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