论文标题
适应大脑样神经网络，用于建模皮质视觉假体
Adapting Brain-Like Neural Networks for Modeling Cortical Visual Prostheses
论文作者
论文摘要
皮质假体是植入视觉皮层中的设备，试图通过电刺激神经元来恢复视力失去视力。当前，这些设备提供的视觉是有限的，并且准确地预测刺激引起的视觉感知是一个开放的挑战。我们建议通过利用“大脑样”卷积神经网络（CNN）来应对这一挑战，这些卷积神经网络已成为视觉系统的有前途的模型。为了研究适应大脑样的CNN来建模视觉假体的可行性，我们开发了一种概念验证模型，以预测电刺激引起的感知。我们表明，CNN激活的神经学启发的解码会产生定性准确的磷酸，可与实际患者报道的磷酸相媲美。总体而言，这是建立类似大脑的电刺激模型的重要第一步，这可能不仅可以提高皮质假体提供的视力质量，而且还可以进一步提高我们对神经视力守则的理解。
Cortical prostheses are devices implanted in the visual cortex that attempt to restore lost vision by electrically stimulating neurons. Currently, the vision provided by these devices is limited, and accurately predicting the visual percepts resulting from stimulation is an open challenge. We propose to address this challenge by utilizing 'brain-like' convolutional neural networks (CNNs), which have emerged as promising models of the visual system. To investigate the feasibility of adapting brain-like CNNs for modeling visual prostheses, we developed a proof-of-concept model to predict the perceptions resulting from electrical stimulation. We show that a neurologically-inspired decoding of CNN activations produces qualitatively accurate phosphenes, comparable to phosphenes reported by real patients. Overall, this is an essential first step towards building brain-like models of electrical stimulation, which may not just improve the quality of vision provided by cortical prostheses but could also further our understanding of the neural code of vision.
