A machine learning framework to optimize optic nerve electrical stimulation for vision restoration - Romeni, Simone; Zoccolan, Davide; Micera, Silvestro (Patterns (New York, N.Y.), 2021)
Optic nerve electrical stimulation is a promising technique to restore vision in blind subjects. Machine learning methods can be used to select effective stimulation protocols, but they require a model of the stimulated system to generate enough training data. Here, we use a convolutional neural network (CNN) as a model of the ventral visual stream. A genetic algorithm drives the activation of the units in a layer of the CNN representing a cortical region toward a desired pattern, by refining the activation imposed at a layer representing the optic nerve. To simulate the pattern of activation elicited by the sites of an electrode array, a simple point-source model was introduced and its optimization process was investigated for static and dynamic scenes. Psychophysical data confirm that our stimulation evolution framework produces results compatible with natural vision. Machine learning approaches could become a very powerful tool to optimize and personalize neuroprosthetic systems. •A framework to optimize optic nerve stimulation protocols has been implemented•A physiologically constrained convolutional neural network models the visual system•A genetic algorithm evolves optimal stimulations to match cortical activation•Our protocols elicit the right stimulus classes in static and dynamic scenarios Electrical stimulation of the optic nerve can allow the restoration of lost visual functions in an effective and clinically exploitable way. To achieve this goal, it is crucial to develop a suitable approach to target selectively nerve fiber subpopulations that mediate different sensations but share similar locations in the nerve. In the present work, we use a simple computational model of the primate visual system to show that it is possible to optimize the stimulation at the level of the optic nerve to replicate a pattern of activity in a cortical region, producing, at the same time, reliable sensations. This result could produce nerve stimulation patterns that exploit the convergent nature of the visual system to “correct” the representation error introduced at the nerve level. In the long term, this would lead to eliciting naturalistic sensations from non-intuitive protocols that exploit machine learning to overcome the technological limits of nerve interfaces. We formulated a computational framework for the optimization of optic nerve stimulation patterns. We have implemented a model of the primate visual system, and an algorithm that allows the evolution of an optic nerve stimulation protocol that induces activation corresponding to natural visual stimuli in a given brain region and, consequently, a specified visual sensation. This could pave the way for novel machine-learning-based optimization of optic nerve stimulation to produce naturalistic sensations in blind patients.