Oct 10-13, 2022
7:45 am - 8:30 pm EST
Over the past few years, behavior quantification and modeling has experienced an explosion of innovation and discovery largely enabled by application of new machine learning methods. These methods have enabled the quantification of behavior at high temporal and spatial resolution, and in concordance with simultaneous measurement and manipulation of neural and genetic function. However, access to this revolutionary technology is limited primarily due to a lack of adequate resources and training. Democratization of this technology through training of the next generation of scientists is necessary to elevate the field of quantitative behavior. The Short Course on the Application of Machine Learning for Automated Quantification of Behavior will disseminate the theoretical and technical knowledge of this field, and train researchers to apply machine learning methods to behavior quantitation and modeling. Our goal is to build an educational program that fosters productive and interactive dialogue, teaches proper methodology, and provides support structure to nurture and lower the barrier of entry into this nascent field. The course will:
Read more about featured speakers and organizers here.
Who: This course is appropriate for early career researchers from the fields of neuroscience, genetics, and biomedical research. Prerequisites: Basic scripting or programming knowledge is suggested but not required.
Where: 600 Main Street, Bar Harbor, Maine. Get directions with OpenStreetMap or Google Maps.
When: Oct 10-13, 2022. Add to your Google Calendar.
Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.). They should have a few specific software packages installed (listed below).
Accessibility: We are committed to making this workshop accessible to everybody. For workshops at a physical location, the workshop organizers have checked that:
Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.
Contact: Please email to-be-announced for more information.
Course description: To learn more about this course, refer to the course page at JAX.
| 08:00 | What is Behavior? Gordon Berman, Ph.D., Emory University |
| 09:00 | Keynote: Using Machine Vision and Learning to Analyze Animal Behavior Kristin Branson, Ph.D., Janelia Research Campus |
| 10:00 | Coffee/tea |
| 10:15 | Deep Learning-Based Pose Tracking Talmo Pereira, Ph.D., The Salk Institute for Biological Studies |
| 11:15 | Behavioral Analysis with Deep Learning Alexander Mathis, Ph.D., Swiss Federal Institute of Technology |
| 12:15 | Lunch |
| 1:00 | Special activities |
| 3:00 | DeepLabCut tutorial |
| 4:30 | Social LEAP Estimates Animal Poses (SLEAP) tutorial |
| 6:00 | Dinner |
| 7:00 | Evening discussion |
| 8:30 | End |
| 08:00 | Keynote: Interrogating Neural Mechanisms Underlying Spontaneous Behavior Using MoSeq Sandeep Robert Datta, M.D., Ph.D., Harvard Medical School |
| 09:00 | Measuring Behavior Across Scales Gordon Berman, Ph.D., Emory University |
| 10:00 | Coffee/tea |
| 10:15 | Keynote: Quantifying and Modeling Social Behaviors Mala Murthy, Ph.D., Princeton University |
| 11:15 | Sparse Sensing and Control in Agile Movements Bing Brunton, Ph.D., University of Washington, Seattle |
| 12:15 | Lunch |
| 1:00 | Special activities |
| 3:00 | Using MotionMapper for Unsupervised Behavioral Analysis |
| 6:00 | Dinner |
| 7:00 | Evening discussion |
| 8:30 | End |
| 08:00 | Latent States of Neural Activity and Behavior Scott Linderman, Ph.D., Stanford University |
| 09:00 | Neurobiology of individuality Benjamin De Bivort Ph.D., Harvard University |
| 10:00 | Coffee/tea |
| 10:15 | Leveraging Lingual Dexterity to Study Error-Driven Motor Control Jesse Goldberg, Ph.D., Cornell University |
| 11:15 | Applications of Machine Vision to Preclinical Mouse Models Vivek Kumar, Ph.D., The Jackson Laboratory |
| 12:15 | Lunch |
| 1:00 | Special activities |
| 3:00 | RCR practices in BBD and BBD |
| 6:00 | Dinner |
| 7:00 | Evening discussion |
| 8:30 | End |
| 08:00 | Detailed Behavioral Tracking and its Applications in Neuroscience Bence Olveczky, Ph.D., Harvard University |
| 09:00 | The Application of Machine Learning for Automated Quantification of Behavior Ishmail Adbus-Saboor, Ph.D., University of Pennsylvania |
| 10:00 | Coffee/tea |
| 10:15 | Quantifying Behavior in Dyadic Social Interactions Ann Kennedy, Ph.D., Northwestern University |
| 11:15 | Quantifying Worm Behaviour in Multiwell Plates: A Few Challenges, Lots of Opportunities Andre Brown, Ph.D., Imperial College London |
| 12:15 | Lunch |
| 1:00 | Special activities |
| 3:00 | Extracting Features and Detecting Actions in Pose Datasets (Python notebook) |
| 4:40 | Supervised Behavior Analysis (Python notebooks) (documentation) |
| 6:00 | Dinner |
| 7:00 | Evening discussion |
| 8:30 | End |
View a more detailed schedule here.
To participate in a workshop, you will need access to software as described below. In addition, you will need an up-to-date web browser.
We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.
Python is a popular language for research computing, and great for general-purpose programming as well. Installing all of its research packages individually can be a bit difficult, so we recommend Anaconda, an all-in-one installer.
Regardless of how you choose to install it, please make sure you install Python version 3.x (e.g., 3.6 is fine).
We will teach Python using the Jupyter Notebook, a programming environment that runs in a web browser (Jupyter Notebook will be installed by Anaconda). For this to work you will need a reasonably up-to-date browser. The current versions of the Chrome, Safari and Firefox browsers are all supported (some older browsers, including Internet Explorer version 9 and below, are not).
bash Anaconda3-and then press Tab to autocomplete the full file name. The name of file you just downloaded should appear.
yes and press enter to approve the license.
Press Enter (or Return)
to approve the default location
for the files.
Type yes and press
Enter (or Return)
to prepend Anaconda to your PATH
(this makes the Anaconda distribution the default Python).