MATLAB Toolbox for Pulse Coupled Neural Network Algorithm

Pulse Coupled Neural Network (PCNN) is a biologically-inspired neural network model based on the mammalian visual cortex, featuring pulse synchronization and coupling properties. It is widely applied in image processing, pattern recognition, and related fields.

The MATLAB toolbox provides a complete PCNN implementation framework with the following core functionalities: Parameter Configuration Interface - Allows flexible adjustment of biological parameters such as neuronal linking strength and decay coefficients through structured input arguments Pulse Propagation Module - Simulates dynamic coupling and pulse synchronization between neurons using iterative matrix operations and threshold-based firing mechanisms Image Processing Applications - Built-in typical processing pipelines for image segmentation and edge detection, implemented via pixel-level neural activation mapping Visualization Tools - Real-time display of neuronal firing patterns and network dynamics using MATLAB's graphics and animation capabilities

By encapsulating underlying mathematical operations, this toolbox enables researchers to rapidly validate PCNN performance in time-series signal processing and medical image analysis scenarios. Users can focus on network topology design and application logic without reimplementing fundamental mechanisms like pulse coupling. The toolbox utilizes vectorized operations for efficient computation and includes predefined configuration templates for common use cases.