Towards an ai endowed visual neuroprosthesis for the blinddevelopment and first-in-human implementation of a deep learning intracortical neural interface

Resumen

Resumen de la tesis: In this doctoral thesis, I aim to step further beyond state-of-the-art towards the development of a cortical neuroprosthesis for the blind. Currently, a fully working visual cortical neuroprosthesis able to help blind people to recover a form of vision is still a challenging ongoing project, although strong efforts are being made by research groups all over the world. This work is situated at the conjunction between neuroscience, neural engineering, and artificial intelligence. In order to learn how to write into the brain, we hypothesize and develop a Deep Learning (DL) artificial retina able to mimic ganglion cells’ firing rates in response to spatiotemporal light patters. We developed and tested a hardware/software neural interface pipeline composed of a camera integrated within the user’s pair of glasses, an edge-computing system to process the video feed -where its Deep Learning capabilities abide- and transform the scene’s most relevant visual features into commands to a neurostimulator’s which drives electrical pulses to the brain through an Utah array. To extract the most relevant information from a complex and dynamic visual environment, we used task-oriented DL models, such as object detection and semantic segmentation models. Furthermore, we propose and deploy computational neural encoding models such an artificial retina, to drive electrical stimulation pulse patterns that are delivered into the neural tissue through a Utah array, to encode this information into the brain. Finally, we discuss the evoked visual percepts generated by a multielectrode intracortical implant in a first-in-human trial. To the best of our knowledge, we have developed and implemented the first AI driven intracortical visual neuroprosthesis for the blind: Neurolight. http://repositorio.bib.upct.es/dspace/