MATLAB Simulation of Hopfield Neural Network

This MATLAB-based Hopfield neural network simulation program delivers exceptional performance in modeling neural network operations. The implementation includes core algorithms for energy minimization and pattern storage using Hebbian learning rules, where connection weights are computed through matrix operations like W = pattern' * pattern. The program simulates network dynamics through iterative updates using sign() or tanh() activation functions, enabling applications in pattern recognition, optimization challenges, and data processing scenarios. The simulation offers extensive customizable parameters including network size configuration via neuron count initialization, adjustable learning rates controlling convergence speed, and configurable iteration limits for stability control. Users can modify weight matrices, threshold values, and activation functions to optimize performance. Key functions include train_hopfield() for weight initialization and recall_hopfield() for pattern retrieval using asynchronous updates. This Hopfield neural network simulator serves as a powerful computational tool for studying neural network dynamics, with practical implementations featuring energy calculation functions and convergence monitoring. The code structure allows easy integration with real-world applications while maintaining biological plausibility through symmetric weight matrices and asynchronous state updates.