The Summer Workshop on the Dynamic Brain is an intensive, project-based residential course with a focus on the neurobiology of sensory processing, coding, and neural population dynamics. This program is intended for researchers at the graduate and postdoctoral level with an interest in developing the intersection of their scientific knowledge and their computational skills.

Founded by Adrienne Fairhall and Christof Koch, the Summer Workshop on the Dynamic Brain is co-hosted by the Allen Institute for Brain Science and the Computational Neuroscience Center at the University of Washington with additional support from the Simons Foundation’s Collaboration for the Global Brain. It is directed by Drs. Michael A. Buice, Saskia de Vries, Adrienne Fairhall, Shawn Olsen, and Eric Shea-Brown.

The workshop is geared towards graduate students and postdoctoral fellows in neuroscience, biology, physics, engineering, and computer science as well as other early-career researchers at the equivalent level. Ideal candidates have a planned or established research agenda related to the course topics.

Workshop Details

Workshop dates: August 21 – September 4, 2022

Optional Python boot camp: August 20-21, 2022

Application deadline: Tuesday, February 1, 2022, at 11:59pm Pacific Time

Preference will be given to advanced graduate students and postdocs. Applicants must have completed an undergraduate degree to be considered for the workshop. International applications are welcome.

Cost: Conference fees, housing and meals during the workshop will be covered by the organizers. Additional expenses, including transportation to and from Seattle and incidentals, are the responsibility of the attendee. There is no additional cost to participate in the Python boot camp.

Applicant notification: All applicants will be notified of the decision on their application by April 1, 2022.

By the end of the course, students will have:

• Developed an understanding of the neuroanatomy and neurophysiology of visual coding.

• Learned about optical and neurophysiological methods for measuring cell populations and about the analysis and interpretation of such data in visual coding.

• Developed their skills in developing data models, coding, and big data approaches.

• Attended lectures by Allen Institute for Brain Science staff and visiting instructors on current research in visual coding and neural dynamics.

• Completed a collaborative project with other workshop students using open datasets from the Allen Institute for Brain Science, with the potential to extend the project beyond the workshop as an ongoing collaboration with other students or as part of a dissertation.

  Throughout the course, students attend lectures by Allen Institute for Brain Science staff and additional visiting instructors. Lecture topics include neuroanatomy and neurophysiology of cortex, neuronal cell types and cortical layers, connectomics, optical and electrophysiological methods for measuring cell populations, theories and modeling of neocortex and associated structures, big data approaches, and perceptual and behavioral neuroscience, with a focus on vision.

  All tutorials and provided software packages use the Python programming language. The workshop includes an optional Python bootcamp and ongoing software tutorials in modeling, data science approaches, and statistical analysis implementation. Previous experience with Python is not required or expected. It is highly recommended that students have some previous coding experience.

  Students will have the opportunity to receive training with featured large-scale datasets generated by the Allen Institute for Brain Science, including the Allen Brain Observatory Visual Coding and Neuropixels datasets. Other Allen Institute resources are available for students to explore, including pre-release data. datasets featured in previous workshops, and modeling tools.

  A core feature of the workshop curriculum is a computational neuroscience/data science research project. With faculty guidance, students collaborate to develop a project that explores these open data sets. The students present their project proposals at the end of the first week of the course and the outcome of their work at the conclusion of the workshop to faculty and fellow participants. Previous projects from this course have been developed into publications and presented at scientific conferences.