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Intelligent and low-power retinal prostheses are highly demanded in this era,where wearable and implantable devices are used for numerous healthcareapplications. In this paper, we propose an energy-efficient dynamic scenesprocessing framework (SpikeSEE) that combines a spike representation encodingtechnique and a bio-inspired spiking recurrent neural network (SRNN) model toachieve intelligent processing and extreme low-power computation for retinalprostheses. The spike representation encoding technique could interpret dynamicscenes with sparse spike trains, decreasing the data volume. The SRNN model,inspired by the human retina special structure and spike processing method, isadopted to predict the response of ganglion cells to dynamic scenes.Experimental results show that the Pearson correlation coefficient of theproposed SRNN model achieves 0.93, which outperforms the state of the artprocessing framework for retinal prostheses. Thanks to the spike representationand SRNN processing, the model can extract visual features in amultiplication-free fashion. The framework achieves 12 times power reductioncompared with the convolutional recurrent neural network (CRNN)processing-based framework. Our proposed SpikeSEE predicts the response ofganglion cells more accurately with lower energy consumption, which alleviatesthe precision and power issues of retinal prostheses and provides a potentialsolution for wearable or implantable prostheses.

SpikeSEE: An Energy-Efficient Dynamic Scenes Processing Framework for Retinal Prostheses