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.

Participants

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

Faculty

                
Chris BaldassanoColumbia University

Luke ChangDartmouth College

Janice ChenJohns Hopkins University

Sam GershmanHarvard University

Caterina GrattonNorthwestern University

Howard EichenbaumBoston University

Yaroslav HalchenkoDartmouth College

James HaxbyDartmouth College

Christopher HoneyJohns Hopkins University

Caleb KemereRice University

Jeremy ManningDartmouth College

Ida MomennejadPrinceton University

Alireza SoltaniDartmouth College

Matthijs van der MeerDartmouth College

Thalia WheatleyDartmouth College

Tutorials

                
Scientific Computingby: Luke Chang

Containersby: Matteo Visconti di Oleggio Castello

Jupyter Notebooksby: Eshin Jolly

Hypertoolsby: Jeremy Manning

High Performance Computingby: John Hudson

Howard Eichenbaum Tributeby: Matt van der Meer

Topgraphic Latent Source Analysisby: Sam Gershman

Model Fittingby: Sam Gershman

Reinforcement Learningby: Alireza Soltani

Hyperalignmentby: Jim Haxby

fMRI Decodingby: Luke Chang

Spike Decodingby: Caleb Kemere

Time Scalesby: Chris Honey

Shared Memoryby: Janice Chen

Successor Representationsby: Ida Momennejad

Social Connectionby: Thalia Wheatley

Functional Connectivityby: Caterina Gratton

Social Networksby: Ida Momennejad

Hidden Markov Modelsby: Chris Baldassano

Topographic Factor Analysisby: Jeremy Manning