A neural network model with asymmetric synaptic interactions can store and retrieve multiple continuous memory sequences with their temporal structure.

Continuous attractor networks can constrain population activity to manifolds with topology radically different from that of the network architecture.

An experimentally supported model of grid cells naturally enables them to participate in firing sequences that encode rapid trajectories in space.

A theoretical model combines self-supervised predictive learning with structural inductive biases to reveal how quasi-continuous attractors that perform accurate angular path integration can be learned from experience during development in the Drosophila and potentially other animal models.

Naturalistic animal behavior exhibits a complex organization in the temporal domain, whose variability stems from hierarchical, contextual, and stochastic sources and can be naturally explained in terms of metastable attractor models.

The hierarchy of entorhinal grid cell modules with constant scale ratios can self-organize through a new geometrically organized attractor mechanism.

Intrinsic neuronal resonance stabilizes heterogeneous neural networks by suppressing low-frequency perturbations.

Computational modeling revealed that balanced assemblies of excitatory and inhibitory neurons shape representational manifolds in olfactory cortex-like recurrent networks, resulting in joint maps of sensory and semantic information.

Adaptation within a continuous attractor neural network explains theta phase precession and procession, providing insight into the neural mechanism of theta phase coding in the hippocampus.

Two-module networks, combining sensory efficient coding and attractor dynamics for memory maintenance, effectively explain working memory error patterns, highlighting the role of sensory-memory interactions in continuously shaping memory representations.