Methods in Neuroscience at Dartmouth (MIND) 2017 Computational Summer School

We are delighted to announce our inaugural Methods in Neuroscience at Dartmouth (MIND) Computational Summer School, to be held August 13-20. The theme of this year's summer school is Network Dynamics at Multiple Spatiotemporal Scales. 

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.  In addition, there is increasing interest in supplementing 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 networks 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.

Our curriculum is motivated by the realization that there is a common core of computational methods that apply across different subfields of neuroscience, with exciting opportunities for crossover across these subfields.

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.

The summer school will be taught by faculty with unique expertise in using innovative computational techniques to understand network dynamics at multiple scales.

  • Chris Baldassano (Princeton University)
  • Luke Chang (Dartmouth College)
  • Janice Chen (Johns Hopkins University)
  • Howard Eichenbaum (Boston University)
  • Sam Gershman (Harvard University)
  • Caterina Gratton (Washington 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)

Topics include:

  • Open source computing resources
  • Estimation and hypothesis testing with computational modeling
  • Representing and describing networks
  • Identifying structure in networks through connectivity
  •  Characterizing the temporal dynamics of networks
  • Identifying representations of information using decoding techniques

The application deadline is April 15th, 2017.

Information about how to apply can be found on our website

Luke Chang, Jeremy Manning, and Matt van der Meer (Directors)

Jim Haxby,  Thalia Wheatley, Todd Heatherton, and Dan Rockmore (Advisory Committee)