Descrizione del progetto
Neuroprotesi viste sotto una nuova luce
Le neuroprotesi utilizzano elettrodi per interfacciarsi con il sistema nervoso. Questi dispositivi possono soppiantare o integrare gli input/output del sistema nervoso al fine di ripristinare funzionalità, quali la vista per i non vedenti. Il progetto NeuraViPeR, finanziato dall’UE, realizzerà una neuroprotesi con migliaia di elettrodi e svilupperà algoritmi di apprendimento automatico adattativi per una nuova tecnologia di interfacciamento cervello-computer che verrà impiegata per stimolare la corteccia visiva. Il lavoro del progetto comprende la creazione di sonde flessibili e sottili (~10 µm di spessore, < 50 µm di larghezza), che provocano danni minimi ai tessuti, e la progettazione di nuovi rivestimenti per elettrodi che rimangono stabili nonostante la ripetuta stimolazione elettrica a lungo termine. Gli algoritmi di apprendimento profondo in fase di sviluppo trasformeranno le riprese della telecamera in modelli di stimolazione per la corteccia.
Obiettivo
Approaches that aim to restore vision for blind individuals with electrical stimulation of the brain have hit a technology wall. Existing systems only stimulate a small set of neurons in the brain, and interfaces have a longevity of only a few months. NeuraViPeR aims to lay ground-breaking foundation for a radically new paradigm which consists not only of constructing a neuroprosthesis with thousands of electrodes but also the creation of adaptive machine learning algorithms for a new brain-computer interfacing technology, which will remain safe and effective for decades. Several technological breakthroughs will be established. First, innovative approaches for stimulation with high-electrode-count interfacing with the visual cortex; creating thin (~10 µm thick, < 50 µm wide) flexible probes that cause minimal tissue damage; new electrode coatings that will be stable in spite of long-term repeated electrical stimulation; and novel microchip methods for combining online channeling of the stimulation currents to many thousands of electrodes, combined with monitoring of neuronal activity in higher cortical areas. Second, new deep learning algorithms that transform the camera footage into stimulation patterns for the cortex and that use feedback on recorded brain states and eye tracking to improve perception in a closed-loop approach. The algorithms will extract maximally relevant information to enable blind individuals to recognize objects and facial expressions and navigate through unfamiliar environments. The software algorithms will be translated onto low-latency, power-efficient neuromorphic deep learning hardware, to create a neuroprosthesis system that is lightweight, robust, and portable. NeuraViPeR will tackle these challenges through interdisciplinary teams with complementary expertise in computational, systems and clinical neuroscience, materials engineering, microsystems design, and deep learning.
Campo scientifico
- natural sciencesbiological sciencesneurobiology
- natural sciencescomputer and information sciencessoftware
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsoptical sensors
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- medical and health sciencesmedical biotechnologyimplants
Parole chiave
Programma(i)
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Vedi altri progetti per questo bandoBando secondario
H2020-FETOPEN-2018-2019-2020-01
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
8006 Zurich
Svizzera