Topic: DeepLabCut

Advances in computer vision and deep learning have made it possible to gather markerless pose estimation data from consumer-grade cameras. Accessible software tools, such as DeepLabCut, have made it possible to include behavioral pose as one data modality in larger neuroscience projects. Staying at the leading edge of neuroscience requires significant data engineering and computational programming skills to handle day-to-day data operations in order to integrate across multiple modalities, each with separate analysis packages. Together, these increase the data management and synchronization bu

The development of algorithms for high-accuracy animal tracking is fundamental to unravel the neural basis of behavior. The recent growth of Deeplabcut, an open source toolbox for marker-less pose estimation based on transfer learning with deep neural networks, promises to greatly enhance our ability to track animal pose with high-accuracy and high-efficiency, because it can match human labelling accuracy with minimal training data. Here, we implement Deeplabcut routines and develop a pipeline to accurately track the pose of mice undergoing defensive behaviors. Defensive behaviors are elicited

Who says you can't reinvent the wheel?! This running wheel for head-fixed mice allows 3D reconstruction of body kinematics using a single camera and DeepLabCut (or similar) software. A lightweight, transparent polycarbonate floor and a mirror mounted on the inside allow two views to be captured simultaneously. All parts are commercially available or laser cut

DeepLabStream is a python based multi-purpose tool that enables the realtime tracking and manipulation of animals during ongoing experiments. Our toolbox was orginally adapted from the previously published DeepLabCut (Mathis et al., 2018) and expanded on its core capabilities, but is now able to utilize a variety of different network architectures for online pose estimation (SLEAP, DLC-Live, DeepPosekit's StackedDenseNet, StackedHourGlass and LEAP). Our aim is to provide an open-source tool that allows researchers to design custom experiments based on real-time behavior-dependent feedback. My