Vision

There is a growing gap between how graduate students in psychology and neuroscience are trained and what they actually need to know to do cutting edge work. We see two fundamental issues driving this gap. First, most training programs do not expose students to the latest computational tools. Second, an even greater challenge is to supplement the traditional reductionist approach to studying the elements of brain, cognition, and behavior in isolation, to integrating how these elements interact as a cohesive complex system. This entails considering not just which elements in a network interact, but also the content of the interaction, and the dynamics of how this information flows through the network over time. This general issue is present in multiple domains, with an accompanying need for similar tools: neurophysiologists studying spiking activity in ensembles of single neurons, cognitive neuroscientists studying whole-brain activity levels, and social psychologists studying group interactions. Thus, our summer program aims to provide integrated training of network methods at the circuit, whole-brain, and social network levels. The overall format has short lectures in the morning, followed by hands-on tutorial-style labs, and a hackathon in which students will collaboratively work on projects with faculty. Themes running through the curriculum include open tools and data, data visualization, statistical modeling, and model comparison.

Schedule

Day 1: Computing Stack and Setup

GitHub, Jupyter, d3, Graphviz, hypertools

Day 2: Project Brainstorming

Types & availability of data, Example projects

Day 3: Modeling Basics

Model comparison, hierarchical Bayes, Dirichlet processes

Day 4: Decoding

Bayesian decoding, MVPA, hyperalignment

Day 5: Network Basics

Descriptive statistics, inferring network structure

Day 6: Connectivity

Granger, DCM, phase slopes

Day 7: Dynamics

Change points, Markov chains, HMMs

Day 8: Presentations

-- the final list of topics will be chosen based on applicant interests; several topics will feature parallel beginner and advanced tracks --

Faculty

Chris Baldassano

Princeton University

Luke Chang

Dartmouth College

Janice Chen

Johns Hopkins University

Nicholas Christakis

Yale University

Howard Eichenbaum

Boston University

Sam Gershman

Harvard University

Caterina Gratton

Washington University

Yaroslav Halchenko

Dartmouth College

James Haxby

Dartmouth College

Christoher Honey

Johns Hopkins University

Caleb Kemere

Rice University

Jeremy Manning

Dartmouth College

Matthijs van der Meer

Dartmouth College

Ida Momennejad

Princeton University

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

Todd Heatherton

Dartmouth College

Dan Rockmore

Dartmouth College

Thalia Wheatley

Dartmouth College

Interested in participating?