Project acronym activeFly
Project Circuit mechanisms of self-movement estimation during walking
Researcher (PI) M Eugenia CHIAPPE
Host Institution (HI) FUNDACAO D. ANNA SOMMER CHAMPALIMAUD E DR. CARLOS MONTEZ CHAMPALIMAUD
Call Details Starting Grant (StG), LS5, ERC-2017-STG
Summary The brain evolves, develops, and operates in the context of animal movements. As a consequence, fundamental brain functions such as spatial perception and motor control critically depend on the precise knowledge of the ongoing body motion. An accurate internal estimate of self-movement is thought to emerge from sensorimotor integration; nonetheless, which circuits perform this internal estimation, and exactly how motor-sensory coordination is implemented within these circuits are basic questions that remain to be poorly understood. There is growing evidence suggesting that, during locomotion, motor-related and visual signals interact at early stages of visual processing. In mammals, however, it is not clear what the function of this interaction is. Recently, we have shown that a population of Drosophila optic-flow processing neurons —neurons that are sensitive to self-generated visual flow, receives convergent visual and walking-related signals to form a faithful representation of the fly’s walking movements. Leveraging from these results, and combining quantitative analysis of behavior with physiology, optogenetics, and modelling, we propose to investigate circuit mechanisms of self-movement estimation during walking. We will:1) use cell specific manipulations to identify what cells are necessary to generate the motor-related activity in the population of visual neurons, 2) record from the identified neurons and correlate their activity with specific locomotor parameters, and 3) perturb the activity of different cell-types within the identified circuits to test their role in the dynamics of the visual neurons, and on the fly’s walking behavior. These experiments will establish unprecedented causal relationships among neural activity, the formation of an internal representation, and locomotor control. The identified sensorimotor principles will establish a framework that can be tested in other scenarios or animal systems with implications both in health and disease.
Summary
The brain evolves, develops, and operates in the context of animal movements. As a consequence, fundamental brain functions such as spatial perception and motor control critically depend on the precise knowledge of the ongoing body motion. An accurate internal estimate of self-movement is thought to emerge from sensorimotor integration; nonetheless, which circuits perform this internal estimation, and exactly how motor-sensory coordination is implemented within these circuits are basic questions that remain to be poorly understood. There is growing evidence suggesting that, during locomotion, motor-related and visual signals interact at early stages of visual processing. In mammals, however, it is not clear what the function of this interaction is. Recently, we have shown that a population of Drosophila optic-flow processing neurons —neurons that are sensitive to self-generated visual flow, receives convergent visual and walking-related signals to form a faithful representation of the fly’s walking movements. Leveraging from these results, and combining quantitative analysis of behavior with physiology, optogenetics, and modelling, we propose to investigate circuit mechanisms of self-movement estimation during walking. We will:1) use cell specific manipulations to identify what cells are necessary to generate the motor-related activity in the population of visual neurons, 2) record from the identified neurons and correlate their activity with specific locomotor parameters, and 3) perturb the activity of different cell-types within the identified circuits to test their role in the dynamics of the visual neurons, and on the fly’s walking behavior. These experiments will establish unprecedented causal relationships among neural activity, the formation of an internal representation, and locomotor control. The identified sensorimotor principles will establish a framework that can be tested in other scenarios or animal systems with implications both in health and disease.
Max ERC Funding
1 500 000 €
Duration
Start date: 2017-11-01, End date: 2022-10-31
Project acronym DeepSPIN
Project Deep Learning for Structured Prediction in Natural Language Processing
Researcher (PI) André Filipe TORRES MARTINS
Host Institution (HI) INSTITUTO DE TELECOMUNICACOES
Call Details Starting Grant (StG), PE6, ERC-2017-STG
Summary Deep learning is revolutionizing the field of Natural Language Processing (NLP), with breakthroughs in machine translation, speech recognition, and question answering. New language interfaces (digital assistants, messenger apps, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines.
