Compiling public datasets into a single, centralised repository and linking directly to analytical software completely transforms the scale and scope of causal inference across the phenome.

Emergent property inference, a novel machine learning methodology, learns distributions of neural circuit model parameters that produce computational properties and provides novel scientific insight through the quantification of the rich parametric structure it captures.

The proposed techniques enable researchers to disentangle the statistical features of neural population responses, and rigorously quantify how these features carry information about stimuli and experimental variables.

A new measure for cross-frequency coupling assesses phase-amplitude coupling and amplitude-amplitude coupling, and accounts for confounding factors such as low-frequency amplitude fluctuations, using a flexible statistical modeling approach.

Confluent cellular layers display the remarkable ability of supportingmultiscale orientational order, that is the existence of different typeof liquid crystal order at different length scales.

Visual, motor, and forward model gains learn from a postdictive update of space to keep perception and saccadic motor function aligned.

Recordings from brainstem nuclei involved in chemosensory regulation of breathing in awake freely behaving mice show different complementary types of neuronal responses to hypercapnia in the retrotrapezoid nucleus and the rostral medullary raphe.