Project acronym 2DNanoSpec
Project Nanoscale Vibrational Spectroscopy of Sensitive 2D Molecular Materials
Researcher (PI) Renato ZENOBI
Host Institution (HI) EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Call Details Advanced Grant (AdG), PE4, ERC-2016-ADG
Summary I propose to investigate the nanometer scale organization of delicate 2-dimensional molecular materials using nanoscale vibrational spectroscopy. 2D structures are of great scientific and technological importance, for example as novel materials (graphene, MoS2, WS2, etc.), and in the form of biological membranes and synthetic 2D-polymers. Powerful methods for their analysis and imaging with molecular selectivity and sufficient spatial resolution, however, are lacking. Tip-enhanced Raman spectroscopy (TERS) allows label-free spectroscopic identification of molecular species, with ≈10 nm spatial resolution, and with single molecule sensitivity for strong Raman scatterers. So far, however, TERS is not being carried out in liquids, which is the natural environment for membranes, and its application to poor Raman scatterers such as components of 2D polymers, lipids, or other membrane compounds (proteins, sugars) is difficult. TERS has the potential to overcome the restrictions of other optical/spectroscopic methods to study 2D materials, namely (i) insufficient spatial resolution of diffraction-limited optical methods; (ii) the need for labelling for all methods relying on fluorescence; and (iii) the inability of some methods to work in liquids. I propose to address a number of scientific questions associated with the spatial organization, and the occurrence of defects in sensitive 2D molecular materials. The success of these studies will also rely critically on technical innovations of TERS that notably address the problem of energy dissipation. This will for the first time allow its application to study of complex, delicate 2D molecular systems without photochemical damage.
Summary
I propose to investigate the nanometer scale organization of delicate 2-dimensional molecular materials using nanoscale vibrational spectroscopy. 2D structures are of great scientific and technological importance, for example as novel materials (graphene, MoS2, WS2, etc.), and in the form of biological membranes and synthetic 2D-polymers. Powerful methods for their analysis and imaging with molecular selectivity and sufficient spatial resolution, however, are lacking. Tip-enhanced Raman spectroscopy (TERS) allows label-free spectroscopic identification of molecular species, with ≈10 nm spatial resolution, and with single molecule sensitivity for strong Raman scatterers. So far, however, TERS is not being carried out in liquids, which is the natural environment for membranes, and its application to poor Raman scatterers such as components of 2D polymers, lipids, or other membrane compounds (proteins, sugars) is difficult. TERS has the potential to overcome the restrictions of other optical/spectroscopic methods to study 2D materials, namely (i) insufficient spatial resolution of diffraction-limited optical methods; (ii) the need for labelling for all methods relying on fluorescence; and (iii) the inability of some methods to work in liquids. I propose to address a number of scientific questions associated with the spatial organization, and the occurrence of defects in sensitive 2D molecular materials. The success of these studies will also rely critically on technical innovations of TERS that notably address the problem of energy dissipation. This will for the first time allow its application to study of complex, delicate 2D molecular systems without photochemical damage.
Max ERC Funding
2 311 696 €
Duration
Start date: 2017-09-01, End date: 2022-08-31
Project acronym 2STEPPARKIN
Project A novel two-step model for neurodegeneration in Parkinson’s disease
Researcher (PI) Emi Nagoshi
Host Institution (HI) UNIVERSITE DE GENEVE
Call Details Starting Grant (StG), LS5, ERC-2012-StG_20111109
Summary Parkinson’s disease (PD) is the second most common neurodegenerative disorder primarily caused by the progressive loss of dopaminergic (DA) neurons in the substantia nigra (SN). Despite the advances in gene discovery associated with PD, the knowledge of the PD pathogenesis is largely limited to the involvement of these genes in the generic cell death pathways, and why degeneration is specific to DA neurons and why the degeneration is progressive remain enigmatic. Broad goal of our work is therefore to elucidate the mechanisms underlying specific and progressive DA neuron degeneration in PD. Our new Drosophila model of PD ⎯Fer2 gene loss-of-function mutation⎯ is unusually well suited to address these questions. Fer2 mutants exhibit specific and progressive death of brain DA neurons as well as severe locomotor defects and short life span. Strikingly, the death of DA neuron is initiated in a small cluster of Fer2-expressing DA neurons and subsequently propagates to Fer2-negative DA neurons. We therefore propose a novel two-step model of the neurodegeneration in PD: primary cell death occurs in a specific subset of dopamindegic neurons that are genetically defined, and subsequently the failure of the neuronal connectivity triggers and propagates secondary cell death to remaining DA neurons. In this research, we will test this hypothesis and investigate the underlying molecular mechanisms. This will be the first study to examine circuit-dependency in DA neuron degeneration. Our approach will use a combination of non-biased genomic techniques and candidate-based screening, in addition to the powerful Drosophila genetic toolbox. Furthermore, to test this hypothesis beyond the Drosophila model, we will establish new mouse models of PD that exhibit progressive DA neuron degeneration. Outcome of this research will likely revolutionize the understanding of PD pathogenesis and open an avenue toward the discovery of effective therapy strategies against PD.
