Research on Lateral Inhibition Network Based on Cell Membrane Electrical Model

Abstract

Lateral inhibition is a prevalent occurrence within the biological neural system, enhancing the human brain’s ability to perceive edge information within a given scene. With the increasing prominence of neural network-based machine vision, there is a significant importance in incorporating this crucial biological mechanism into the field of machine vision. However, current research on lateral inhibition networks is divorced from biological reality, especially in the study of the inhibition coefficient. To address this issue, we proposed a lateral inhibition network based on the cell membrane electrical model and applied it to image enhancement. Firstly, we analyzed the visual formation mechanism and the lateral inhibition principle, laying the theoretical foundation. Secondly, leveraging the cell membrane electrical model, we construct a lateral inhibition network with a negative exponential distribution. Finally, our experiment demonstrates that a lateral inhibition network with a negative exponential distribution has better image enhancement ability than other distributions. Using images processed with lateral inhibition as an input improved the classification accuracy of the GoogLeNet neural network by 3.39%.