000901618 001__ 901618
000901618 005__ 20230210112659.0
000901618 0247_ $$2CORDIS$$aG:(EU-Grant)899287$$d899287
000901618 0247_ $$2CORDIS$$aG:(EU-Call)H2020-FETOPEN-2018-2019-2020-01$$dH2020-FETOPEN-2018-2019-2020-01
000901618 0247_ $$2originalID$$acorda__h2020::899287
000901618 035__ $$aG:(EU-Grant)899287
000901618 150__ $$aNeural Active Visual Prosthetics for Restoring Function$$y2020-09-01 - 2025-02-28
000901618 372__ $$aH2020-FETOPEN-2018-2019-2020-01$$s2020-09-01$$t2025-02-28
000901618 450__ $$aNeuraViPeR$$wd$$y2020-09-01 - 2025-02-28
000901618 5101_ $$0I:(DE-588b)5098525-5$$2CORDIS$$aEuropean Union
000901618 680__ $$aApproaches that aim to restore vision for blind individuals with electrical stimulation of the brain have hit a technology wall. Existing systems only stimulate a small set of neurons in the brain, and interfaces have a longevity of only a few months. NeuraViPeR aims to lay ground-breaking foundation for a radically new paradigm which consists not only of constructing a neuroprosthesis with thousands of electrodes but also the creation of adaptive machine learning algorithms for a new brain-computer interfacing technology, which will remain safe and effective for decades. Several technological breakthroughs will be established. First, innovative approaches for stimulation with high-electrode-count interfacing with the visual cortex; creating thin (~10 µm thick, < 50 µm wide) flexible probes that cause minimal tissue damage; new electrode coatings that will be stable in spite of long-term repeated electrical stimulation; and novel microchip methods for combining online channeling of the stimulation currents to many thousands of electrodes, combined with monitoring of neuronal activity in higher cortical areas. Second, new deep learning algorithms that transform the camera footage into stimulation patterns for the cortex and that use feedback on recorded brain states and eye tracking to improve perception in a closed-loop approach. The algorithms will extract maximally relevant information to enable blind individuals to recognize objects and facial expressions and navigate through unfamiliar environments. The software algorithms will be translated onto low-latency, power-efficient neuromorphic deep learning hardware, to create a neuroprosthesis system that is lightweight, robust, and portable.  NeuraViPeR will tackle these challenges through interdisciplinary teams with complementary expertise in computational, systems and clinical neuroscience, materials engineering, microsystems design, and deep learning.
000901618 909CO $$ooai:juser.fz-juelich.de:901618$$pauthority$$pauthority:GRANT
000901618 980__ $$aG
000901618 980__ $$aCORDIS
000901618 980__ $$aAUTHORITY