Computational analyses quantitatively account for optical refraction at air-water interfaces to enable visual neuroscientists to present distortion-free stimuli to underwater animals.

High-level visual cortex and leading neural network models of the visual system retain information about multiple visual scene variables in independent, non-interfering dimensions of their population codes.

A standard for complex mouse behavior that can be successfully reproduced across laboratories, along with open-access data and tools to implement provide a resource for reproducibility through collaborative open-science approaches.

Towards a community effort, we here present a flexible yet standardised visual stimulator design based on open hardware and software.

Functional coupling between visual areas reflects supra-areal eccentricity organization distinct from overlapping receptive field connectivity during both rest conditions and naturalistic movie viewing.

Pynapple advances data analysis in systems neuroscience with a versatile, open-source framework that uses common objects to handle diverse data streams, ensuring reliable and reproducible analysis while maintaining long-term backward compatibility.

A novel Bayesian method of modeling retinotopic maps is more accurate than traditional voxel-wise methods and can be used to automatically derive high-quality maps.

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.

Genome-wide analysis of DNA methylation and accessible chromatin shows that retinal rods and cones have distinct epigenomic features that reflect differences in their development and function.

Measures of visuocortical activity during aversive generalization learning revealed sharpened representations of facial identity, reflecting inhibitory interactions between neuronal populations that represent facial features associated with threat versus safety.