ZebraZoom: Tracking of freely swimming zebrafish larvae.
ZebraZoom works for a wide range of different experimental conditions and for both larvae and adult fish
Tail tracking of head-embedded larvae
We’ve combined ZebraZoom with DeepLabCut to track head-embedded (in agar) larvae with poor light conditions
Automatic detection of rollovers using Deep Learning and TensorFlow
Custom-made analysis of thousands of bouts
We’ve used supervised and unsupervised machine learning to automatically classify bouts into distinct behavior
Clusters are detected automatically with unsupervised machine learning. Cluster 1: Slow Forward Swims. Cluster 2: Small Amplitude Turns. Cluster 3: Large Amplitude Turns. Cluster 4: Burst Swims
Graphical User Interface to explore and validate tracking results:
After publishing the first version of ZebraZoom, we’ve kept improving our software to make it faster, more reliable, adaptable to wide range of experimental conditions, and able to track both larvae and adult fish. We’ve also built a graphical interface that enables to both explore the results and efficiently check that the tracking and data extraction worked appropriately.
We’ve combined ZebraZoom with DeepLabCut to track the tail of head-embedded larvae in poor lighting conditions. We’ve also used deep learning and TensorFlow to automatically detect rollovers.
We have extensive experience analyzing and making sense of the thousands of bouts extracted by our software and we know how to use machine learning to classify bouts into distinct behaviors.
We’ve used C++, Python, Matlab, OpenCv, TensorFlow, DeepLabCut, scikit-learn and more. We are constantly learning new computer vision, data science, machine learning, and software engineering skills. We combine those skills with our biology and neuroscience knowledge to always push further our ability to efficiently analyze animal behavior.
Video Credits: Claire Wyart, Feng Quan, Mingyue Wu, Laura Desban, Martin Carbó-Tano