Romeni Simone, Zoccolan Davide, Micera Silvestro
Bertarelli Foundation Chair in Translational NeuroEngineering, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Visual Neuroscience Lab, International School for Advanced Studies (SISSA), Trieste, Italy.
Patterns (N Y). 2021 Jun 16;2(7):100286. doi: 10.1016/j.patter.2021.100286. eCollection 2021 Jul 9.
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.
视神经电刺激是一种有望恢复盲人视力的技术。机器学习方法可用于选择有效的刺激方案，但它们需要受刺激系统的模型来生成足够的训练数据。在这里，我们使用卷积神经网络（CNN）作为腹侧视觉通路的模型。遗传算法通过优化施加在代表视神经的一层的激活，驱动CNN中代表皮质区域的一层中的单元朝着所需模式激活。为了模拟电极阵列各部位引发的激活模式，引入了一个简单的点源模型，并研究了其在静态和动态场景下的优化过程。心理物理学数据证实，我们的刺激进化框架产生的结果与自然视觉兼容。机器学习方法可能会成为优化和个性化神经假体系统的非常强大的工具。
