Attractor network

An attractor network is a type of neural network that can represent patterns or memories in a stable state, often used in computational neuroscience and machine learning. The concept is based on the idea that certain configurations of the network can act as attractors in a state space, where the network evolves toward these configurations over time in response to inputs or initial conditions. ### Key Characteristics: 1. **Attractor States**: These are the stable configurations that the network can converge to.