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ANDA 2019 - G-Node Advanced Neural Data Analysis Course

01. - 18. April 2019, Jülich-Barmen

Termindetails

Wann

01.04.2019 um 14:00 bis
18.04.2019 um 14:00

Wo

Jülich-Barmen

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 experience (preferably in Python).

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Date and Venue
April 1 - 18, 2019
Haus Overbach, Juelich-Barmen, Germany

Faculty   

  • Moshe Abeles, Bar-Ilan Univ, Israel
  • Izhar Bar Gad, Bar-Ilan Univ, Israel
  • Michael Denker, Jülich Research Center and RWTH Aachen, Germany
  • Sonja Grün, Jülich Research Center and RWTH Aachen, 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

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

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 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 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.

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 a single PDF file to .

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

For further information see here

Organizers

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

 

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