Descripción del proyecto
Neuroprótesis bajo una nueva óptica
Las neuroprótesis utilizan electrodos para interactuar con el sistema nervioso. Estos dispositivos pueden reemplazar o complementar los estímulos de entrada o salida del sistema nervioso para restaurar su función, como la visión para las personas ciegas. El proyecto NeuraViPeR, financiado con fondos europeos, creará una neuroprótesis con miles de electrodos y desarrollará algoritmos de aprendizaje automático adaptativo para una nueva tecnología de interfaz cerebro-ordenador que se utilizará para estimular la corteza visual. El trabajo del proyecto incluye la creación de sondas flexibles y delgadas (~10 µm de grosor, <50 µm de anchura) que provoquen un daño tisular mínimo y el diseño de nuevos materiales de recubrimiento para electrodos que sean estables a pesar de la estimulación repetida a largo plazo. Los algoritmos de aprendizaje profundo en desarrollo transformarán imágenes de cámaras en patrones de estímulos para la corteza.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-FETOPEN-2018-2019-2020-01
Régimen de financiación
RIA - Research and Innovation actionCoordinador
8006 Zurich
Suiza