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Neurolight: A Deep Learning Neural Interface for Cortical Visual Prostheses
International Journal of Neural Systems ( IF 8 ) Pub Date : 2020-05-12 , DOI: 10.1142/s0129065720500458 Antonio Lozano 1 , Juan Sebastián Suárez 2, 3 , Cristina Soto-Sánchez 2, 3 , Javier Garrigós 1 , J Javier Martínez-Alvarez 1 , J Manuel Ferrández 1 , Eduardo Fernández 2
International Journal of Neural Systems ( IF 8 ) Pub Date : 2020-05-12 , DOI: 10.1142/s0129065720500458 Antonio Lozano 1 , Juan Sebastián Suárez 2, 3 , Cristina Soto-Sánchez 2, 3 , Javier Garrigós 1 , J Javier Martínez-Alvarez 1 , J Manuel Ferrández 1 , Eduardo Fernández 2
Affiliation
Visual neuroprosthesis, that provide electrical stimulation along several sites of the human visual system, constitute a potential tool for vision restoration for the blind. Scientific and technological progress in the fields of neural engineering and artificial vision comes with new theories and tools that, along with the dawn of modern artificial intelligence, constitute a promising framework for the further development of neurotechnology. In the framework of the development of a Cortical Visual Neuroprosthesis for the blind (CORTIVIS), we are now facing the challenge of developing not only computationally powerful tools and flexible approaches that will allow us to provide some degree of functional vision to individuals who are profoundly blind. In this work, we propose a general neuroprosthesis framework composed of several task-oriented and visual encoding modules. We address the development and implementation of computational models of the firing rates of retinal ganglion cells and design a tool — Neurolight — that allows these models to be interfaced with intracortical microelectrodes in order to create electrical stimulation patterns that can evoke useful perceptions. In addition, the developed framework allows the deployment of a diverse array of state-of-the-art deep-learning techniques for task-oriented and general image pre-processing, such as semantic segmentation and object detection in our system’s pipeline. To the best of our knowledge, this constitutes the first deep-learning-based system designed to directly interface with the visual brain through an intracortical microelectrode array. We implement the complete pipeline, from obtaining a video stream to developing and deploying task-oriented deep-learning models and predictive models of retinal ganglion cells’ encoding of visual inputs under the control of a neurostimulation device able to send electrical train pulses to a microelectrode array implanted at the visual cortex.
中文翻译:
Neurolight:用于皮质视觉假体的深度学习神经接口
视觉神经假体沿人类视觉系统的多个部位提供电刺激,是盲人恢复视力的潜在工具。神经工程和人工视觉领域的科技进步带来了新的理论和工具,伴随着现代人工智能的曙光,构成了神经技术进一步发展的前景广阔的框架。在开发用于盲人的皮质视觉神经假体 (CORTIVIS) 的框架中,我们现在面临的挑战不仅是开发强大的计算工具和灵活的方法,这将使我们能够为那些深刻理解的人提供某种程度的功能性视觉。瞎的。在这项工作中,我们提出了一个由几个面向任务和视觉编码模块组成的通用神经假体框架。我们解决了视网膜神经节细胞放电率计算模型的开发和实施,并设计了一种工具 - Neurolight - 允许这些模型与皮质内微电极连接,以创建可以唤起有用感知的电刺激模式。此外,开发的框架允许部署各种最先进的深度学习技术,用于面向任务和一般图像预处理,例如我们系统管道中的语义分割和对象检测。据我们所知,这构成了第一个基于深度学习的系统,旨在通过皮层内微电极阵列直接与视觉大脑交互。
更新日期:2020-05-12
中文翻译:
Neurolight:用于皮质视觉假体的深度学习神经接口
视觉神经假体沿人类视觉系统的多个部位提供电刺激,是盲人恢复视力的潜在工具。神经工程和人工视觉领域的科技进步带来了新的理论和工具,伴随着现代人工智能的曙光,构成了神经技术进一步发展的前景广阔的框架。在开发用于盲人的皮质视觉神经假体 (CORTIVIS) 的框架中,我们现在面临的挑战不仅是开发强大的计算工具和灵活的方法,这将使我们能够为那些深刻理解的人提供某种程度的功能性视觉。瞎的。在这项工作中,我们提出了一个由几个面向任务和视觉编码模块组成的通用神经假体框架。我们解决了视网膜神经节细胞放电率计算模型的开发和实施,并设计了一种工具 - Neurolight - 允许这些模型与皮质内微电极连接,以创建可以唤起有用感知的电刺激模式。此外,开发的框架允许部署各种最先进的深度学习技术,用于面向任务和一般图像预处理,例如我们系统管道中的语义分割和对象检测。据我们所知,这构成了第一个基于深度学习的系统,旨在通过皮层内微电极阵列直接与视觉大脑交互。