Summary
Parkinson’s disease (PD) is the second most common neurodegenerative disorder primarily caused by the progressive loss of dopaminergic (DA) neurons in the substantia nigra (SN). Despite the advances in gene discovery associated with PD, the knowledge of the PD pathogenesis is largely limited to the involvement of these genes in the generic cell death pathways, and why degeneration is specific to DA neurons and why the degeneration is progressive remain enigmatic. Broad goal of our work is therefore to elucidate the mechanisms underlying specific and progressive DA neuron degeneration in PD. Our new Drosophila model of PD ⎯Fer2 gene loss-of-function mutation⎯ is unusually well suited to address these questions. Fer2 mutants exhibit specific and progressive death of brain DA neurons as well as severe locomotor defects and short life span. Strikingly, the death of DA neuron is initiated in a small cluster of Fer2-expressing DA neurons and subsequently propagates to Fer2-negative DA neurons. We therefore propose a novel two-step model of the neurodegeneration in PD: primary cell death occurs in a specific subset of dopamindegic neurons that are genetically defined, and subsequently the failure of the neuronal connectivity triggers and propagates secondary cell death to remaining DA neurons. In this research, we will test this hypothesis and investigate the underlying molecular mechanisms. This will be the first study to examine circuit-dependency in DA neuron degeneration. Our approach will use a combination of non-biased genomic techniques and candidate-based screening, in addition to the powerful Drosophila genetic toolbox. Furthermore, to test this hypothesis beyond the Drosophila model, we will establish new mouse models of PD that exhibit progressive DA neuron degeneration. Outcome of this research will likely revolutionize the understanding of PD pathogenesis and open an avenue toward the discovery of effective therapy strategies against PD.
Max ERC Funding
1 518 960 €
Duration
Start date: 2013-06-01, End date: 2018-05-31
Project acronym AGRISCENTS
Project Scents and sensibility in agriculture: exploiting specificity in herbivore- and pathogen-induced plant volatiles for real-time crop monitoring
Researcher (PI) Theodoor Turlings
Host Institution (HI) UNIVERSITE DE NEUCHATEL
Call Details Advanced Grant (AdG), LS9, ERC-2017-ADG
Summary Plants typically release large quantities of volatiles in response to attack by herbivores or pathogens. I may claim to have contributed to various breakthroughs in this research field, including the discovery that the volatile blends induced by different attackers are astonishingly specific, resulting in characteristic, readily distinguishable odour blends. Using maize as our model plant, I wish to take several leaps forward in our understanding of this signal specificity and use this knowledge to develop sensors for the real-time detection of crop pests and diseases. For this, three interconnected work-packages will aim to:
• Develop chemical analytical techniques and statistical models to decipher the odorous vocabulary of plants, and to create a complete inventory of “odour-prints” for a wide range of herbivore-plant and pathogen-plant combinations, including simultaneous infestations.
• Develop and optimize nano-mechanical sensors for the detection of specific plant volatile mixtures. For this, we will initially adapt a prototype sensor that has been successfully developed for the detection of cancer-related volatiles in human breath.
