The G-Node Course on Neural Data Analysis offers hands-on experience in state-of-the-art methods for analyzing complex neural data to PhD students and postdocs. Participants may have a theoretical background with interest in analyzing biological data, or they may come from an experimental background, interested in new ways of analyzing their data.
Applicants should have an elementary understanding of linear algebra and statistics, as well as basic programming knowledge in either Matlab or Python. Most analysis tools will have to be programmed by the individual participant, under the guidance of tutors. Participants should show interest in model-driven analyses and be open to theoretical approaches to data analysis. Likewise, they should be interested in the practical analysis of real biological data.
· Spectral analysis · Mutual information · Machine learning · Neural tuning and decoding · Signal Detection Theory · Multi-Channel Recording Analysis · Population Dynamics · Synchronization · Noise ·
(see description of course modules)
Please send your application with the following material as a single pdf file to dataanalysis-course@g-node.org:
Deadline: May 15, 2017
Jan Grewe, Eberhard-Karls-Universität Tübingen
Fabian Sinz · Baylor College of Medicine, Houston
Local Organization
Monika Volk, LMU München
Host
Thomas Wachtler
German Neuroinformatics Node (G-Node)
Ludwig-Maximilians-Universität München
www.g-node.org
Local Address
LMU Biocenter
Großhaderner Str. 2
82152 Planegg-Martinsried
Course Room C 00.005
Download map and directions [pdf]
The German Neuroinformatics Node receives funding from the Bundesministerium für Bildung und Forschung (BMBF)