A very large number of place-field maps can be robustly learned by association of external cues with the grid-driven response, however plasticity in the grid-cell inputs renders the place-cell responses volatile.

A shift in fitness optimum of a polygenic trait rapidly introduces small frequency differences between alleles with effects aligned with and opposing the shift, which gradually translate into small differences in fixation probability.

Spatial genomics reveals the mode of evolution of a tumour based on the positions of mutations in high-resolution samples.

Neural sensory representations impose an inductive bias over the space of learning tasks, allowing some tasks to be learned by a downstream neuron more sample-efficiently than others.

Evolution of multiple closely related strains with host-pathogen-like interactions but only one niche and no tradeoffs, can give rise to a spatiotemporally chaotic ecological state that continually diversifies even with generalist mutations that slow the evolution.

A mathematical modelling approach to understanding zebrafish stripe pattern formation exemplifies a biological rule-set sufficient to generate wild-type and a diverse range of mutant patterns.

The collective dynamics of cell signaling relays are at once dramatically sensitive to the system dimensionality and insensitive to many biological details.

Benchmark simulations show that the VEXAT muscle model is more accurate than a Hill-type muscle model at mimicking the response of biological muscle to length changes great and small.

aLENS implements new parallel methods to resolve cytoskeletal assembly dynamics with high stability, physical consistency, and scalability.