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Acquisition and Analysis of Neural Data

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#40785 / #4

SoSe 2020 - SoSe 2021

English

Acquisition and Analysis of Neural Data

12

Obermayer, Klaus

benotet

Mündliche Prüfung

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34351300 FG Neuronale Informationsverarbeitung

No information

Kontakt


MAR 5-6

Velenosi, Lisa Alexandria

graduateprograms@bccn-berlin.de

https://www.bccn-berlin.de/courses-and-modules.html

Learning Outcomes

In this module, students will gain knowledge about: - the most important methods for experimental acquisition of neural data - the respective analytical methods - the different fields of application - the advantages and disadvantages of the different methods - how to handle the respective raw data. They will be enabled to: - choose the most appropriate analysis method - apply them to experimental data.

Content

The module intends to provide knowledge about experimental acquisition of neural data and their analysis. This comprises two major parts: 1) Acquisition of neural data The lecture and tutorial aim at providing a broad overview of the most common techniques for acquiring neural data and the theoretical underpinnings of these techniques. Both lecture and tutorial will be divided in a first part dealing with large scale signals (fMRI, EEG, MEG etc) and a second part concerned with cellular signals. In the tutorial emphasis is placed on hands on experience with neural data acquisition techniques. 2) Analysis of neural data This lecture gives an broad overview over analysis techniques for neural data. Specifically it will deal with: firing rates, spike statistics, spike statistics and the neural code, neural encoding, neural decoding, discrimination and population decoding, information theory, statistical analysis of electroencephalogram (EEG) data, e.g., investigation of event-related potentials (ERPs) and event-related desynchronization (ERD), spatial filters, classification, adaptive classifiers. In the tutorial emphasis is placed on hands on experience with neural data analysis.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Acquisition and Analysis of Neural Data - LaboratoryPRWiSe/SoSeNo information3
Acquisition and Analysis of Neural Data - LectureVLWiSe/SoSeNo information2
Acquisition and Analysis of Neural Data - TutorialUESoSeNo information2

Workload and Credit Points

Acquisition and Analysis of Neural Data - Laboratory (PR):

Workload descriptionMultiplierHoursTotal
Präsenzzeit15.03.0h45.0h
Vor-/Nachbereitung15.03.0h45.0h
90.0h(~3 LP)

Acquisition and Analysis of Neural Data - Lecture (VL):

Workload descriptionMultiplierHoursTotal
Präsenzzeit30.02.0h60.0h
Vor-/Nachbereitung30.02.0h60.0h
120.0h(~4 LP)

Acquisition and Analysis of Neural Data - Tutorial (UE):

Workload descriptionMultiplierHoursTotal
Präsenzzeit15.02.0h30.0h
Vor-/Nachbereitung15.08.0h120.0h
150.0h(~5 LP)
The Workload of the module sums up to 360.0 Hours. Therefore the module contains 12 Credits.

Description of Teaching and Learning Methods

Lecture: Theoretical and experimental basic knowledge is presented to the class by a lecturer. Tutorial: self-contained solving of programming exercises regarding problems of data analysis. Practical: lab work, supervised conduction of an experiment and analysis of data

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Wünschenswerte Voraussetzungen für die Teilnahme zu den Lehrveranstaltungen: - sound knowledge in mathematics (Analysis, Linear Algebra, and Probability Theory / Statistics) - basic programming knowledge

Mandatory requirements for the module test application:

1. Requirement
[CNS] Certificate of successful participation in the practical AAND
2. Requirement
[CNS] Certificate of successful participation in the tutorial AAND

Module completion

Grading

graded

Type of exam

Oral exam

Language

English

Duration/Extent

40 Min.

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
2 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 20.

Registration Procedures

Enrollment to the module: in the first class of each module component (cf. 3). Students must be present in person. Students of the Master program in Computational Neuroscience have to register for the final oral exam at least three working days prior to the examination date. Registration has to be done with the examination office (Prüfungsamt) of TU Berlin. sekr@ni.tu-berlin.de

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  available
Additional information:
Lecture notes are provided to the students through the teaching coordinator

 

Literature

Recommended literature
01. Kandel et al., Principles of Neural Science, McGraw-Hill Medical, 2000. (recommended)
02. M.F. Bear, Neuroscience: Exploring the Brain, Williams & Wilkins, 1996 (recommended)
03. Johnston and Wu, Foundations of Cellular Neurophysiology, MIT Press, 1994 (recommended)
04. Sakman and Neher, Single-Channel Recording, Springer, 2007 (recommended)
05. Jezzard et al., Functional MRI : An Introduction to Methods, Oxford University Press), 2003. (recommended)
06. Guido Dornhege, José del R. Millán, Thilo Hinterberger, Dennis McFarland, and Klaus-Robert Müller, editors. Toward Brain-Computer Interfacing. MIT Press, Cambridge, MA, 2007. (recommended)
07. Dayan, Abbott, Theoretical Neuroscience, MIT Press, 2001. (recommended)
08. Koch, Segev, Methods in Neuronal Modelling, MIT Press, 1998. (recommended)
09. Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe, and Klaus- Robert Müller. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Proc Magazine, 25(1):41-56, 2008. (additional)
10. Lucas C. Parra, Clay D. Spence, Adam D. Gerson, and Paul Sajda. Recipes for the linear analysis of EEG. NeuroImage, 28(2):326-341, 2005. (additional)
11. Jonathan R. Wolpaw, Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, and Theresa M. Vaughan. Brain-computer interfaces for communication and control. Clin. Neurophysiol., 113:767-791, 2002. (additional)
12. Gert Pfurtscheller and F. H. Lopes da Silva. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol, 110(11):1842-1857, Nov 1999. (additional)
13. Key AP, Dove GO, Maguire MJ. Linking brainwaves to the brain: an ERP primer. Dev Neuropsychol. 2005;27(2):183-215. (additional)

Assigned Degree Programs


This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
This module is not used in any degree program.

Miscellaneous

Responsible for this module are: Prof. Dr. Richard Kempter, HU Berlin (r.kempter@biologie.hu-berlin.de) Prof. Dr. Michael Brecht, HU Berlin (Michael.Brecht@bccn-berlin.de) Prof. Dr. John-Dylan Haynes, Charité Universitätsmedizin Berlin (johndylan.haynes@gmail.com) Prof. Dr. Benjamin Blankertz, TU Berlin (benjamin.blankertz@tu-berlin.de)