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Multiscale Network Dynamics

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. The focus of the 2017 MIND summer program was on understanding network dynamics at multiple scales, from circuit, whole-brain, and social network levels. Themes running through the curriculum include open tools and data, data visualization, statistical modeling, and model comparison.


Yoni Ashar, Iva Brunec, Shannon Burns, Jin Cheong, Sherry Chien, Samantha Cohen, Rose Cooper, Rebecca Cutler, Elizabeth DuPre, Steven Greening, Nathaniel Haines, Aaron Heller, Siti Ikhsan, Eshin Jolly, Seth Koslov, Yuan Chang Leong, Grace Leslie, Yunzhe Liu, Feilong Ma, Lisa Musz, Gina Notaro, Anuya Patil, Rui Pei, Kristina Rapuano, Harrison Ritz, Beau Sievers, Ben Smith, Ryan Stolier, Youki Tanaka, Vincent Taschereau-Dumouchel, Mark Thornton, Matteo Visconti di Oleggio Castello, Mai-Anh Vu, Kirsten Ziman


Chris Baldassano

Columbia University

Luke Chang

Dartmouth College

Janice Chen

Johns Hopkins University

Sam Gershman

Harvard University

Caterina Gratton

Northwestern University

Howard Eichenbaum

Boston University

Yaroslav Halchenko

Dartmouth College

James Haxby

Dartmouth College

Christopher Honey

Johns Hopkins University

Caleb Kemere

Rice University

Jeremy Manning

Dartmouth College

Ida Momennejad

Princeton University

Alireza Soltani

Dartmouth College

Matthijs van der Meer

Dartmouth College

Thalia Wheatley

Dartmouth College


Scientific Computing

by: Luke Chang

Jupyter Notebooks

by: Eshin Jolly


by: Jeremy Manning

High Performance Computing

by: John Hudson

Howard Eichenbaum Tribute

by: Matt van der Meer

Topgraphic Latent Source Analysis

by: Sam Gershman

Model Fitting

by: Sam Gershman

Reinforcement Learning

by: Alireza Soltani


by: Jim Haxby

fMRI Decoding

by: Luke Chang

Spike Decoding

by: Caleb Kemere

Time Scales

by: Chris Honey

Shared Memory

by: Janice Chen

Successor Representations

by: Ida Momennejad

Social Connection

by: Thalia Wheatley

Functional Connectivity

by: Caterina Gratton

Social Networks

by: Ida Momennejad

Hidden Markov Models

by: Chris Baldassano

Topographic Factor Analysis

by: Jeremy Manning