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Lecture + Tutorial + Programming Course, Summer 2006

Computational Neuroscience IV:
Analysis of Neural Systems

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Lectures and Tutorials: Dr. Richard Kempter and Prof. Andreas V.M. Herz
Programming Course: Dr. Christian Leibold and Robert Schmidt

Time and Location:
Lectures: Mondays from 12:15 to 2:00 pm in the seminar room 1322 of the ITB, Invalidenstr. 43
Tutorials: Fridays from 8:30 to 10:00 am in front of room 2317 I-W (Zwischengeschoß) of the ITB, Invalidenstr. 43
Programming Course: Wednesdays from 18:00 to 20:00 pm in the Computer 'CIP' Pool (room 106/107), Invalidenstr. 42

All participants of the written exam the programming course have passed the test. Congratulations! You can pick up course certificates at Elvira Lauterbach's office (room 2325) starting Wednesday July-26-2006.

Target Group: Students of Biology, Biophysics, Physics, Mathematics, and Computer Science.

Requirements: Basic knowledge in Neurobiology; ''Mathematics for Biologists'' I and II (III recommended), ''Data Analysis and Stochastic Processes'' recommended; basic knowledge in programming.

Topics: analysis of high-dimensional data (neural network/EEG/MEG/imaging/audio), description of network states (e.g. the hippocampus), cocktail-party problem, principal and independent component analysis, probability theory and higher order statistics, information theory, estimation theory

Lectures

With the advent of new functional brain imaging methods (EEG, MRI, optical methods) and multi-electrode devices it is now possible to simultaneously record from many different sites of living brains. Vast amounts of data can be collected, the analysis of which poses a challenge. It has thus become important to extract the essential information from the data to allow an easier interpretation of their properties, and to test relevant scientific hypotheses. The lecture series ''Computational Neuroscience IV'' will provide an overview over currently used statistical methods for the analysis of high-dimensional data. A framework for the analysis of neural systems will be given, using probability theory, estimation theory, information theory, and optimization. In the first part of the lecture series, we introduce Independent Component Analysis (ICA), which is a method for finding ''factors'' (or ''sources'' or ''components'') that underlie sets of random variables. ICA has also applications in denoising data, data compression and mining, feature extraction, and pattern recognition. A short summary can be found in the article by Hyväarinen and Oja, 2000. The second half of the lecture series will focus on the analysis of multi-electrode data.

Tutorials

The tutorials accompany the lectures. They mediate advanced analytic tools for data analysis. Weekly exercise sheets are handed out on Mondays, solutions are to be handed in one week later, and the solutions are discussed on Fridays.

Exercises:
Exercises 1
Exercises 2
Exercises 3
Exercises 4
Exercises 5
Exercises 6
Exercises 7

Course Certificate (''Schein'') for the Tutorials + Lecture:
To obtain a Course Certificate (''Schein'') for the Tutorials+Lectures (4 SWS or 4 ECTS), a written exam must be passed (more than 50 points of the maximum of 100 points). The date of the examination is Friday, July-21-2006, 8:30-10:00 am (only resources allowed: two A4 pages containing handwritten notes).

The written exam heavily relies on the exercises. Regular attendance of the weekly tutorials as well as occasional presentations of solutions during the tutorials are therefore highly recommended. At most 15 extra points can be obtained through correctly solving the weekly excercises. Those extra points will be added to the points obtained in the final exam so that the maximum number of points is 115. However, more than 95 points in total are sufficient for a "1.0 (sehr gut/excellent)".

Programming Course

The programming course accompanies the lectures. The course mediates basic knowledge in programming and gives insight into basic tools for data analysis. In about the first half of the semester, computer assignments will be distributed weekly, and the given problems can be solved in the Computer Pool. Help will be provided during the regular course hours by Christian Leibold and Robert Schmidt. At the beginning of the second half of the semester, a set of more advanced problems will be distributed. A group of two or three students can sign up for a specific project, and they should work together on a solution for several weeks. Help will again be provided during the regular course hours.

An additional web page on the Programming Course contains all the details on hardware, software, assignments, and solutions.

Course Certificate (''Schein'') for the Programming Course:
To obtain a Course Certificate (''Schein'') for the Programming Course (2 SWS or 2 ECTS), an oral presentation of the results of the project together with a one-page written summary of the results is required. The presentations will take place at the end of the semester on Monday, July-17-2006, 12:15-14:00 pm.

Background material:
L. Wiskott. Principal Component Analysis. ITB, 2004. (download manuscript)
A. Hyvärinen, J. Karhunen, and E. Oja. Independent Component Analysis. Wiley, New York, 2001. (table of contents, first chapter)
A. Hyvärinen, E. Oja. Independent component analysis: algorithms and applications. Neural Networks 13:411-430, 2000.
G. Buzsaki. Large-scale recording of neuronal ensembles. Nature Neurosci. 7:446-451, 2004.
E. N. Brown, R. E. Kass, and P. P. Mitra. Multiple neural spike train data analysis: state-of-the-art and future challenges. Nature Neurosci. 7:456-461, 2004.
M. W. Oram, M. C. Wiener, R. Lestienne, and B. J. Richmond. Stochastic nature of precisely timed patterns in visual system neuronal responses. J. Neurophysiol. 81:3021-3033, 1999)
D.J.C. Mackay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge, 2003. (download the book)
A. Webb. Statistical Pattern Recognition. Second Edition, Wiley, 2002.

Last update: July-19-2006

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