EFRI BRAID: Optical Neural Co-Processors for Predictive and Adaptive Brain Restoration and Augmentation

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Neurological disorders, such as traumatic brain injury, stroke, or cerebral palsy, are an important cause of disability and death worldwide. Nearly one in six of the world’s population experience these disorders. However, the very limited treatments available for these disorders provide only modest therapeutic benefits and are often associated with serious side effects. Brain-inspired, implanted computing devices could provide a solution for rehabilitating and curing these disorders. Such devices can operate by recording electrical signals from the nervous system, processing them, and stimulating another part of the brain in real-time. This allows the injured or impaired area of the brain to be bypassed or rehabilitated. However, existing brain-inspired computing devices consume too much power and are not fast enough to provide such real-time feedback and control. This project aims to create a “brain co-processor” by innovating in two aspects: first, create new algorithms based on neural signals collected from the brain to provide higher accuracy; and second, by employing optical hardware that not only can process information with high speed and low power, but also directly interfaces with the brain by exploiting light-controlled proteins in the brain. Furthermore, this project aims to improve the training and education of undergraduate and high school students, with a strong emphasis on including women and underrepresented minority communities, in multi-disciplinary research on optics, machine learning, and neuroscience. The scientific results will be disseminated to a wide scientific audience via seminars, workshops, peer-reviewed publications, and conferences.Understanding how the brain works and using that knowledge to restore or augment brain function require ultrafast parallel algorithms that are orders-of-magnitude more advanced than current state-of-the-art. This research project will build “optical neural co-processors” that use light as a computational resource and leverage brain-inspired encoder-decoder recurrent neural networks to interact with the brain in multiple natural timescales of the brain. Combining expertise in theoretical neuroscience, neuro-inspired machine learning, optogenetics, neuro-rehabilitation, nanophotonics and integrated semiconductor optics, this research project will develop brain-inspired predictive coding artificial neural networks for neural interfacing and co-processing; design and fabricate optical neural architectures that exploit emerging semiconductor nanophotonics and integrated photonics; as well as demonstrate optical neural co-processors that interface with the brain in real-time for rehabilitation in non-human primates. Along with technical advancements, neuro-ethical implications of the developed technologies will be investigated in this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.