PiDose is an open-source tool for scientists performing drug administration experiments with mice. It allows for automated daily oral dosing of mice over long time periods (weeks to months) without the need for experimenter interaction and handling.
pyControl is a system of open source hardware and software for controlling behavioural experiments, built around the Micropython microcontroller.
pyControl makes it easy to program complex behavioural tasks using a clean, intuitive, and flexible syntax for specifying tasks as state machines.
SLEAP (Social LEAP Estimates Animal Poses) is a multi-animal pose tracker based on deep learning. It is the successor of LEAP (Pereira et al., Nature Methods, 2019) and was designed to deal with the problem of tracking body landmarks of multiple freely interacting animals.
PiVR is a system that allows experimenters to immerse small animals into virtual realities. The system tracks the position of the animal and presents light stimulation according to predefined rules, thus creating a virtual landscape in which the animal can behave.
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.
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.
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).
Bonsai is a high-performance, easy to use, and flexible visual programming language for designing closed-loop neuroscience experiments combining physiology and behaviour data.
Bonsai has allowed scientists with no previous programming experience to quickly develop their own experimental rigs and is also being increasingly used as a platform to integrate new open-source hardware and software from the experimental neuroscience community.
Ethoscopes are machines for high-throughput analysis of behavior in Drosophila and other animals.
Ethoscopes provide a software and hardware solution that is reproducible and easily scalable.
They perform, in real-time, tracking and profiling of behavior by using a supervised machine learning algorithm, are able to deliver behaviorally triggered stimuli to flies in a feedback-loop mode, and are highly customizable and open source.
BonVision is an open-source closed-loop visual environment generator developed by the Saleem Lab and Solomon Lab at the UCL Institute of Behavioural Neuroscience in collaboration with NeuroGEARS.
BonVision’s key features include: