Hopfield Neural Network

This repository contains a python implementation of the Hopfield Network, a form of recurrent artificial neural network that serves as a content-addressable (associative) memory system. This project is developed with educational purpose to study attractor dynamics, energy minimization, and memory retrieval in neural lattices.

Features

• Multiple Update Rules: Supports both Synchronous (parallel) and Sequential (asynchronous) updates.
• Pattern Generation: Preset patterns for ‘H’, ‘X’, ‘+’, and striped lattices.
• Energy Tracking: Real-time calculation of the network’s energy descent during retrieval.
• Visualization Suite: High-quality matplotlib plotting for lattice configurations and mathematical vector schematics.
• Noise Robustness: Tools to test memory retrieval against varying levels of stochastic noise.