A dynamical neural-network model whose activity tends toward stable states or sets. Recurrent excitation and inhibition can maintain a representation after the original input ends. The model must specify stability and noise consequences to predict behavior.
A localized population-activity pattern in a network with a continuous family of possible represented positions. Noise can shift the pattern's center, producing drift in a remembered continuous variable while the pattern remains present. This yields testable relationships between delay activity and report errors.
Articles by others on the same topic
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.