[Neuroinfo] G-Node Advanced Course on Neural Data Analysis 2017

Thomas Wachtler wachtler at biologie.uni-muenchen.de
Fri Dec 2 14:22:49 CET 2016

March 26 - April 8, 2017
Haus Overbach, Juelich-Barmen, Germany

Techniques to record neuronal data from single neurons and population of
neurons are rapidly improving. Meanwhile recordings are possible from
hundreds of channels simultaneously while animals perform complex tasks.
Thus also the analysis of such data becomes increasingly challenging.
This advanced course aims at providing deeper insights in
state-of-the-art questions in neuroscience, analysis approaches and how
to formalize questions to neuronal data so they can be answered

The course is addressed to excellent master and PhD students and young
researchers who are interested in learning advanced techniques in data
analytics and in getting hands-on experience in the analysis of
electrophysiological data (multiple-parallel spike trains and local
field potentials). In the first week of the course, international
researchers will give lectures on statistical data analysis and data
mining methods with accompanying exercises. In the second week the
participants will analyze provided data on their own, with self-written
code and/or by use of provided tool boxes. Participants are required to
have a strong interest in data analysis, a background in mathematics or
related fields, knowledge on algebra, matrix operations, and statistics,
and need to have solid programming experiences (preferably in Python). 

March 26 - April 8, 2017
Haus Overbach, Juelich-Barmen
and Research Center Juelich, Germany 

  Moshe Abeles, Bar-Ilan Univ, Israel
  Jürgen Dammers, Juelich Research Center, Germany
  Michael Denker, Juelich Research Center and RWTH Aachen Univ, Germany
  Sonja Grün, Juelich Research Center and RWTH Aachen Univ, Germany
  Martin Nawrot, University of Cologne, Germany
  Thomas Wachtler, G-Node, LMU Munich, Germany
  Byron Yu, Carnegie Mellon Univ Pittsburgh, USA 

  Alain Destexhe, CNRS, France
  Yifat Prut, Hebrew Univ Jerusalem, Israel

Single neuron properties and statistics · Modeling stochastic processes
· Surrogate methods · Detection of spatio-temporal patterns ·
Statistical analysis of massively parallel spike data · Higher-order
correlation analyses · Spike-LFP relationship · Population coding ·
State space analysis · Machine learning · Artifact rejection · MEG
source localization · Data mining · Data management, reproducibility,
data sharing · MNE toolbox, Elephant toolbox 

Applicants should be familiar with linear algebra, probability,
differential and integral calculus and experienced using Python or
Matlab. Preparatory reading material will be provided. Students should
bring their own laptops and should be able to install software on their
system. Students that do not have a suitable laptop should indicate this
immediately after acceptance for the course. We will be able to provide
a small number of laptops for the time of the course. 

A course fee of 1.000 Euros will be charged to accepted students. The
course fee covers accommodation and meals, including coffee breaks. A
few stipends will be available to support students with documented need
of funding. 

Accommodation in 2-bed rooms for students will be provided at the course

The application should include · a letter of motivation (max 1 page) ·
curriculum vitae (please indicate the relevant courses you have taken) ·
description of programming experience · a letter of recommendation.
Please send all documents as PDF to <advanced-course at g-node.org>.

Applications must be received by JANUARY 1, 2017. Early application is
encouraged because number of participants is limited. Notifications of
acceptance will be given by mid January 2017. 

  Sonja Grün, Juelich Research Center and RWTH Aachen Univ, Germany
  Martin Nawrot, University of Cologne, Germany
  Thomas Wachtler, G-Node, Ludwig-Maximillians-Universität München,
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