A new machine-learning toolbox unveils coevolutionary protein motifs related to structure, function, and phylogeny from sequence information only.

Evidence for multiple brain systems for sequence processing involving statistical inferences at multiple scales.

Hippocampal sharp-wave sequences, which are considered the key phenomenon underlying consolidation of episodic memory, are preserved even after an animal’s memory is impaired.

The epistasis observed in TF-DNA binding preferences can be explained by the presence of two optima of very similar Gibbs energy that are located relatively far from each other in sequence space.

A systematic assessment of previously proposed fMRI metrics of motor sequence learning reveals widespread activity reductions and subtle multivariate pattern changes outside of primary motor cortex.

Interactions in protein complexes can be predicted from evolutionary information from genomic sequences.

Detailed analysis of fMRI data shows that sequences of movements are associated with individual patterns of neural activity that become more distinct with training.

The cerebellum can learn a sequence of responses by using a feedback signal from the previous movement to learn the next one.

Building on simple unsupervised matrix factorization techniques, the seqNMF algorithm successfully recovers neural sequences in a wide range of simulated and real datasets.

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