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ANDA 2018 - G-Node Advanced Neural Data Analysis Course

March 5 - 22, 2018 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).

Date and Venue

March 5 - 22, 2018

Haus Overbach, Juelich-Barmen, Germany


· Moshe Abeles, Bar-Ilan Univ, Israel

· Michael Denker, Juelich Research Center and RWTH Aachen Univ, Germany

· Alain Destexhe, CNRS, Gif sur Yvette, France

· Sonja Grün, Juelich Research Center and RWTH Aachen Univ, Germany

· Björn Kampa, RWTH Aachen, 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

· Jonathan Victor, Weill Cornell Medical College, USA

· Thomas Wachtler, G-Node, LMU Munich, Germany

Topics covered

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.

Course Fee

A course fee of 800 Euros will be charged to accepted students. The course fee contributes to covering 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 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 a single PDF file to <>.


Applications must be received by October 15, 2017. Early application is encouraged because number of participants is limited.

For further information see


· Sonja Grün, Juelich Research Center and RWTH Aachen Univ, Germany

· Martin Nawrot, University of Cologne, Germany

· Thomas Wachtler, G-Node, Ludwig-Maximilians-Universität München, Germany


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