Obiettivo "Neural networks and deep learning algorithms are currently achieving impressive state-of-the-art results. In parallel computational neuroscience has made tremendous progress with both theories of neural computation and with hardware implementations of dedicated brain-inspired computing platforms.However, despite this remarkable progress, today’s artificial systems are still not able to compete with biological ones in tasks that involve processing of sensory data acquired in real-time, in complex and uncertain settings. One of the reasons is that neural computation in biological systems is very different from the way today's computers operate: it is tightly linked to the properties of their computational embodiment, to the physics of their computing elements and to their temporal dynamics. Conventional computers on the other hand operate with mainly serial and synchronous logic gates, with functions that are decoupled from their hardware implementation, and with discretized and virtual time.In this project we will combine the recent advancements in machine learning and neural computation with the latest developments in neuromorphic computing technology to design autonomous systems that can express robust cognitive behavior while interacting with the environment, through the physics of their computing substrate. To achieve this we will embed in robotic platforms microelectronic neuromorphic processors and sensors that implement biophysically realistic neural computational primitives and dynamics. We will adopt active-sensing and on-line spike-based learning strategies, context and state-dependent computation, and probabilistic inference methods for ""programming"" these neuromorphic cognitive agents to solve challenging tasks in real-time. Our results will lead to compact low-power intelligent sensory-motor systems that will have a large impact on service and consumer robotics, Internet of Things, as well as prosthetics and personalized medicine." Campo scientifico natural sciencescomputer and information sciencesinternetnatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningnatural sciencesbiological sciencesneurobiologycomputational neuroscienceengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringroboticsnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Parole chiave Neural networks cognitive systems biomimetics computational neuroscience embedded systems deep netwroks learning neuromorphic Programma(i) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Argomento(i) ERC-2016-COG - ERC Consolidator Grant Invito a presentare proposte ERC-2016-COG Vedi altri progetti per questo bando Meccanismo di finanziamento ERC-COG - Consolidator Grant Istituzione ospitante UNIVERSITAT ZURICH Contribution nette de l'UE € 1 999 090,00 Indirizzo RAMISTRASSE 71 8006 Zurich Svizzera Mostra sulla mappa Regione Schweiz/Suisse/Svizzera Zürich Zürich Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 1 999 090,00 Beneficiari (1) Classifica in ordine alfabetico Classifica per Contributo netto dell'UE Espandi tutto Riduci tutto UNIVERSITAT ZURICH Svizzera Contribution nette de l'UE € 1 999 090,00 Indirizzo RAMISTRASSE 71 8006 Zurich Mostra sulla mappa Regione Schweiz/Suisse/Svizzera Zürich Zürich Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 1 999 090,00
