaging technique can be applied during reaction and can probe structural changes of individual nanocrystals under conditions where up to now, no other techniques could probe the relevant parameters. My project will shed light into most relevant unsolved issues (durability, activity…) that limit the efficiency of today’s industrial processes and will open new horizons with outstanding impact in catalytic research.
Max ERC Funding
1 875 000 €
Duration
Start date: 2019-11-01, End date: 2024-10-31
Project acronym CIRQUSS
Project Circuit Quantum Electrodynamics with Single Electronic and Nuclear Spins
Researcher (PI) Patrice Emmanuel Bertet
Host Institution (HI) COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
Country France
Call Details Consolidator Grant (CoG), PE3, ERC-2013-CoG
Summary "Electronic spins are usually detected by their interaction with electromagnetic fields at microwave frequencies. Since this interaction is very weak, only large ensembles of spins can be detected. In circuit quantum electrodynamics (cQED) on the other hand, artificial superconducting atoms are made to interact strongly with microwave fields at the single photon level, and quantum-limited detection of few-photon microwave signals has been developed.
The goal of this project is to apply the concepts and techniques of cQED to the detection and manipulation of electronic and nuclear spins, in order to reach a novel regime in which a single electronic spin strongly interacts with single microwave photons. This will lead to
1) A considerable enhancement of the sensitivity of spin detection by microwave methods. We plan to detect resonantly single electronic spins in a few milliseconds. This could enable A) to perform electron spin resonance spectroscopy on few-molecule samples B) to measure the magnetization of various nano-objects at millikelvin temperatures, using the spin as a magnetic sensor with nanoscale resolution.
2) Applications in quantum information science. Strong interaction with microwave fields at the quantum level will enable the generation of entangled states of distant individual electronic and nuclear spins, using superconducting qubits, resonators and microwave photons, as “quantum data buses” mediating the entanglement. Since spins can have coherence times in the seconds range, this could pave the way towards a scalable implementation of quantum information processing protocols.
These ideas will be primarily implemented with NV centers in diamond, which are electronic spins with properties suitable for the project."
Summary
"Electronic spins are usually detected by their interaction with electromagnetic fields at microwave frequencies. Since this interaction is very weak, only large ensembles of spins can be detected. In circuit quantum electrodynamics (cQED) on the other hand, artificial superconducting atoms are made to interact strongly with microwave fields at the single photon level, and quantum-limited detection of few-photon microwave signals has been developed.
The goal of this project is to apply the concepts and techniques of cQED to the detection and manipulation of electronic and nuclear spins, in order to reach a novel regime in which a single electronic spin strongly interacts with single microwave photons. This will lead to
1) A considerable enhancement of the sensitivity of spin detection by microwave methods. We plan to detect resonantly single electronic spins in a few milliseconds. This could enable A) to perform electron spin resonance spectroscopy on few-molecule samples B) to measure the magnetization of various nano-objects at millikelvin temperatures, using the spin as a magnetic sensor with nanoscale resolution.
2) Applications in quantum information science. Strong interaction with microwave fields at the quantum level will enable the generation of entangled states of distant individual electronic and nuclear spins, using superconducting qubits, resonators and microwave photons, as “quantum data buses” mediating the entanglement. Since spins can have coherence times in the seconds range, this could pave the way towards a scalable implementation of quantum information processing protocols.
These ideas will be primarily implemented with NV centers in diamond, which are electronic spins with properties suitable for the project."
