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Dynamic Brain Circuits DeepLabCut Workshop Research Talks
2:00pm-4:00pm, Mar 25, 2021
UBC Brain Circuits Cluster
On March 25th, from 3-5 pm, the Dynamic Brain Circuits cluster from The University of British Columbia in collaboration with the ICORD Trainee Committee and the BCCHRI TOG will host a virtual zoom workshop on DeepLabCut. DLC is an efficient method for 3D markerless pose estimation and obviates much of the time intensive hand labeling of behavioural data in neuroscience experiments. DLC is based on transfer learning with deep neural networks that achieves excellent results working on minimal training data, about 20-200 frames. There will be four speakers focusing on various use cases such as behaviour classification and synthetic data. Following this, there will be a guided tutorial on the use of DLC with sample data.
Video Located Here: https://www.youtube.com/watch?v=j_eUHo7GPyI&t=11s
At the end you will leave with a good understanding of DLC and its potential! Link to Instructions: https://drive.google.com/file/d/19XBUjUUkowJYn7gbEQwauwIiewdRda6w/view
This has video has presentations from the following researchers actively working with DLC in their projects:
Adrian Lindsay is a PhD student in the Seamans Lab and serves as a tutor for the Brain Circuits cluster. His work uses a combination of electrophysiology, computer vision, and artificial neural networks to explore how motor signals and behaviour are encoded in the prefrontal cortex.
Dongsheng Xiao trained as a doctor mainly engaged in the research and treatment of Parkinson’s disease and other functional brain disorders using deep brain stimulation (DBS). As a PDF in Tim Murphy's lab, his research interests are in discovering brain circuits that underlie sensorimotor integration and motor functions. He is also involved in cultivating artificial intelligence and computer vision to automate exploratory mining of our rich neural and behavior video datasets.
Ellen Koch is a PhD Candidate in the Raymond lab and Canadian Open Neuroscience Platform Scholar. Her project focuses on studying how cortico-striatal signaling is altered in Huntington's disease, and how these neuropathological changes contribute to motor behaviour and motor learning deficits. She explores this using a combination of in vitro and in vivo methods, including fiber photometry.
Hao Hu is a Ph.D. student in the Murphy lab. His project focuses on studying how to quantify the correlation between behavior and brain activities. He explores this using recent machine learning techniques to learn the representation of behavioral time sequence at a low dimensional manifold.