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Ten popular spike sorting codes are reproducibly benchmarked for accuracy on electrophysiology datasets from eleven laboratories with interactive web-based exploration of thousands of ground-truth units.

Creating structural models with multiple conformations of proteins improves the fit of the model to experimental data X-ray crystallography and improve the model geometry.

A new automated system for recording and analyzing neural activity in behaving animals over months-long time-scales offers new perspectives on how neural circuits underlie processes such as learning, development, and recovery from brain injury.

A machine learning model, TweetyNet, make it possible to scale up annotation of birdsong across individual birds, days of song, and species.

A fully automated workflow for high-throughput mouse behavioral and optogenetic experiments in homecage reveals involvement of brain regions in tactile decision-making.

The detection of chemical synapses in 3-dimensional electron microscopy data has been automated such that synapses in large-scale connectivity maps of the cerebral cortex, connectomes, can be charted without the need for human interaction.

An open-source user-friendly toolbox implementing machine learning for single-molecule FRET analysis enabling experts and non-experts to reproducibly provide dynamic structural biology insights.

A set of stochastic processes reproduces the mouse exploration patterns in an automated variant of the Barnes maze.

An innovative, fully computerized approach for measuring spontaneous social behavior in mice closely follows murine ethology, eliminates crucial sources of data irreproducibility and enables fast, inexpensive assessment of sociability in group-housed subjects.

SerialFIB is an adaptable open-source software designed for streamlining, advanced development and automation of cryo-focused ion beam-based cellular preparations for in situ cryo-electron tomography.