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@g-node.org
Deadlines
Applications must be received by January 31, 2017. Early application is encouraged because number of participants is limited.
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
The German Neuroinformatics Node receives funding from the Bundesministerium für Bildung und Forschung (BMBF)
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