Stochastic Resonance Based Visual Perception Using Spiking Neural Networks

Overview

Journal Front Comput Neurosci

Specialty Biology

Date 2020 Jun 6

PMID 32499690

Citations 5

Authors

Yuxuan Fu

Yanmei Kang

Guanrong Chen

Affiliations

Soon will be listed here.

Abstract

Our aim is to propose an efficient algorithm for enhancing the contrast of dark images based on the principle of stochastic resonance in a global feedback spiking network of integrate-and-fire neurons. By linear approximation and direct simulation, we disclose the dependence of the peak signal-to-noise ratio on the spiking threshold and the feedback coupling strength. Based on this theoretical analysis, we then develop a dynamical system algorithm for enhancing dark images. In the new algorithm, an explicit formula is given on how to choose a suitable spiking threshold for the images to be enhanced, and a more effective quantifying index, the variance of image, is used to replace the commonly used measure. Numerical tests verify the efficiency of the new algorithm. The investigation provides a good example for the application of stochastic resonance, and it might be useful for explaining the biophysical mechanism behind visual perception.

Citing Articles

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Mutual information measure of visual perception based on noisy spiking neural networks.

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