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A neural network showed better prediction of upcoming states when it was selective in when it encoded and retrieved episodic memories, thereby explaining why humans show this selectivity in studies of naturalistic memory.

A theory of memory consolidation illustrates the benefits of communication between short- and long-term memory systems to prioritize the storage of reliable memories.

A two-part neural network models reward-based training and provides a unified framework in which to study diverse computations that can be compared to electrophysiological recordings from behaving animals.

A novel automated behavior analysis method for Hydra identifies pre-defined and new behavior types, and reveals a stable behavior repertoire.

A computational neural network model leverages a simple unsupervised learning principle to account for recent findings on when memories move apart (differentiate) or together (integrate) in the brain.

Triggered activity bursts in place cells can increase and decrease the strength of some inputs.

Protracted systemic and microvascular inflammation, as well as high anti-SARS-CoV-2 antibody levels, is associated with a high risk of persistent structural and functional lung deficits 6 months after COVID-19.

Mice learn a categorization task faster when exposed to task-relevant stimuli outside training, supporting neural-network models in which unsupervised learning first captures the statistical structure of inputs before classifying them.

Easy-to-use image analysis software enables single cell quantitation of cell types and division rates in complex 3D tissues including living Drosophila brains, mouse embryos and Zebrafish organoids.

BehaviorDEPOT is a general purpose behavior analysis software that will meet the needs of thousands of behavioral neuroscientists who need accurate, flexible open-source software to analyze naturalistic behaviors and align then with neural data.