When performing canine operant conditioning studies, the delivery of the reward can be a limiting factor of the study. While there are a few commercially available options for automatically delivering rewards, they generally require manual input, such as using a remote control, in accordance with the experiment script.
Harvey Lab miniaturized mouse VR rig for head-fixed virtual navigation and decision-making tasks.
The VR setup is comprised of several independent assemblies:
The screen assembly: a laser projector projects onto a parabolic screen surrounding the mouse.
FastTrack is an open-source cross-platform tracking software. Easy to install and easy to use, it can track a large variety of systems from active particles to animals, with a known or unknown number of objects.
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).