Project description
Seeing neuroprostheses in a new light
Neuroprostheses use electrodes to interface with the nervous system. These devices can supplant or supplement the input/output of the nervous system in order to restore function, such as vision for the blind. The EU-funded NeuraViPeR project will construct a neuroprosthesis with thousands of electrodes and develop adaptive machine learning algorithms for a new brain–computer interfacing technology that will be used to stimulate the visual cortex. The project's work includes the creation of thin (~10 µm thick, < 50 µm wide) flexible probes that cause minimal tissue damage and the design of new electrode coatings that are stable in spite of long-term repeated electrical stimulation. The deep learning algorithms under development will transform camera footage into stimulation patterns for the cortex.
Objective
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.
Fields of science
- 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
Keywords
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
8006 Zurich
Switzerland