Max ERC Funding
1 999 995 €
Duration
Start date: 2014-03-01, End date: 2019-02-28
Project acronym CORPHO
Project Theory of strongly correlated photonic systems
Researcher (PI) Cristiano Ciuti
Host Institution (HI) UNIVERSITE PARIS DIDEROT - PARIS 7
Country France
Call Details Consolidator Grant (CoG), PE3, ERC-2013-CoG
Summary "The physics of complex quantum systems with controllable interactions is emerging as a fundamental topic for a broad community, providing an opportunity to test theories of strongly correlated quantum many-body systems and opening interesting applications such as quantum simulators. Recently, in solid-state structures with effective photon-photon interactions the rich physics of quantum fluids of light has been explored, albeit not yet in the regime of strong photonic correlations. Exciting advances in cavity Quantum Electro-Dynamics (QED) and superconducting circuit QED make strong photon-photon interactions now accessible. A growing interest is focusing on lattices of coupled resonators, implementing Hubbard-like Hamiltonians for photons injected by pump driving fields. Similarly to electronic systems, the physics of large two-dimensional (2D) photonic lattices is a fundamental theoretical challenge in the regime of strong correlations. CORPHO has the ambition to develop novel scalable theoretical methods for 2D lattices of cavities, including spatially inhomogeneous driving and dissipation. The proposed methods are based on a hybrid strategy combining cluster mean-field theory and Wave Function Monte Carlo on a physical ‘Corner’ of the Hilbert space in order to calculate the steady-state density matrix and the properties of the non-equilibrium phases. We will study 2D lattices with complex unit cells and ‘fractional’ driving (only a fraction of the sites is pumped), a configuration that, according to recent preliminary studies, is expected to dramatically enhance and enrich quantum correlations. We will also investigate the interplay between driving and geometric frustration in 2D lattices with polarization-dependent interactions. Finally, the quantum control of strongly correlated photonic systems will be explored, including quantum feedback processes, cooling of thermal fluctuations and switching between multi-stable phases."
Summary
"The physics of complex quantum systems with controllable interactions is emerging as a fundamental topic for a broad community, providing an opportunity to test theories of strongly correlated quantum many-body systems and opening interesting applications such as quantum simulators. Recently, in solid-state structures with effective photon-photon interactions the rich physics of quantum fluids of light has been explored, albeit not yet in the regime of strong photonic correlations. Exciting advances in cavity Quantum Electro-Dynamics (QED) and superconducting circuit QED make strong photon-photon interactions now accessible. A growing interest is focusing on lattices of coupled resonators, implementing Hubbard-like Hamiltonians for photons injected by pump driving fields. Similarly to electronic systems, the physics of large two-dimensional (2D) photonic lattices is a fundamental theoretical challenge in the regime of strong correlations. CORPHO has the ambition to develop novel scalable theoretical methods for 2D lattices of cavities, including spatially inhomogeneous driving and dissipation. The proposed methods are based on a hybrid strategy combining cluster mean-field theory and Wave Function Monte Carlo on a physical ‘Corner’ of the Hilbert space in order to calculate the steady-state density matrix and the properties of the non-equilibrium phases. We will study 2D lattices with complex unit cells and ‘fractional’ driving (only a fraction of the sites is pumped), a configuration that, according to recent preliminary studies, is expected to dramatically enhance and enrich quantum correlations. We will also investigate the interplay between driving and geometric frustration in 2D lattices with polarization-dependent interactions. Finally, the quantum control of strongly correlated photonic systems will be explored, including quantum feedback processes, cooling of thermal fluctuations and switching between multi-stable phases."
Max ERC Funding
1 378 440 €
Duration
Start date: 2014-06-01, End date: 2019-05-31
Project acronym CORTIGRAD
Project Cortical gradients of functional integration
Researcher (PI) Daniel MARGULIES
Host Institution (HI) CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Country France
Call Details Consolidator Grant (CoG), SH4, ERC-2019-COG
Summary Historically, cognitive neuroscience has focused on discrete, mutually exclusive modules or networks, however, current network-level descriptions of brain organization fail to account for integrated features of cognition. I recently described a principal gradient in cortical connectivity that reflects how activity from primary sensory/motor areas is integrated into transmodal regions of the default-mode network. This novel line of research led me to hypothesize that coherent aspects of cognition are an emergent property of a whole brain architecture consisting of multiple zones of integration. In particular, I hypothesize that each region of transmodal cortex is the apex of a zone of integration that is anchored by multiple unimodal cortical regions. To investigate the mechanism that allows abstract representations to form in transmodal systems, I first propose structural studies to investigate covariance in zone geometry across healthy adults, how zones have emerged through evolution and how they change across the lifespan. I will then explore the functional consequence of zones of integration for higher-order human cognition. I will examine if individual differences in cognition emerges from variation in the architecture of different zones, and how brain activity is altered when simple decisions depend on integrating information from multiple zones. Finally, I will examine how the absence of input from a sensory modality (through congenital deafness or blindness) alters the structure and function of transmodal regions in a zone-specific manner. By describing how the spatial layout of the cortex shapes its functions, this research provides a radically new framework for understanding the structural constraints that underpin the integrated nature of human cognition.
