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A computational model of the initial visual encoding together with Bayesian image reconstruction quantifies how that encoding, combined with the statistical regularities of natural images, shapes key aspects of visual perception.

Visual and prefrontal cortex excitability predict individual differences in visual imagery strength, and modulating excitability in these cortical regions causally alters the strength of visual imagery.

A method to carefully measure the molecular distance between a site of interest and a reference surface by the repeated acquisition of the two image channels followed by the statistical calculation of the relative difference.

Physiological evidence shows that the pupillary response to imagined light can be used to index the strength and vividness of an individual’s visual imagery and as a new tool for confirming aphantasia.

Differences in behavior and brain connectivity in young adolescents predicted future substance use.

Single neuron images of Drosophila driver lines reveal neuron shape and are searchable, enabling comparisons to electron microscopy and prediction of intersectional neuron targeting strategies.

Peripheral appearance models emphasising pooling processes that depend on retinal eccentricity will instead need to explore input-dependent grouping and segmentation.

A computational strategy for extracting representative numerical features from 3D microscopy data enables in-depth quantitative analysis of cell and tissue organization through machine learning-driven data integration and context-guided visualization.

Approximately 13% of figures in biology-related research articles are difficult for people with red/green colorblindness to decipher, but machine-learning models can help to identify these.

A new pipeline of electron microscopy techniques reduces the time required to visualize genetically targeted neurons and their connections by two orders of magnitude.