From a machine learning perspective, many problems in NLP can be characterized as structured prediction: they involve predicting structurally rich and interdependent output variables. In spite of this, current neural NLP systems ignore the structural complexity of human language, relying on simplistic and error-prone greedy search procedures. This leads to serious mistakes in machine translation, such as words being dropped or named entities mistranslated. More broadly, neural networks are missing the key structural mechanisms for solving complex real-world tasks requiring deep reasoning.
This project attacks these fundamental problems by bringing together deep learning and structured prediction, with a highly disruptive and cross-disciplinary approach. First, I will endow neural networks with a "planning mechanism" to guide structural search, letting decoders learn the optimal order by which they should operate. This makes a bridge with reinforcement learning and combinatorial optimization. Second, I will develop new ways of automatically inducing latent structure inside the network, making it more expressive, scalable and interpretable. Synergies with probabilistic inference and sparse modeling techniques will be exploited. To complement these two innovations, I will investigate new ways of incorporating weak supervision to reduce the need for labeled data.
Three highly challenging applications will serve as testbeds: machine translation, quality estimation, and dependency parsing. To maximize technological impact, a collaboration is planned with a start-up company in the crowd-sourcing translation industry.
Summary
Deep learning is revolutionizing the field of Natural Language Processing (NLP), with breakthroughs in machine translation, speech recognition, and question answering. New language interfaces (digital assistants, messenger apps, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines.
From a machine learning perspective, many problems in NLP can be characterized as structured prediction: they involve predicting structurally rich and interdependent output variables. In spite of this, current neural NLP systems ignore the structural complexity of human language, relying on simplistic and error-prone greedy search procedures. This leads to serious mistakes in machine translation, such as words being dropped or named entities mistranslated. More broadly, neural networks are missing the key structural mechanisms for solving complex real-world tasks requiring deep reasoning.
This project attacks these fundamental problems by bringing together deep learning and structured prediction, with a highly disruptive and cross-disciplinary approach. First, I will endow neural networks with a "planning mechanism" to guide structural search, letting decoders learn the optimal order by which they should operate. This makes a bridge with reinforcement learning and combinatorial optimization. Second, I will develop new ways of automatically inducing latent structure inside the network, making it more expressive, scalable and interpretable. Synergies with probabilistic inference and sparse modeling techniques will be exploited. To complement these two innovations, I will investigate new ways of incorporating weak supervision to reduce the need for labeled data.
Three highly challenging applications will serve as testbeds: machine translation, quality estimation, and dependency parsing. To maximize technological impact, a collaboration is planned with a start-up company in the crowd-sourcing translation industry.
Max ERC Funding
1 436 000 €
Duration
Start date: 2018-02-01, End date: 2023-01-31
Project acronym FattyCyanos
Project Fatty acid incorporation and modification in cyanobacterial natural products
Researcher (PI) Pedro LEÃO
Host Institution (HI) CIIMAR - Centro Interdisciplinar de Investigação Marinha e Ambiental
Call Details Starting Grant (StG), PE5, ERC-2017-STG
Summary Known, but mostly novel natural products (NPs) are in high demand – these are used in drugs, cosmetics and agrochemicals and serve also as research tools to probe biological systems. NP structures inspire chemists to develop new syntheses, and NP biosynthetic enzymes add to the metabolic engineer’s toolbox. The advent of next generation DNA-sequencing has revealed a vastly rich pool of NP biosynthetic gene clusters (BGCs) among bacterial genomes, most of which with no corresponding NP. Hence, opportunities abound for the discovery of new chemistry and enzymology that has the potential to push the boundaries of chemical space and enzymatic reactivity. Still, we cannot reliably predict chemistry from BGCs with unusual organization or encoding unknown functionalities, and, for molecules of unorthodox architecture, it is difficult to anticipate how their BGCs are organized. It is the valuable, truly novel chemistry and biochemistry that lies on these unexplored connections, that we aim to reveal with this proposal. To achieve it, we will work with a chemically-talented group of organisms – cyanobacteria, and with a specific structural class – fatty acids (FAs) – that is metabolized in a quite peculiar fashion by these organisms, paving the way for NP and enzyme discovery. On one hand, we will exploit the unique FA metabolism of cyanobacteria to develop a feeding strategy that will quickly reveal unprecedented FA-incorporating NPs. On the other, we will scrutinize the intriguing biosynthesis of three unique classes of metabolites that we have isolated recently and that incorporate and modify FA-moieties. We will find the BGCs for these compounds and dissect the functionality involved in such puzzling modifications to uncover important underlying enzymatic chemistry. This proposal is a blend of discovery- and hypothesis-driven research at the NP chemistry/biosynthesis interface that draws on the experience of the PI’s work on different aspects of cyanobacterial NPs.
