#associative-memory

Dec 4, 2023 · Hopfield Networks for dummies

Hopfield Networks, a type of Recurrent Neural Network (RNN), are renowned for their unique ability to store and retrieve patterns through associative memory. This means they can recall a complete memory from just a partial input. Inspired by the Ising model in physics, which explains magnetic behaviors in certain materials, Hopfield Networks use a system of interconnected neurons, each able to be in one of two states, akin to magnetic dipoles. These neurons are fully connected, each influencing the other based on the strength of their connections. The network dynamically evolves to a stable state where the system's energy is minimized, representing a memory. The overall network state thus can represent binary information, like an image or a pattern, making Hopfield Networks particularly effective in pattern recognition and completion tasks.