Deeplabcut
DeepLabCut
Modern Approaches to Behavioural Analysis
The goal of neuroscience is to understand how the nervous system controls behaviour, not only in the simplified environments of the lab, but also in the natural environments for which nervous systems evolved. In pursuing this goal, neuroscience research is supported by an ever-larger toolbox, ranging from optogenetics to connectomics. However, often these tools are coupled with reductionist approaches for linking nervous systems and behaviour. This course will introduce advanced techniques for measuring and analysing behaviour, as well as three fundamental principles as necessary to understanding biological behaviour: (1) morphology and environment; (2) action-perception closed loops and purpose; and (3) individuality and historical contingencies [1]. [1] Gomez-Marin, A., & Ghazanfar, A. A. (2019). The life of behavior. Neuron, 104(1), 25-36
Measuring the Motions of Mice: Open source tracking with the KineMouse Wheel
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
SimBA for Behavioral Neuroscientists
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. SimBA was developed for the analysis of complex social behaviors, but includes the flexibility for users to generate predictive classifiers across other behavioral modalities with minimal effort and no specialized computational background. SimBA has a variety of extended functions for large scale batch video pre-processing, generating descriptive statistics from movement features, and interactive modules for user-defined regions of interest and visualizing classification probabilities and movement patterns.
DeepLabStream
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 personal ideal goal would be a swiss-army knife like solution where we could integrate the many brilliant python interfaces. We are constantly upgrading DLStream with new features and integrate other open-source solutions.
A computational analysis of second-order conditioning in mice using DeepLabCut
FENS Forum 2024
DeepLabCut 3.0: Efficient deep learning for single and multi-animal pose tracking and identification
FENS Forum 2024