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

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

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

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

A fundamental lower-bound on memory recall precision, which declines with storage duration and number of stored items, is derived, and human performance is shown to be well-fit by this theoretical bound.

The experiences that are most rewarding at the time are not necessarily those that are remembered long-term.

A decoding-based, state-space reconstruction reveals that neurons in macaque IT cortex change the structure of their collective attractor dynamics depending on task contexts.

Mathematical modeling suggests that grid cells in the rodent brain use fundamental principles of number theory to maximize the efficiency of spatial mapping, enabling animals to accurately encode their location with as few neurons as possible.

Spike frequency adaptation provides spiking neural networks with long short-term memory.

A longer birdsong-learning window evolves in response to sexual selection for song complexity and is associated with faster evolution of song performance characteristics.