[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
G-NODE ADVANCED COURSE ON NEURAL DATA ANALYSIS 2017
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
quantitatively.
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).
DATE AND VENUE
March 26 - April 8, 2017
Haus Overbach, Juelich-Barmen
and Research Center Juelich, Germany
COURSE INSTRUCTORS
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
KEYNOTE SPEAKERS
Alain Destexhe, CNRS, France
Yifat Prut, Hebrew Univ Jerusalem, Israel
TOPICS COVERED
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
REQUIREMENTS
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.
COURSE FEE
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.
HOUSING
Accommodation in 2-bed rooms for students will be provided at the course
site.
HOW TO APPLY
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>.
DEADLINES
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.
ORGANIZERS
Sonja Grün, Juelich Research Center and RWTH Aachen Univ, Germany
Martin Nawrot, University of Cologne, Germany
Thomas Wachtler, G-Node, Ludwig-Maximillians-Universität München,
Germany
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