[Comp-neuro] 2017 Methods in Neuroscience at Dartmouth Computational Summer School

Matthijs van der Meer mvdm at dartmouth.edu
Mon Mar 13 16:34:14 CET 2017

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

August 13th - 20th, 2017


We are delighted to welcome applications for our inaugural Methods in
Neuroscience at Dartmouth (MIND) Computational Summer School. 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

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)
   Nicholas Christakis (Yale 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)
   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

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

This event is sponsored by the Center for Social Brain Sciences, Center
for Cognitive Neuroscience, and Neukom Institute for Computational
Sciences at Dartmouth.

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