• Genetically manipulate maize plants to release a unique blend of root-produced volatiles upon herbivory. For this, we will engineer gene cassettes that combine recently identified P450 (CYP) genes from poplar with inducible, root-specific promoters from maize. This will result in maize plants that, in response to pest attack, release easy-to-detect aldoximes and nitriles from their roots.
In short, by investigating and manipulating the specificity of inducible odour blends we will generate the necessary knowhow to develop a novel odour-detection device. The envisioned sensor technology will permit real-time monitoring of the pests and enable farmers to apply crop protection treatments at the right time and in the right place.
Summary
Plants typically release large quantities of volatiles in response to attack by herbivores or pathogens. I may claim to have contributed to various breakthroughs in this research field, including the discovery that the volatile blends induced by different attackers are astonishingly specific, resulting in characteristic, readily distinguishable odour blends. Using maize as our model plant, I wish to take several leaps forward in our understanding of this signal specificity and use this knowledge to develop sensors for the real-time detection of crop pests and diseases. For this, three interconnected work-packages will aim to:
• Develop chemical analytical techniques and statistical models to decipher the odorous vocabulary of plants, and to create a complete inventory of “odour-prints” for a wide range of herbivore-plant and pathogen-plant combinations, including simultaneous infestations.
• Develop and optimize nano-mechanical sensors for the detection of specific plant volatile mixtures. For this, we will initially adapt a prototype sensor that has been successfully developed for the detection of cancer-related volatiles in human breath.
• Genetically manipulate maize plants to release a unique blend of root-produced volatiles upon herbivory. For this, we will engineer gene cassettes that combine recently identified P450 (CYP) genes from poplar with inducible, root-specific promoters from maize. This will result in maize plants that, in response to pest attack, release easy-to-detect aldoximes and nitriles from their roots.
In short, by investigating and manipulating the specificity of inducible odour blends we will generate the necessary knowhow to develop a novel odour-detection device. The envisioned sensor technology will permit real-time monitoring of the pests and enable farmers to apply crop protection treatments at the right time and in the right place.
Max ERC Funding
2 498 086 €
Duration
Start date: 2018-09-01, End date: 2023-08-31
Project acronym ALGILE
Project Foundations of Algebraic and Dynamic Data Management Systems
Researcher (PI) Christoph Koch
Host Institution (HI) ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Call Details Starting Grant (StG), PE6, ERC-2011-StG_20101014
Summary "Contemporary database query languages are ultimately founded on logic and feature an additive operation – usually a form of (multi)set union or disjunction – that is asymmetric in that additions or updates do not always have an inverse. This asymmetry puts a greater part of the machinery of abstract algebra for equation solving outside the reach of databases. However, such equation solving would be a key functionality that problems such as query equivalence testing and data integration could be reduced to: In the current scenario of the presence of an asymmetric additive operation they are undecidable. Moreover, query languages with a symmetric additive operation (i.e., which has an inverse and is thus based on ring theory) would open up databases for a large range of new scientific and mathematical applications.
The goal of the proposed project is to reinvent database management systems with a foundation in abstract algebra and specifically in ring theory. The presence of an additive inverse allows to cleanly define differences between queries. This gives rise to a database analog of differential calculus that leads to radically new incremental and adaptive query evaluation algorithms that substantially outperform the state of the art techniques. These algorithms enable a new class of systems which I call Dynamic Data Management Systems. Such systems can maintain continuously fresh query views at extremely high update rates and have important applications in interactive Large-scale Data Analysis. There is a natural connection between differences and updates, motivating the group theoretic study of updates that will lead to better ways of creating out-of-core data processing algorithms for new storage devices. Basing queries on ring theory leads to a new class of systems, Algebraic Data Management Systems, which herald a convergence of database systems and computer algebra systems."
Summary
"Contemporary database query languages are ultimately founded on logic and feature an additive operation – usually a form of (multi)set union or disjunction – that is asymmetric in that additions or updates do not always have an inverse. This asymmetry puts a greater part of the machinery of abstract algebra for equation solving outside the reach of databases. However, such equation solving would be a key functionality that problems such as query equivalence testing and data integration could be reduced to: In the current scenario of the presence of an asymmetric additive operation they are undecidable. Moreover, query languages with a symmetric additive operation (i.e., which has an inverse and is thus based on ring theory) would open up databases for a large range of new scientific and mathematical applications.
