[Comp-neuro] ANDA 2019 - G-Node Advanced Neural Data Analysis Course, April 1-18, 2019, Jülich, Germany

Thomas Wachtler wachtler at biologie.uni-muenchen.de
Mon Jul 30 12:47:25 CEST 2018


April 1 - 18, 2019
Haus Overbach, Juelich-Barmen, Germany 

Techniques to record neuronal data from populations of neurons are
rapidly improving. Simultaneous recordings from hundreds of channels are
possible while animals perform complex behavioral tasks. The analysis of
such massive and complex data becomes increasingly challenging. This
advanced course aims at providing deeper training in state-of-the-art
analysis approaches in systems neuroscience. 

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. Internationally renowned researchers will
give lectures on statistical data analysis and data mining methods with
accompanying exercises. Students will define and perform their own
analyses on provided data to solve a challenge. 

Participants are required to have a strong interest in data analysis, a
background in a mathematical or related field, knowledge of algebra,
matrix operations, and statistics, and need to have solid programming
experience (preferably in Python).


· Moshe Abeles, Bar-Ilan Univ, Israel
· Izhar Bar Gad, Bar-Ilan Univ, Israel
· Michael Denker, Juelich Research Center and RWTH Aachen Univ, Germany
· Sonja Grün, Juelich Research Center and RWTH Aachen Univ, Germany
· Björn Kampa, RWTH Aachen, Germany
· Tatiana Korotkova, MPI and University of Cologne, Germany
· Christian Machens, Champalimaud Centre for the Unknown, Portugal
· Martin Nawrot, University of Cologne, Germany
· Yifat Prut, Hebrew Univ Jerusalem, Israel
· Alexa Riehle, CNRS, Marseille, France
· Alexander Thiele, University of Newcastle, UK
· Thomas Wachtler, G-Node, LMU Munich, Germany


Single neuron properties and statistics · Stochastic processes ·
Surrogate methods · Detection of spatio-temporal patterns · Unitary
Events · Statistical analysis of massively parallel spike data ·
Higher-order correlation analyses · Spike-LFP relationship · Population
coding · State space analysis · Machine learning · Data mining · Data
management, reproducibility, data sharing · 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 to the course. We will be able to provide a
small number of laptops for the time of the course.


A course fee of 1000 Euros will be charged to cover costs for
accommodation and meals. Limited financial support may be available for
students that otherwise would not be able to attend, which is to be
indicated in the application. 


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 a single PDF file to
<advanced-course at g-node.org>. 


Applications must be received by SEPTEMBER 30, 2018. Early application
is encouraged. 

For further information see http://www.g-node.org/anda2019 


· Sonja Grün, Juelich Research Center and RWTH Aachen Univ, Germany
· Martin Nawrot, University of Cologne, Germany
· Yifat Prut, Hebrew University Jerusalem, Israel
· Thomas Wachtler, G-Node, Ludwig-Maximilians-Universität München,
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