ERC FUNDED PROJECTS

Sensory perception involves the discrimination and binding of multiple modalities of sensory input. This is especially evident in the somatosensory system where different modalities of sensory input, including thermal and mechanosensory, are combined to generate a unified percept. The neural mechanisms of multisensory binding are unknown, in part because sensory perception is typically studied within a single modality in a single brain region. I propose a multi-level approach to investigate thermo-tactile processing in the mouse forepaw system from the primary sensory afferent neurons to thalamo-cortical circuits and behaviour. The mouse forepaw system is the ideal system to investigate multisensory binding as the sensory afferent neurons are well investigated, cell type-specific lines are available, in vivo optogenetic manipulation is possible both in sensory afferent neurons and central circuits and we have developed high-resolution somatosensory perception behaviours. We have previously shown that mouse primary somatosensory forepaw cortical neurons respond to both tactile and thermal stimuli and are required for non-noxious cooling perception. With multimodal neurons how, then, is it possible to both discriminate and bind thermal and tactile stimuli? I propose 3 objectives to address this question. We will first, perform functional mapping of the thermal and tactile pathways to cortex; second, investigate the neural mechanisms of thermo-tactile discrimination in behaving mice; and third, compare neural processing during two thermo-tactile binding tasks, the first using passively applied stimuli, and the second, active manipulation of thermal objects. At each stage we will perform cell type-specific neural recordings and causal optogenetic manipulations in awake and behaving mice. Our multi-level approach will provide a comprehensive investigation into how the brain performs multisensory perceptual binding: a fundamental yet unsolved problem in neuroscience.

1 999 877 €

Start date: 2016-09-01, End date: 2021-08-31

"Children learn any language that they grow up with, adapting to any of the ca. 7000 languages of the world, no matter how divergent or complex their structures are. What cognitive processes make this extreme flexibility possible? This is one of the most burning questions in cognitive science and the ACQDIV project aims at answering it by testing and refining the following leading hypothesis: Language acquisition is flexible and adaptive to any kind of language because it relies on a small set of universal cognitive processes that variably target different structures at different times during acquisition in every language. The project aims at establishing the precise set of processes and at determining the conditions of variation across maximally diverse languages. This project focuses on three processes: (i) distributional learning, (ii) generalization-based learning and (iii) interaction-based learning. To investigate these processes I will work with a sample of five clusters of languages including longitudinal data of two languages each. The clusters were determined by a clustering algorithm seeking the structurally most divergent languages in a typological database. The languages are: Cluster 1: Slavey and Cree, Cluster 2: Indonesian and Yucatec, Cluster 3: Inuktitut and Chintang, Cluster 4: Sesotho and Russian, Cluster 5: Japanese and Turkish. For all languages, corpora are available, except for Slavey where fieldwork is planned. The leading hypothesis will be tested against the acquisition of aspect and negation in each language of the sample and also against the two structures in each language that are most salient and challenging in them (e. g. complex morphology in Chintang). The acquisition processes also depend on statistical patterns in the input children receive. I will examine these patterns across the sample with respect to repetitiveness effects, applying data-mining methods and systematically comparing child-directed and child-surrounding speech."

1 998 438 €

Start date: 2014-09-01, End date: 2019-08-31

During the 20th century computer technology evolved from bulky, slow, special purpose mechanical engines to the now ubiquitous silicon chips and software that are one of the pinnacles of human ingenuity. The goal of the field of molecular programming is to take the next leap and build a new generation of matter-based computers using DNA, RNA and proteins. This will be accomplished by computer scientists, physicists and chemists designing molecules to execute ``wet'' nanoscale programs in test tubes. The workflow includes proposing theoretical models, mathematically proving their computational properties, physical modelling and implementation in the wet-lab. The past decade has seen remarkable progress at building static 2D and 3D DNA nanostructures. However, unlike biological macromolecules and complexes that are built via specified self-assembly pathways, that execute robotic-like movements, and that undergo evolution, the activity of human-engineered nanostructures is severely limited. We will need sophisticated algorithmic ideas to build structures that rival active living systems. Active-DNA, aims to address this challenge by achieving a number of objectives on computation, DNA-based self-assembly and molecular robotics. Active-DNA research work will range from defining models and proving theorems that characterise the computational and expressive capabilities of such active programmable materials to experimental work implementing active DNA nanostructures in the wet-lab.

2 349 603 €

Start date: 2018-11-01, End date: 2023-10-31

The two biggest challenges in solving practical optimization problems are computational intractability, and the presence of uncertainty: most problems are either NP-hard, or have incomplete input data which makes an exact computation impossible. Recently, there has been a huge progress in our understanding of intractability, based on spectacular algorithmic and lower bound techniques. For several problems, especially those with only local constraints, we can design optimum approximation algorithms that are provably the best possible. However, typical optimization problems usually involve complex global constraints and are much less understood. The situation is even worse for coping with uncertainty. Most of the algorithms are based on ad-hoc techniques and there is no deeper understanding of what makes various problems easy or hard. This proposal describes several new directions, together with concrete intermediate goals, that will break important new ground in the theory of approximation and online algorithms. The particular directions we consider are (i) extend the primal dual method to systematically design online algorithms, (ii) build a structural theory of online problems based on work functions, (iii) develop new tools to use the power of strong convex relaxations and (iv) design new algorithmic approaches based on non-constructive proof techniques. The proposed research is at the cutting edge of algorithm design, and builds upon the recent success of the PI in resolving several longstanding questions in these areas. Any progress is likely to be a significant contribution to theoretical computer science and combinatorial optimization.

1 519 285 €

Start date: 2014-05-01, End date: 2019-04-30