The goal of the proposed project is to reinvent database management systems with a foundation in abstract algebra and specifically in ring theory. The presence of an additive inverse allows to cleanly define differences between queries. This gives rise to a database analog of differential calculus that leads to radically new incremental and adaptive query evaluation algorithms that substantially outperform the state of the art techniques. These algorithms enable a new class of systems which I call Dynamic Data Management Systems. Such systems can maintain continuously fresh query views at extremely high update rates and have important applications in interactive Large-scale Data Analysis. There is a natural connection between differences and updates, motivating the group theoretic study of updates that will lead to better ways of creating out-of-core data processing algorithms for new storage devices. Basing queries on ring theory leads to a new class of systems, Algebraic Data Management Systems, which herald a convergence of database systems and computer algebra systems."
Max ERC Funding
1 480 548 €
Duration
Start date: 2012-01-01, End date: 2016-12-31
Project acronym AlgoRNN
Project Recurrent Neural Networks and Related Machines That Learn Algorithms
Researcher (PI) Juergen Schmidhuber
Host Institution (HI) UNIVERSITA DELLA SVIZZERA ITALIANA
Call Details Advanced Grant (AdG), PE6, ERC-2016-ADG
Summary Recurrent neural networks (RNNs) are general parallel-sequential computers. Some learn their programs or weights. Our supervised Long Short-Term Memory (LSTM) RNNs were the first to win pattern recognition contests, and recently enabled best known results in speech and handwriting recognition, machine translation, etc. They are now available to billions of users through the world's most valuable public companies including Google and Apple. Nevertheless, in lots of real-world tasks RNNs do not yet live up to their full potential. Although universal in theory, in practice they fail to learn important types of algorithms. This ERC project will go far beyond today's best RNNs through novel RNN-like systems that address some of the biggest open RNN problems and hottest RNN research topics: (1) How can RNNs learn to control (through internal spotlights of attention) separate large short-memory structures such as sub-networks with fast weights, to improve performance on many natural short-term memory-intensive tasks which are currently hard to learn by RNNs, such as answering detailed questions on recently observed videos? (2) How can such RNN-like systems metalearn entire learning algorithms that outperform the original learning algorithms? (3) How to achieve efficient transfer learning from one RNN-learned set of problem-solving programs to new RNN programs solving new tasks? In other words, how can one RNN-like system actively learn to exploit algorithmic information contained in the programs running on another? We will test our systems existing benchmarks, and create new, more challenging multi-task benchmarks. This will be supported by a rather cheap, GPU-based mini-brain for implementing large RNNs.
Summary
Recurrent neural networks (RNNs) are general parallel-sequential computers. Some learn their programs or weights. Our supervised Long Short-Term Memory (LSTM) RNNs were the first to win pattern recognition contests, and recently enabled best known results in speech and handwriting recognition, machine translation, etc. They are now available to billions of users through the world's most valuable public companies including Google and Apple. Nevertheless, in lots of real-world tasks RNNs do not yet live up to their full potential. Although universal in theory, in practice they fail to learn important types of algorithms. This ERC project will go far beyond today's best RNNs through novel RNN-like systems that address some of the biggest open RNN problems and hottest RNN research topics: (1) How can RNNs learn to control (through internal spotlights of attention) separate large short-memory structures such as sub-networks with fast weights, to improve performance on many natural short-term memory-intensive tasks which are currently hard to learn by RNNs, such as answering detailed questions on recently observed videos? (2) How can such RNN-like systems metalearn entire learning algorithms that outperform the original learning algorithms? (3) How to achieve efficient transfer learning from one RNN-learned set of problem-solving programs to new RNN programs solving new tasks? In other words, how can one RNN-like system actively learn to exploit algorithmic information contained in the programs running on another? We will test our systems existing benchmarks, and create new, more challenging multi-task benchmarks. This will be supported by a rather cheap, GPU-based mini-brain for implementing large RNNs.
