Vision

There is a growing divide between how we train graduate students in psychology/neuroscience and what they actually need to know to do cutting edge work. We see two fundamental issues driving this divide. First, most training programs do not expose students to the latest computational tools. Second, the traditional reductionist approach to studying elements of the brain, behavior, and the environment in effective isolation misses out on key emergent properties of these complex systems that arise only through how they interact.


At last year’s inaugural MIND Computational Summer School, we focused our training program on network dynamics at different spatial scales (cellular, systems, brain-wide, and social networks). This year we will shift our primary focus to narratives and natural contexts -- analyses, experiments, and models that capture the rich spatiotemporal structure of the “real world.” There has been renewed interest in moving the study of neural and psychological processes from highly controlled laboratory studies to more naturalistic contexts. However, this requires developing new methods to develop experiments and analyze data. We are excited to incorporate some of the latest theoretical, methodological, and experimental advances that are advancing progress towards this goal. Our program will feature a mix of expertise at a broad range of spatiotemporal scales and domains (e.g. behavioral, cognitive, social; circuit, whole-brain, and social networks; etc.). In addition, we will maintain our special focus on training students to use and contribute to open-source tools, data sharing, and other “best practices” for open science.

Format

This year’s summer program will be 9 days, with each day organized around three sessions. Morning sessions will begin with lectures that highlight the use of a specific methods within the context of a particular research question. This will be followed by a hands-on laboratory where participants will learn to use the core methods presented in the morning lecture through prepared tutorials and exercises. Finally, afternoon/evenings will provide time for participants to work together with the course instructors to develop new projects inspired by discussions from earlier in the day in “pop-up-labs”. Suggested projects are provided, but will ideally be self-generated by participants and inspired by discussions that arise during the course to test new ideas or develop new methods. Participants will present their projects on the last day of the course.

Schedule

1 | Computing Stack

  • High Performance Computing
  • Git
  • Datalad
  • Containers
  • Jupyter Notebooks
  • Visualization

2 | Project Brainstorming

  • Project Pitches
  • Outdoor Outing

3 | The Importance of Models

  • Encoding & Decoding Models
  • Representational Similarity Analysis
  • Hyperalignment
  • Intersubject Connectivity

4 | Language

  • Topic Modeling
  • Vector Representations

5 | Temporal Organization

  • Time Series Models
  • Frequency Decomposition
  • Markov Chains
  • Hidden Markov Models

6 | Free Day

  • Participants can explore the beautiful upper valley.

7 | Memory

  • Content Models
  • Spatial Models
  • Cognitive Models
  • Multisubject Models

8 | Social Contexts and Conversations

  • Social Networks
  • Synchrony

9 | Project Presentations

  • Participants will present a summary of their Pop Up Lab Project

Faculty

Luke Chang

Dartmouth College

Janice Chen

Johns Hopkins University

Michale Fee

Massachusetts Institute of Technology

Yaroslav Halchenko

Dartmouth College

James Haxby

Dartmouth College

Christoher Honey

Johns Hopkins University

Alex Huth

University of Texas Austin

Caleb Kemere

Rice University

Jeremy Manning

Dartmouth College

Ida Momennejad

Princeton University

Emily Mower Provost

University of Michigan

Carolyn Parkinson

University of California Los Angeles

Alireza Soltani

Dartmouth College

Mark Thornton

Princeton University

Matthijs van der Meer

Dartmouth College

Thalia Wheatley

Dartmouth College

Organizing committee

Luke Chang

Dartmouth College

Jeremy Manning

Dartmouth College

Matthijs van der Meer

Dartmouth College

Courtney Rogers

Dartmouth College

Advisory board

James Haxby

Dartmouth College

Dan Rockmore

Dartmouth College

Thalia Wheatley

Dartmouth College

Interested in participating?