Computational simulations and data analysis show that random clustering in the connections of neurons receiving minimal external cues can generate place fields and their spontaneous activation in trajectory-like sequences.

Generation of a home-and-rescue gene drive targeting an essential gene reveals confinable population modification.

Neuronal recordings reveal complex, heterogeneous inputs to face-selective neurons, suggesting that inferotemporal neurons do not represent objects in isolation, but are also sensitive to object relationships that reflect environmental regularities.

Analysis of human fMRI data reveal that intermediary areas within the fronto-parietal control network (FPCN) are critical for integrating control processing, cognitive ability, and amenability to neuromodulation.

Complex motor behaviors can be assembled from simpler repeating elements controlled by pattern-generation circuits operating at different time scales.

Signals recorded directly from human anterior temporal cortex reveal that the brain represents animacy information using a distributed code that changes radically as a stimulus is processed, as predicted by an artificial neural network model.

Neural representations of stimulus-specific information increase in fidelity as the power of alpha/beta activity decreases, suggesting that alpha/beta power decreases reflect a domain-general mechanism that supports information representation.

Deep Learning Artificial Intelligence tools can be developed to accurately identify cell divisions during development and wound repair of epithelial tissue.

Theory explains how transport of gene expression vortices by cell advection may cause intermingled defective and normal segments along the body axis during resynchronization experiments in the zebrafish segmentation clock.