“Memory from patterns: Attractor and integrator networks in the brain”, with Mikail, is submitted. Comments and suggestions are welcomed!

The theory of how complex patterns emerge from simple interactions and constituents is one of the big ideas in biology, explaining animal coats and morphogenesis.

The same principles can produce dynamical states for computation in the brain, in the form of attractor networks. We review how attractor networks generate states for robust representation, integration, and memory.

Our review covers the conceptual ideas, the theory, and the potential utility of continuous and discrete attractor networks, then focuses on the empirical evidence that the brain computes using these structures. Finally, we discuss modern developments in combining the concepts of modularity and attractors, and list future challenges.

We hope the review provides a vista of a field of systems neuroscience driven by theoretical ideas, where theory and experiment have come together fruitfully and harmoniously.