Emergence of Robust Memory Manifolds

The ability to store continuous variables in the state of a biological system (e.g., a neural network) is critical for many behaviors. Most models for implementing such a memory manifold require hand-crafted symmetries in the interactions or precise fine-tuning of parameters. We present a general principle that we refer to as (FS), which allows a family of neural networks to self-organize to a critical state exhibiting multiple memory manifolds without parameter fine-tuning or symmetries. Memory manifolds arising from FS exhibit a wide range of emergent relaxational timescales and can be used as general purpose integrators for inputs aligned with the manifold. Moreover, FS allows robust memory manifolds in small networks, and is therefore relevant to debates about implementing continuous attractors with a small number of neurons in light of recent experimental discoveries.