# Dynamic-Step-Size Regulation in Pulse-Coupled Neural Networks

## TL;DR

This paper introduces a dynamic-step-size mechanism in pulse-coupled neural networks to improve image segmentation adaptability and performance.

## Contribution

The novel dynamic-step-size mechanism uses trigonometric functions and supervised optimization to enhance segmentation adaptability.

## Key findings

- The proposed method achieves 92.1% Dice score under noise (σ=0.2).
- It outperforms existing PCNN variants with higher IoU and faster processing speed.

## Abstract

Pulse-coupled neural networks (PCNNs) are capable of segmenting digital images in a multistage unsupervised fashion; however, optimal output selection remains challenging. To address the above problem, this paper emphasizes the role of the step size, which influences the decreasing speed of the membrane potential and the dynamic threshold profoundly. A dynamic-step-size mechanism is proposed, utilizing trigonometric functions to adaptively control segmentation granularity, along with the supervised optimization of a single parameter ϕ via intersection over union (IoU) maximization, reducing tuning complexity. Thus, the number of groups of image segmentation becomes controllable and the model itself becomes more adaptive than ever for various scenarios. Experimental results further demonstrate the enhanced robustness under noise (92.1% Dice at σ=0.2), outperforming SPCNN and PCNN with IoU = 0.8863, Dice = 0.901, and 0.8684 s/image.