Summary
Historically, cognitive neuroscience has focused on discrete, mutually exclusive modules or networks, however, current network-level descriptions of brain organization fail to account for integrated features of cognition. I recently described a principal gradient in cortical connectivity that reflects how activity from primary sensory/motor areas is integrated into transmodal regions of the default-mode network. This novel line of research led me to hypothesize that coherent aspects of cognition are an emergent property of a whole brain architecture consisting of multiple zones of integration. In particular, I hypothesize that each region of transmodal cortex is the apex of a zone of integration that is anchored by multiple unimodal cortical regions. To investigate the mechanism that allows abstract representations to form in transmodal systems, I first propose structural studies to investigate covariance in zone geometry across healthy adults, how zones have emerged through evolution and how they change across the lifespan. I will then explore the functional consequence of zones of integration for higher-order human cognition. I will examine if individual differences in cognition emerges from variation in the architecture of different zones, and how brain activity is altered when simple decisions depend on integrating information from multiple zones. Finally, I will examine how the absence of input from a sensory modality (through congenital deafness or blindness) alters the structure and function of transmodal regions in a zone-specific manner. By describing how the spatial layout of the cortex shapes its functions, this research provides a radically new framework for understanding the structural constraints that underpin the integrated nature of human cognition.
Max ERC Funding
1 998 961 €
Duration
Start date: 2020-12-01, End date: 2025-11-30
Project acronym DBA
Project Distributed Biological Algorithms
Researcher (PI) Amos Korman
Host Institution (HI) CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Country France
Call Details Consolidator Grant (CoG), PE6, ERC-2014-CoG
Summary This project proposes a new application for computational reasoning. More specifically, the purpose of this interdisciplinary project is to demonstrate the usefulness of an algorithmic perspective in studies of complex biological systems. We focus on the domain of collective behavior, and demonstrate the benefits of using techniques from the field of theoretical distributed computing in order to establish algorithmic insights regarding the behavior of biological ensembles. The project includes three related tasks, for which we have already obtained promising preliminary results. Each task contains a purely theoretical algorithmic component as well as one which integrates theoretical algorithmic studies with experiments. Most experiments are strategically designed by the PI based on computational insights, and are physically conducted by experimental biologists that have been carefully chosen by the PI. In turn, experimental outcomes will be theoretically analyzed via an algorithmic perspective. By this integration, we aim at deciphering how a biological individual (such as an ant) “thinks”, without having direct access to the neurological process within its brain, and how such limited individuals assemble into ensembles that appear to be far greater than the sum of their parts. The ultimate vision behind this project is to enable the formation of a new scientific field, called algorithmic biology, that bases biological studies on theoretical algorithmic insights.
Summary
This project proposes a new application for computational reasoning. More specifically, the purpose of this interdisciplinary project is to demonstrate the usefulness of an algorithmic perspective in studies of complex biological systems. We focus on the domain of collective behavior, and demonstrate the benefits of using techniques from the field of theoretical distributed computing in order to establish algorithmic insights regarding the behavior of biological ensembles. The project includes three related tasks, for which we have already obtained promising preliminary results. Each task contains a purely theoretical algorithmic component as well as one which integrates theoretical algorithmic studies with experiments. Most experiments are strategically designed by the PI based on computational insights, and are physically conducted by experimental biologists that have been carefully chosen by the PI. In turn, experimental outcomes will be theoretically analyzed via an algorithmic perspective. By this integration, we aim at deciphering how a biological individual (such as an ant) “thinks”, without having direct access to the neurological process within its brain, and how such limited individuals assemble into ensembles that appear to be far greater than the sum of their parts. The ultimate vision behind this project is to enable the formation of a new scientific field, called algorithmic biology, that bases biological studies on theoretical algorithmic insights.
Max ERC Funding
1 894 947 €
Duration
Start date: 2015-05-01, End date: 2021-04-30