Max ERC Funding
2 500 000 €
Duration
Start date: 2017-10-01, End date: 2022-09-30
Project acronym AMADEUS
Project Advancing CO2 Capture Materials by Atomic Scale Design: the Quest for Understanding
Researcher (PI) Christoph Rüdiger MÜLLER
Host Institution (HI) EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Call Details Consolidator Grant (CoG), PE8, ERC-2018-COG
Summary Carbon dioxide capture and storage is a technology to mitigate climate change by removing CO2 from flue gas streams or the atmosphere and storing it in geological formations. While CO2 removal from natural gas by amine scrubbing is implemented on the large scale, the cost of such process is currently prohibitively expensive. Inexpensive alkali earth metal oxides (MgO and CaO) feature high theoretical CO2 uptakes, but suffer from poor cyclic stability and slow kinetics. Yet, the key objective of recent research on alkali earth metal oxide based CO2 sorbents has been the processing of inexpensive, naturally occurring CO2 sorbents, notably limestone and dolomite, to stabilize their modest CO2 uptake and to establish re-activation methods through engineering approaches. While this research demonstrated a landmark Megawatt (MW) scale viability of the process, our fundamental understanding of the underlying CO2 capture, regeneration and deactivation pathways did not improve. The latter knowledge is, however, vital for the rational design of improved, yet practical CaO and MgO sorbents. Hence this proposal is concerned with obtaining an understanding of the underlying mechanisms that control the ability of an alkali metal oxide to capture a large quantity of CO2 with a high rate, to regenerate and to operate with high cyclic stability. Achieving these aims relies on the ability to fabricate model structures and to characterize in great detail their surface chemistry, morphology, chemical composition and changes therein under reactive conditions. This makes the development of operando and in situ characterization tools an essential prerequisite. Advances in these areas shall allow achieving the overall goal of this project, viz. to formulate a roadmap to fabricate improved CO2 sorbents through their precisely engineered structure, composition and morphology.
Summary
Carbon dioxide capture and storage is a technology to mitigate climate change by removing CO2 from flue gas streams or the atmosphere and storing it in geological formations. While CO2 removal from natural gas by amine scrubbing is implemented on the large scale, the cost of such process is currently prohibitively expensive. Inexpensive alkali earth metal oxides (MgO and CaO) feature high theoretical CO2 uptakes, but suffer from poor cyclic stability and slow kinetics. Yet, the key objective of recent research on alkali earth metal oxide based CO2 sorbents has been the processing of inexpensive, naturally occurring CO2 sorbents, notably limestone and dolomite, to stabilize their modest CO2 uptake and to establish re-activation methods through engineering approaches. While this research demonstrated a landmark Megawatt (MW) scale viability of the process, our fundamental understanding of the underlying CO2 capture, regeneration and deactivation pathways did not improve. The latter knowledge is, however, vital for the rational design of improved, yet practical CaO and MgO sorbents. Hence this proposal is concerned with obtaining an understanding of the underlying mechanisms that control the ability of an alkali metal oxide to capture a large quantity of CO2 with a high rate, to regenerate and to operate with high cyclic stability. Achieving these aims relies on the ability to fabricate model structures and to characterize in great detail their surface chemistry, morphology, chemical composition and changes therein under reactive conditions. This makes the development of operando and in situ characterization tools an essential prerequisite. Advances in these areas shall allow achieving the overall goal of this project, viz. to formulate a roadmap to fabricate improved CO2 sorbents through their precisely engineered structure, composition and morphology.
Max ERC Funding
1 994 900 €
Duration
Start date: 2019-06-01, End date: 2024-05-31
Project acronym AMSEL
Project Atomic Force Microscopy for Molecular Structure Elucidation
Researcher (PI) Leo Gross
Host Institution (HI) IBM RESEARCH GMBH
Call Details Consolidator Grant (CoG), PE4, ERC-2015-CoG
Summary Molecular structure elucidation is of great importance in synthetic chemistry, pharmacy, life sciences, energy and environmental sciences, and technology applications. To date structure elucidation by atomic force microscopy (AFM) has been demonstrated for a few, small and mainly planar molecules. In this project high-risk, high-impact scientific questions will be solved using structure elucidation with the AFM employing a novel tool and novel methodologies.
