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Updated: Jul 30, 2025

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SpikeSEE: An energy-efficient dynamic scenes processing framework for retinal prostheses.

Chuanqing Wang1, Chaoming Fang1, Yong Zou2

  • 1Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou, 310024, Zhejiang, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces SpikeSEE, an energy-efficient framework for retinal prostheses. It uses spike representation and a spiking recurrent neural network (SRNN) to accurately process dynamic scenes with significantly reduced power consumption.

Keywords:
Dynamic vision sensorRetinal prosthesesSpiking recurrent neural networkWearable devices

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Neuroscience

Background:

  • Retinal prostheses require intelligent, low-power processing for healthcare applications.
  • Existing frameworks face challenges with precision and energy consumption.

Purpose of the Study:

  • To develop an energy-efficient dynamic scene processing framework for retinal prostheses.
  • To improve the accuracy and reduce the power demands of visual processing in these devices.

Main Methods:

  • Proposed the SpikeSEE framework, combining spike representation encoding and a bio-inspired spiking recurrent neural network (SRNN).
  • Utilized sparse spike trains to reduce data volume for dynamic scene interpretation.
  • Modeled the human retina's structure and spike processing for ganglion cell response prediction.

Main Results:

  • Achieved a Pearson correlation coefficient of 0.93, outperforming state-of-the-art frameworks.
  • Demonstrated an 8x power reduction compared to convolutional recurrent neural network (CRNN) frameworks.
  • Enabled multiplication-free visual feature extraction.

Conclusions:

  • SpikeSEE accurately predicts ganglion cell responses with lower energy consumption.
  • The framework addresses precision and power limitations in retinal prostheses.
  • Offers a viable solution for wearable and implantable visual prosthetics.