DataJoint Elements is a growing compilation of community-curated, open-source software modules for building automated data pipelines and analysis workflows for neuroscience experiments. DataJoint Elements enables: Secure tracking of animal subjects, equipment, and procedures Automatic data ingestion Customized research workflows Faster deployment Improved reproducibility Greater continuity across projects Secure tracking.
A search for magnetosensitive neurons Many species across the animal kingdom are said to use the Earth’s magnetic field for orientation and navigation. However, the mechanisms by which information about magnetic fields enters the nervous system remain unknown.
Video capture is increasingly necessary for neuroscience research where neural and behavioral data are synchronized to reveal correlative and causal relationships. This relies on a recording system that can capture quality videos without significant alterations to preexisting experimental conditions (e.
DataJoint Core is an open-source toolkit for defining and operating computational data pipelines (i.e., sequences of steps for data acquisition, processing, and transformation). Pipelines built in DataJoint Core offer: Efficient design with intuitive queries Automated, reproducible computation with full referential integrity Coordination of multiple human and computer workers Flexibility to adapt and change DataJoint Core includes libraries for Python and MATLAB, a REST API, and GUI tools for data entry and visualizations.
SqueakR is an open-source R package, available on CRAN, which streamlines bioacoustics research through automated data processing and visualizations for rodent vocalizations exported from DeepSqueak. These functions are harnessed through the ‘SqueakR’ Shiny Dashboard, available as a function within the package, which can be used to visualize experimental results and analyses, as well as conduct statistical significance test between call features across groups.
While accurate behavioral state classification is critical for many research applications, it is often done manually, which can be both tedious and inaccurate. Here we present a novel artificial neural network that uses electrophysiological features to automatically classify behavioral state in rats with high accuracy, sensitivity, and specificity.
ArControl is a Arduino based digital signals control system. A special application for ArControl is to establish a animal behavioral platform (as Skinner box), which control devices to deliver stimulation, monitor behavioral response and record data.
Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel.
BrainJ enables high-throughput analysis of serial tissue sections imaged using confocal or widefield techniques. Developed in Fiji, our approach leverages freely available tools for machine learning pixel classification for cell detection and mesoscale mapping of axons and dendrites.
FastSurfer is a fast and extensively validated deep-learning pipeline for the fully automated processing of structural human brain MRIs. As such, it provides FreeSurfer conform outputs, enables efficient big-data analysis for large cohort studies, and time-critical clinical applications such as structure localization during image acquisition or rapid extraction of quantitative measures for precision medicine.