Several excellent computational frameworks exist that enable high-throughput and consistent tracking of freely moving unmarked animals. SimBA introduce and distribute a plug-and play pipeline that enables users to use these pose-estimation approaches in combination with behavioral annotation for the generation of supervised machine-learning behavioral predictive classifiers.
Neurodata Without Borders is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build analysis tools for neurophysiology data. NWB is designed to store a variety of neurophysiology data, including data from intracellular and extracellular electrophysiology experiments, data from optical physiology experiments, and tracking and stimulus data.
OpenDrop a modular, open source digital microfludics platform for research purposes. The device uses recent electro-wetting technology to control small droplets of liquids. Potential applications are lab on a chip devices for automating processes of digital biology.
Stytra, a flexible, open-source software package, written in Python and designed to cover all the general requirements involved in larval zebrafish behavioral experiments.
It provides timed stimulus presentation, interfacing with external devices and simultaneous real-time tracking of behavioral parameters such as position, orientation, tail and eye motion in both freely-swimming and head-restrained preparations.
We present an open-source anthropomorphic robot hand system called HRI hand. Our robot hand system was developed with a focus on the end-effector role of the collaborative robot manipulator. HRI hand is a research platform that can be built at a lower price (approximately $500, using only 3D printing) than commercial end-effectors.
cellfinder is software from the Margrie Lab at the Sainsbury Wellcome Centre for automated 3D cell detection and registration of whole-brain images (e.g. serial two-photon or lightsheet imaging).
It’s a work in progress, but cellfinder can:
DeepLabCut™ is an efficient method for 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames).
MNE is a software package for processing electrophysiological signals primarily from magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings, and more recently sEEG, ECoG and fNIRS. It provides a comprehensive solution for data preprocessing, forward modeling (with boundary element models), distributed source imaging, time–frequency analysis, non-parametric multivariate statistics, multivariate pattern analysis, and connectivity estimation.
Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.
The fruit fly, Drosophila melanogaster, continues to be one of the most widely used model organisms in biomedical research.
Though chosen for its ease of husbandry, maintaining large numbers of stocks of fruit flies, as done by many laboratories, is labour-intensive.