Summary
Known, but mostly novel natural products (NPs) are in high demand – these are used in drugs, cosmetics and agrochemicals and serve also as research tools to probe biological systems. NP structures inspire chemists to develop new syntheses, and NP biosynthetic enzymes add to the metabolic engineer’s toolbox. The advent of next generation DNA-sequencing has revealed a vastly rich pool of NP biosynthetic gene clusters (BGCs) among bacterial genomes, most of which with no corresponding NP. Hence, opportunities abound for the discovery of new chemistry and enzymology that has the potential to push the boundaries of chemical space and enzymatic reactivity. Still, we cannot reliably predict chemistry from BGCs with unusual organization or encoding unknown functionalities, and, for molecules of unorthodox architecture, it is difficult to anticipate how their BGCs are organized. It is the valuable, truly novel chemistry and biochemistry that lies on these unexplored connections, that we aim to reveal with this proposal. To achieve it, we will work with a chemically-talented group of organisms – cyanobacteria, and with a specific structural class – fatty acids (FAs) – that is metabolized in a quite peculiar fashion by these organisms, paving the way for NP and enzyme discovery. On one hand, we will exploit the unique FA metabolism of cyanobacteria to develop a feeding strategy that will quickly reveal unprecedented FA-incorporating NPs. On the other, we will scrutinize the intriguing biosynthesis of three unique classes of metabolites that we have isolated recently and that incorporate and modify FA-moieties. We will find the BGCs for these compounds and dissect the functionality involved in such puzzling modifications to uncover important underlying enzymatic chemistry. This proposal is a blend of discovery- and hypothesis-driven research at the NP chemistry/biosynthesis interface that draws on the experience of the PI’s work on different aspects of cyanobacterial NPs.
Max ERC Funding
1 462 938 €
Duration
Start date: 2018-01-01, End date: 2022-12-31
Project acronym IMMOCAP
Project 'If immortality unveil…'– development of the novel types of energy storage systems with excellent long-term performance
Researcher (PI) Krzysztof FIC
Host Institution (HI) POLITECHNIKA POZNANSKA
Call Details Starting Grant (StG), PE8, ERC-2017-STG
Summary The major goal of the project is to develop a novel type of an electrochemical capacitor with high specific power (up to 5 kW/kg) and energy (up to 20 Wh/kg) preserved along at least 50 000 cycles. Thus, completion of the project will result in remarkable enhancement of specific energy, power and life time of modern electrochemical capacitors. Advanced electrochemical testing (galvanostatic cycling with constant power loads, electrochemical impedance spectroscopy, accelerated aging and kinetic tests) will be accompanied by materials design and detailed characterization. Moreover, the project aims at the implementation of novel concepts of the electrolytes and designing of new operando technique for capacitor characterization. All these efforts aim at the development of sustainable and efficient energy conversion and storage system.