A combined low-temperature scanning tunneling microscope/atomic force microscope (LT-STM/AFM) with high throughput and in situ electrospray deposition method will be developed. Chemical resolution will be achieved by novel measurement techniques, in particular the usage of different and novel tip functionalizations and combination with Kelvin probe force microscopy. Elements will be identified using substructure recognition provided by a database that will be erected and by refined theory and simulations.
The developed tools and techniques will be applied to molecules of increasing fragility, complexity, size, and three-dimensionality. In particular samples that are challenging to characterize with conventional methods will be studied. Complex molecular mixtures will be investigated molecule-by-molecule taking advantage of the single-molecule sensitivity. The absolute stereochemistry of molecules will be determined, resolving molecules with multiple stereocenters. The operation of single molecular machines as nanocars and molecular gears will be investigated. Reactive intermediates generated with atomic manipulation will be characterized and their on-surface reactivity will be studied by AFM.
Summary
Molecular structure elucidation is of great importance in synthetic chemistry, pharmacy, life sciences, energy and environmental sciences, and technology applications. To date structure elucidation by atomic force microscopy (AFM) has been demonstrated for a few, small and mainly planar molecules. In this project high-risk, high-impact scientific questions will be solved using structure elucidation with the AFM employing a novel tool and novel methodologies.
A combined low-temperature scanning tunneling microscope/atomic force microscope (LT-STM/AFM) with high throughput and in situ electrospray deposition method will be developed. Chemical resolution will be achieved by novel measurement techniques, in particular the usage of different and novel tip functionalizations and combination with Kelvin probe force microscopy. Elements will be identified using substructure recognition provided by a database that will be erected and by refined theory and simulations.
The developed tools and techniques will be applied to molecules of increasing fragility, complexity, size, and three-dimensionality. In particular samples that are challenging to characterize with conventional methods will be studied. Complex molecular mixtures will be investigated molecule-by-molecule taking advantage of the single-molecule sensitivity. The absolute stereochemistry of molecules will be determined, resolving molecules with multiple stereocenters. The operation of single molecular machines as nanocars and molecular gears will be investigated. Reactive intermediates generated with atomic manipulation will be characterized and their on-surface reactivity will be studied by AFM.
Max ERC Funding
2 000 000 €
Duration
Start date: 2016-06-01, End date: 2021-05-31
Project acronym Amygdala Circuits
Project Amygdala Circuits for Appetitive Conditioning
Researcher (PI) Andreas Luthi
Host Institution (HI) FRIEDRICH MIESCHER INSTITUTE FOR BIOMEDICAL RESEARCH FONDATION
Call Details Advanced Grant (AdG), LS5, ERC-2014-ADG
Summary The project outlined here addresses the fundamental question how the brain encodes and controls behavior. While we have a reasonable understanding of the role of entire brain areas in such processes, and of mechanisms at the molecular and synaptic levels, there is a big gap in our knowledge of how behavior is controlled at the level of defined neuronal circuits.
In natural environments, chances for survival depend on learning about possible aversive and appetitive outcomes and on the appropriate behavioral responses. Most studies addressing the underlying mechanisms at the level of neuronal circuits have focused on aversive learning, such as in Pavlovian fear conditioning. Understanding how activity in defined neuronal circuits mediates appetitive learning, as well as how these circuitries are shared and interact with aversive learning circuits, is a central question in the neuroscience of learning and memory and the focus of this grant application.
Using a multidisciplinary approach in mice, combining behavioral, in vivo and in vitro electrophysiological, imaging, optogenetic and state-of-the-art viral circuit tracing techniques, we aim at dissecting the neuronal circuitry of appetitive Pavlovian conditioning with a focus on the amygdala, a key brain region important for both aversive and appetitive learning. Ultimately, elucidating these mechanisms at the level of defined neurons and circuits is fundamental not only for an understanding of memory processes in the brain in general, but also to inform a mechanistic approach to psychiatric conditions associated with amygdala dysfunction and dysregulated emotional responses including anxiety and mood disorders.
Summary
The project outlined here addresses the fundamental question how the brain encodes and controls behavior. While we have a reasonable understanding of the role of entire brain areas in such processes, and of mechanisms at the molecular and synaptic levels, there is a big gap in our knowledge of how behavior is controlled at the level of defined neuronal circuits.
