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Berlin Theoretical Neuroscience Running lecture

Acquisition and Analysis of Neural Data

Students will gain knowledge about the most important methods for experimental acquisition of neural data and the respective analytical methods, they will learn about the different fields of application, the advantages and disadvantages of the different methods and will become familiar with the respective raw data. They will be enabled to choose the most appropriate analysis method and apply them to experimental data.
Contents include:
Acquisition of neural data (1st semester): large scale signals (fMRI, EEG, MEG etc) and cellular signals, hands-on experience with neural data acquisition techniques.
Analysis of neural data (2nd semester): firing rates, spike statistics, spike statistics and the neural code, neural encoding, neural decoding, discrimination and population decoding, information theory, statistical analysis of EEG) data, spatial filters, classification, adaptive classifiers.

Analytical / Programming Tutorial Practical Course (Lab)

MSc, PhD
  • Maths: 3 semesters
  • Programming: basic
  • Neuroscience: basic
  • Progr. Lang.: Python

This module is compulsory for students of the Master program Computational Neuroscience, compulsory elective or elective for the specialization Computational Neuroscience, Artificial Intelligence, and Signal Processing (generally for advanced Diploma students or master students).

BCCN Berlin

Haynes et al.
Winter and summer, weekly/ 2 x 4 h per week / 5 - 12 ECTS

Runs for 2 semesters, possible combination would be the winter term course with focus on data acquisition, 5 ECTS without the project

Visit the course website