Attractor Network Models

The term attractor, when applied to neural circuits, refers to dynamical states of neural populations that are self-sustained and stable against perturbations. It is part of the vocabulary for describing neurons or neural networks as dynamical systems. This concept helps to quantitatively describe self-organized spatiotemporal neuronal firing patterns in a circuit, during spontaneous activity or underlying brain functions. Moreover, the theory of dynamical systems provides tools for examining the stability and robustness of a neural circuit's behavior, proposes a theory of learning and memory in terms of the formation of multiple attractor states or continuous attractors, and provides insights into how variations in cellular/synaptic properties give rise to a diversity of computational capabilities.