[Comp-neuro] ANDA 2020 - G-Node Advanced Neural Data Analysis Course, April 14-30, 2020, Jülich, Germany

Thomas Wachtler wachtler at biologie.uni-muenchen.de
Tue Oct 15 08:54:04 CEST 2019


 

ANDA 2020 - G-NODE ADVANCED NEURAL DATA ANALYSIS COURSE 

April 14 - 30, 2020
Haus Overbach, Jülich-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).

FACULTY 

 · Moshe Abeles, Hebrew Univ. Jerusalem, Israel
 · 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 and reproducibility

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
<advanced-course at g-node.org>. 

APPLICATION DEADLINE 

Applications must be received by NOVEMBER 15, 2019. Early application is
encouraged. 

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