In natural environments, chances for survival depend on learning about possible aversive and appetitive outcomes and on the appropriate behavioral responses. Most studies addressing the underlying mechanisms at the level of neuronal circuits have focused on aversive learning, such as in Pavlovian fear conditioning. Understanding how activity in defined neuronal circuits mediates appetitive learning, as well as how these circuitries are shared and interact with aversive learning circuits, is a central question in the neuroscience of learning and memory and the focus of this grant application.
Using a multidisciplinary approach in mice, combining behavioral, in vivo and in vitro electrophysiological, imaging, optogenetic and state-of-the-art viral circuit tracing techniques, we aim at dissecting the neuronal circuitry of appetitive Pavlovian conditioning with a focus on the amygdala, a key brain region important for both aversive and appetitive learning. Ultimately, elucidating these mechanisms at the level of defined neurons and circuits is fundamental not only for an understanding of memory processes in the brain in general, but also to inform a mechanistic approach to psychiatric conditions associated with amygdala dysfunction and dysregulated emotional responses including anxiety and mood disorders.
Max ERC Funding
2 497 200 €
Duration
Start date: 2016-01-01, End date: 2020-12-31
Project acronym AOC
Project Adversary-Oriented Computing
Researcher (PI) Rachid Guerraoui
Host Institution (HI) ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Call Details Advanced Grant (AdG), PE6, ERC-2013-ADG
Summary "Recent technological evolutions, including the cloud, the multicore, the social and the mobiles ones, are turning computing ubiquitously distributed. Yet, building high-assurance distributed programs is notoriously challenging. One of the main reasons is that these systems usually seek to achieve several goals at the same time. In short, they need to be efficient, responding effectively in various average-case conditions, as well as reliable, behaving correctly in severe, worst-case conditions. As a consequence, they typically intermingle different strategies: each to cope with some specific condition, e.g., with or without node failures, message losses, time-outs, contention, cache misses,
over-sizing, malicious attacks, etc. The resulting programs end up hard to design, prove, verify, implement, test and debug. Not surprisingly, there are anecdotal evidences of the fragility of the most celebrated distributed systems.
The goal of this project is to contribute to building high-assurance distributed programs by introducing a new dimension for separating and isolating their concerns, as well as a new scheme for composing and reusing them in a modular manner. In short, the project will explore the inherent power and limitations of a novel paradigm, Adversary-Oriented Computing (AOC). Sub-programs, each implementing a specific strategy to cope with a given adversary, modelling a specific working condition, are designed, proved, verified, implemented, tested and debugged independently. They are then composed, possibly dynamically, as black-boxes within the same global program. The AOC project is ambitious and it seeks to fundamentally revisit the way distributed algorithms are designed and distributed systems are implemented. The gain expected in comparison with today's approaches is substantial, and I believe it will be proportional to the degree of difficulty of the distributed problem at hand."
Summary
"Recent technological evolutions, including the cloud, the multicore, the social and the mobiles ones, are turning computing ubiquitously distributed. Yet, building high-assurance distributed programs is notoriously challenging. One of the main reasons is that these systems usually seek to achieve several goals at the same time. In short, they need to be efficient, responding effectively in various average-case conditions, as well as reliable, behaving correctly in severe, worst-case conditions. As a consequence, they typically intermingle different strategies: each to cope with some specific condition, e.g., with or without node failures, message losses, time-outs, contention, cache misses,
over-sizing, malicious attacks, etc. The resulting programs end up hard to design, prove, verify, implement, test and debug. Not surprisingly, there are anecdotal evidences of the fragility of the most celebrated distributed systems.
The goal of this project is to contribute to building high-assurance distributed programs by introducing a new dimension for separating and isolating their concerns, as well as a new scheme for composing and reusing them in a modular manner. In short, the project will explore the inherent power and limitations of a novel paradigm, Adversary-Oriented Computing (AOC). Sub-programs, each implementing a specific strategy to cope with a given adversary, modelling a specific working condition, are designed, proved, verified, implemented, tested and debugged independently. They are then composed, possibly dynamically, as black-boxes within the same global program. The AOC project is ambitious and it seeks to fundamentally revisit the way distributed algorithms are designed and distributed systems are implemented. The gain expected in comparison with today's approaches is substantial, and I believe it will be proportional to the degree of difficulty of the distributed problem at hand."
