Neural Coding Strategies for Event-Based Vision Data

Shane Harrigan; Sonya Coleman; Dermot Kerr; Pratheepan Yogarajah; Zheng Fang; Chengdong Wu

ICASSP 2020accepted0 citations

Neural Coding Strategies for Event-Based Vision Data

Shane Harrigan, Sonya Coleman, Dermot Kerr, Pratheepan Yogarajah, Zheng Fang, Chengdong Wu

Abstract

Neural coding schemes are powerful tools used within neuroscience. This paper introduces three different neural coding scheme formations for event-based vision data which are designed to emulate the neural behaviour exhibited by neurons under stimuli. Presented are phase-of-firing and two sparse neural coding schemes. It is determined that machine learning approaches, i.e. Convolutional Neural Network combined with a Stacked Autoencoder network, produce powerful descriptors of the patterns within events. These coding schemes are deployed in an existing action recognition template and evaluated using two popular event-based data sets.

BibTeX

@inproceedings{icassp2020_neuralcodingstra,
  title = {Neural Coding Strategies for Event-Based Vision Data},
  author = {Shane Harrigan and Sonya Coleman and Dermot Kerr and Pratheepan Yogarajah and Zheng Fang and Chengdong Wu},
  booktitle = {ICASSP 2020},
  year = {2020}
}