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ANDA 2021 - G-Node Advanced Neural Data Analysis Course

April 19-29, 2021, online, application deadline: Jan 31, 2021



19.04.2021 um 00:00 bis
29.04.2021 um 00:00



Termin übernehmen

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 experiences (preferably in Python).


Due to the Covid pandemic, this year the course will be held online



  • Michael Denker, Jülich Research Center, Germany
  • Udo Ernst, Univ. Bremen, Germany
  • Sonja Grün, Jülich Research Center, Germany
  • Adam Kohn, Albert Einstein College of Medicine, New York, USA
  • Jakob Macke, TU Munich, Germany
  • Luca Mazzucato, Univ. of Oregon, Eugene, USA
  • Martin Nawrot, Univ. of Cologne, Germany
  • Yifat Prut, Hebrew Univ. Jerusalem, Israel
  • Hansjörg Scherberger, German Primate Center, Göttingen, Germany
  • Thomas Wachtler, 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 · Elephant toolbox · Spike-LFP relationship · Population coding · State space analysis · Machine learning · Data mining · Research data management, reproducibility, data sharing


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.

Course Fee

50 Euros

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


January 31, 2021


  • Sonja Grün, Jülich 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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