4.6 Article

NeuroSEE: A Neuromorphic Energy-Efficient Processing Framework for Visual Prostheses

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2022.3172306

关键词

Visual prosthesis; Visualization; Biological system modeling; Retina; Predictive models; Image restoration; Image edge detection; Visual prostheses; bio-inspired processing; spiking neural network; Age-related macular degeneration; retinitis pigmentosa; wearable devices

资金

  1. Zhejiang Key RD Program [2021C03002]
  2. Zhejiang Leading Innovative and Entrepreneur Team Introduction Program [2020R01005]

向作者/读者索取更多资源

NeuroSEE is a neuromorphic energy-efficient processing framework that combines spike representation encoding and bio-inspired processing methods. It achieves energy-efficient visual information processing and outperforms existing frameworks in terms of prediction performance while reducing power consumption.
Visual prostheses with both comprehensive visual signal processing capability and energy efficiency are becoming increasingly demanded in the age of intelligent personal healthcare, particularly with the rise of wearable and implantable devices. To address this trend, we propose NeuroSEE, a neuromorphic energy-efficient processing framework that combines a spike representation encoding technique and a bio-inspired processing method. This framework first utilizes sparse spike trains to represent visual information, and then a bio-inspired spiking neural network (SNN) is adopted to process the spike trains. The SNN model makes use of an IF neuron with multiple spike-firing rates to decrease the energy consumption without compensating for prediction performance. The experimental results indicate that when predicting the response of the primary visual cortex, the framework achieves a state-of-the-art Pearson correlation coefficient performance. Spike-based recording and processing methods simplify the storage and transmission of redundant scene information and complex calculation processes. It could reduce power consumption by 15 times compared with the existing Convolutional neural network (CNN) processing framework. The proposed NeuroSEE framework predicts the response of the primary visual cortex in an energy efficient manner, making it a powerful tool for visual prostheses.

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