ERC FUNDED PROJECTS

The development of scientific models suffers from two related problems: ever-growing number of experimental results and scientists’ cognitive limitations (including cognitive biases). This multidisciplinary project (psychology, computer modelling, computer science and cognitive neuroscience) addresses these problems by developing a novel methodology for generating scientific models automatically. The methodology is general and can be applied to any science where experimental data are available. The method treats models as computer programs and evolves a population of models using genetic programming. The extent to which the models fit the empirical data is used as a fitness function. The best models–potentially modified by cross-over and mutation–are selected for the next generation. Pilot simulations have established the validity of the methodology with simple experiments. To demonstrate that the methodology is sound, can be used with complex datasets and can be generalised across sciences, four related strands of research are planned. First, ‘Building New Tools’ develops the methodology and creates techniques to understand and compare the evolved models. Second, ‘Explaining Human Data’ uses the methodology to explain a wide range of data on human cognition. This will be done in two steps: (a) data without learning (working memory and attention); and (b) data with learning (categorisation, implicit learning and explicit learning). Third, ‘Explaining Animal Data’ develops models to account for various aspects of animal behaviour, focusing on conditioning and categorisation. Finally, ‘Explaining Neuroscience Data’ extends the methodology to account for data combining information about cognitive and brain processes. This project explores virgin territory and thus opens up a new field of research. It combines insights from experimental psychology, cognitive modelling, cognitive neuroscience and computer science, disciplines in which the PI has strong track record.

2 182 339 €

Start date: 2019-11-01, End date: 2024-10-31

Quantum simulators (QS) are experimental systems that allow mimic hard to simulate models of condensed matter, high energy physics and beyond. QS have various platforms: from ultracold atoms and ions to superconducting qubits. They constitute the important pillar of quantum technologies (QT), and promise future applications in chemistry, material science and optimization problems. Over the last decade, QS were particularly successful in mimicking topological effects in physics (TEP) and in developing accurate quantum validation/certification (QVC) methods. NOQIA is a theory project, aimed at introducing the established field of QS+TEP+QVC into two novel areas: physics of ultrafast phenomena and attoscience (AS) on one side, and quantum machine learning (ML) and neural networks (NN) on the other. This will open up new horizons/opportunities for research both in AS and in ML/NN. For instance, in AS we will address the question if intense laser physics may serve as a tool to detect topological effects in solid state and strongly correlated systems. We will study response of matter to laser pulses carrying topological signatures, to determine if they can induce topological effects in targets. We will design/analyze QS using trapped atoms to understand and detect TEP in the AS. On the ML/NN side, we will apply classical ML to analyze, design and control QS for topological systems, in order to understand and optimize them. Conversely, we will transfer many-body techniques to ML in order to analyze and possibly improve performance of classical machine learning. We will design and analyze quantum neural network devices that will employ topology in order to achieve robust quantum memory or information processing. We will design/study attractor neural networks with topological stationary states, or feed-forward networks with topological Floquet and time-crystal states. Both in AS and ML/NN, NOQIA will rely on quantum validation and certification protocols and techniques.

2 164 244 €

Start date: 2019-07-01, End date: 2024-06-30

Sleep is vital and universal, but its biological function remains unknown. This project will seek to understand why we need to sleep by studying how the brain responds to sleep loss. My previous work in Drosophila showed that rising sleep pressure activates two dozen sleep-inducing neurons in the dorsal fan-shaped body (dFB) of the central complex. Sleep need is encoded in the electrical excitability of these neurons, which fluctuates because two potassium conductances, voltage-gated Shaker and the leak channel Sandman, are modulated antagonistically. As a consequence, dFB neurons are electrically silent during waking and persistently active during sleep. The key open question addressed in this project is the nature of the molecular changes that drive dFB neurons into the electrically active state. My preliminary data point to two dFB-intrinsic transducers of sleep pressure. First, the Shaker β subunit Hyperkinetic responds via a bound nicotinamide cofactor to oxidative by-products of mitochondrial electron transport, revealing a potential connection between energy metabolism, oxidative stress, and sleep, three processes implicated independently in lifespan, aging, and disease. To strengthen this connection, we will monitor sleep and the biophysics of dFB neurons after perturbing mitochondrial respiration or cellular redox chemistry and vice versa. Second, Rho GTPases relay currently unknown signals to the machinery responsible for the regulated endocytosis of Sandman, whose extraction from the plasma membrane is a prerequisite for switching the sleep-promoting activity of dFB neurons on. To identify these signals, we will investigate cell-autonomous, synaptic, and non-synaptic mechanisms of GTPase control. Because clear parallels exist between dFB neurons and sleep-active neurons in the mammalian hypothalamus, mechanistic insights that can currently be gained only in Drosophila are expected to have broad validity for understanding sleep and its disruptions.

2 374 999 €

Start date: 2019-10-01, End date: 2024-09-30