MATLAB Implementation of Pulse Coupled Neural Network (PCNN)

Pulse Coupled Neural Network (PCNN) is a biologically inspired neural network model derived from visual cortex mechanisms, widely applied in image processing domains. Its core characteristic involves simulating neuron activation through pulse synchronization mechanisms, making it particularly suitable for tasks like image segmentation and edge detection.

Implementing a simplified PCNN model in MATLAB typically involves three key computational steps: input modulation, dynamic threshold comparison, and pulse generation. The input image is first converted into a 2D matrix serving as external stimuli, where each pixel corresponds to a neuron. The internal activity term is generated through linear weighted combinations of neighboring neuron outputs (linking components) and original inputs. When the internal activity exceeds the dynamic threshold, the neuron fires a pulse and resets the threshold, forming an iterative feedback process. In code implementation, this requires maintaining matrices for internal activity (U), dynamic threshold (T), and pulse output (Y), with iterative updates using element-wise operations and convolution for neighbor interactions.

The model incorporates biologically plausible parameters (such as decay coefficients and linking strength) that control pulse propagation characteristics when adjusted. Typical applications include: achieving region segmentation through multiple iterations that synchronize pulse firing in neurons with similar grayscale values, or constructing feature maps using first pulse time differences. Care must be taken to avoid oversimplification that would sacrifice PCNN's core advantage of pulse synchronization特性. Code parameters like alpha_L (linking decay), alpha_T (threshold decay), and V_T (threshold magnitude) require careful tuning based on image characteristics.

Potential extensions include introducing adaptive parameter mechanisms using histogram analysis, or combining with morphological processing methods to enhance robustness in complex scenarios. MATLAB implementations can leverage built-in functions like conv2 for efficient neighbor interactions and logical indexing for threshold comparisons.