Summary
The major goal of the project is to develop a novel type of an electrochemical capacitor with high specific power (up to 5 kW/kg) and energy (up to 20 Wh/kg) preserved along at least 50 000 cycles. Thus, completion of the project will result in remarkable enhancement of specific energy, power and life time of modern electrochemical capacitors. Advanced electrochemical testing (galvanostatic cycling with constant power loads, electrochemical impedance spectroscopy, accelerated aging and kinetic tests) will be accompanied by materials design and detailed characterization. Moreover, the project aims at the implementation of novel concepts of the electrolytes and designing of new operando technique for capacitor characterization. All these efforts aim at the development of sustainable and efficient energy conversion and storage system.
Max ERC Funding
1 385 000 €
Duration
Start date: 2017-10-01, End date: 2022-09-30
Project acronym StemCellHabitat
Project Metabolic and Timed Control of Stem Cell Fate in the Developing Animal
Researcher (PI) Catarina DE CERTIMA FERNANDES HOMEM
Host Institution (HI) UNIVERSIDADE NOVA DE LISBOA
Call Details Starting Grant (StG), LS3, ERC-2017-STG
Summary Stem cell (SC) proliferation during development requires tight spatial and temporal regulation to ensure correct cell number and right cell types are formed at the proper positions. Currently very little is known about how SCs are regulated during development. Specifically, it is unclear how SC waves of proliferation are regulated and how the fate of their progeny changes during development. In addition, it has recently become evident that metabolism provides additional complexity in cell fate regulation, highlighting the need for integrating metabolic information across physiological levels.
This project will answer the question of how the combination of metabolic state and temporal cues (animal developmental stage) regulate SC fate. I will use Drosophila melanogaster, an animal complex enough to be similar to higher eukaryotes and yet simple enough to dissect the mechanistic details of cell regulation and its impact on the organism. Drosophila neural stem cells, the neuroblasts (NB), are a fantastic model of temporally and metabolically regulated cells. NB lineage fate changes with time, directing the generation of a stereotypical set of neurons, after which they disappear. I have previously found that metabolism is an important regulator of NB cell cycle exit, which occurs in response to an increase in levels of oxidative phosphorylation.
Using a multidisciplinary approach combining genetics, cell type/age sorting, multi-omics analysis, fixed and 3D-live NB imaging and metabolite dynamics, I propose an integrative approach to investigate how NBs are regulated in the developing animal. First I will dissect the mechanisms by which metabolism regulates NB fate. Second, I will investigate how metabolism contributes to NB unlimited proliferation and brain tumors. Finally, we will address how temporal transcription factors and hormones dynamically affect cell fate decisions during development.
Summary
Stem cell (SC) proliferation during development requires tight spatial and temporal regulation to ensure correct cell number and right cell types are formed at the proper positions. Currently very little is known about how SCs are regulated during development. Specifically, it is unclear how SC waves of proliferation are regulated and how the fate of their progeny changes during development. In addition, it has recently become evident that metabolism provides additional complexity in cell fate regulation, highlighting the need for integrating metabolic information across physiological levels.
This project will answer the question of how the combination of metabolic state and temporal cues (animal developmental stage) regulate SC fate. I will use Drosophila melanogaster, an animal complex enough to be similar to higher eukaryotes and yet simple enough to dissect the mechanistic details of cell regulation and its impact on the organism. Drosophila neural stem cells, the neuroblasts (NB), are a fantastic model of temporally and metabolically regulated cells. NB lineage fate changes with time, directing the generation of a stereotypical set of neurons, after which they disappear. I have previously found that metabolism is an important regulator of NB cell cycle exit, which occurs in response to an increase in levels of oxidative phosphorylation.
Using a multidisciplinary approach combining genetics, cell type/age sorting, multi-omics analysis, fixed and 3D-live NB imaging and metabolite dynamics, I propose an integrative approach to investigate how NBs are regulated in the developing animal. First I will dissect the mechanisms by which metabolism regulates NB fate. Second, I will investigate how metabolism contributes to NB unlimited proliferation and brain tumors. Finally, we will address how temporal transcription factors and hormones dynamically affect cell fate decisions during development.
Max ERC Funding
1 697 493 €
Duration
Start date: 2018-02-01, End date: 2023-01-31