Max ERC Funding
2 147 012 €
Duration
Start date: 2014-06-01, End date: 2019-05-31
Project acronym astromnesis
Project The language of astrocytes: multilevel analysis to understand astrocyte communication and its role in memory-related brain operations and in cognitive behavior
Researcher (PI) Andrea Volterra
Host Institution (HI) UNIVERSITE DE LAUSANNE
Call Details Advanced Grant (AdG), LS5, ERC-2013-ADG
Summary In the 90s, two landmark observations brought to a paradigm shift about the role of astrocytes in brain function: 1) astrocytes respond to signals coming from other cells with transient Ca2+ elevations; 2) Ca2+ transients in astrocytes trigger release of neuroactive and vasoactive agents. Since then, many modulatory astrocytic actions and mechanisms were described, forming a complex - partly contradictory - picture, in which the exact roles and modes of astrocyte action remain ill defined. Our project wants to bring light into the “language of astrocytes”, i.e. into how they communicate with neurons and, ultimately, address their role in brain computations and cognitive behavior. To this end we will perform 4 complementary levels of analysis using highly innovative methodologies in order to obtain unprecedented results. We will study: 1) the subcellular organization of astrocytes underlying local microdomain communications by use of correlative light-electron microscopy; 2) the way individual astrocytes integrate inputs and control synaptic ensembles using 3D two-photon imaging, genetically-encoded Ca2+ indicators, optogenetics and electrophysiology; 3) the contribution of astrocyte ensembles to behavior-relevant circuit operations using miniaturized microscopes capturing neuronal/astrocytic population dynamics in freely-moving mice during memory tests; 4) the contribution of astrocytic signalling mechanisms to cognitive behavior using a set of new mouse lines with conditional, astrocyte-specific genetic modification of signalling pathways. We expect that this combination of groundbreaking ideas, innovative technologies and multilevel analysis makes our project highly attractive to the neuroscience community at large, bridging aspects of molecular, cellular, systems and behavioral neuroscience, with the goal of leading from a provocative hypothesis to the conclusive demonstration of whether and how “the language of astrocytes” participates in memory and cognition.
Summary
In the 90s, two landmark observations brought to a paradigm shift about the role of astrocytes in brain function: 1) astrocytes respond to signals coming from other cells with transient Ca2+ elevations; 2) Ca2+ transients in astrocytes trigger release of neuroactive and vasoactive agents. Since then, many modulatory astrocytic actions and mechanisms were described, forming a complex - partly contradictory - picture, in which the exact roles and modes of astrocyte action remain ill defined. Our project wants to bring light into the “language of astrocytes”, i.e. into how they communicate with neurons and, ultimately, address their role in brain computations and cognitive behavior. To this end we will perform 4 complementary levels of analysis using highly innovative methodologies in order to obtain unprecedented results. We will study: 1) the subcellular organization of astrocytes underlying local microdomain communications by use of correlative light-electron microscopy; 2) the way individual astrocytes integrate inputs and control synaptic ensembles using 3D two-photon imaging, genetically-encoded Ca2+ indicators, optogenetics and electrophysiology; 3) the contribution of astrocyte ensembles to behavior-relevant circuit operations using miniaturized microscopes capturing neuronal/astrocytic population dynamics in freely-moving mice during memory tests; 4) the contribution of astrocytic signalling mechanisms to cognitive behavior using a set of new mouse lines with conditional, astrocyte-specific genetic modification of signalling pathways. We expect that this combination of groundbreaking ideas, innovative technologies and multilevel analysis makes our project highly attractive to the neuroscience community at large, bridging aspects of molecular, cellular, systems and behavioral neuroscience, with the goal of leading from a provocative hypothesis to the conclusive demonstration of whether and how “the language of astrocytes” participates in memory and cognition.
Max ERC Funding
2 513 896 €
Duration
Start date: 2014-02-01, End date: 2019-01-31
