|
Size: 3945
Comment:
|
Size: 7599
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 19: | Line 19: |
| * ''Part 1:'' Analysis of Spike Trains (Introduction to Linear Systems Theory, Introduction to Point Process Theory, Autoregressive Models for Neural Spike Trains, Correlation Analysis of Neural Spike Trains) [[http://itb.biologie.hu-berlin.de/~kempter/Teaching/2009_SS/index.html|details on part 1]] |
* ''Part 1:'' Analysis of Spike Trains (spike statistics, neural coding, theory of point processes, linear systems theory, correlation analysis, spike-triggered average, reverse correlation, STRF, neural decoding, signal detection theory, infomation theory, signal-to-noise ratio analysis) - see [[http://itb.biologie.hu-berlin.de/~kempter/Teaching/2010-SS-AAND/index.html|here]] for details on the first part. |
| Line 23: | Line 22: |
| === Requirements === | Required background knowledge: Basic knowledge in Neurobiology and Mathematics at the level of the first year of the Masters Program in Computational Neuroscience. |
| Line 25: | Line 24: |
| Requirements: Basic knowledge in Neurobiology and Mathematics at the level of the first year of the Masters Program in Computational Neuroscience. | === Course Certificates === |
| Line 27: | Line 26: |
| To obtain course certificates, at least 75% of the lectures (2 ECTS) must be attended, and at least 75% of the points in the exercises (5 ECTS) must be obtained. | To obtain course certificates, at least 75% of the points in the exercises (5 ECTS) must be obtained. To obtain the full 5 ECTS for the tutorial, every individual student has to complete an additional small programming project, see below. The final oral exam on the module "Acquisition and Analysis of Neuronal Data" will take place in the week from October 4 to October 8, 2010. |
| Line 31: | Line 34: |
| will be provided along with the lecture. | * Lecture #07: [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/aaaond10_lecture07.pdf|Script]] | [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/aaaond10_sheet07.pdf|Sheet]]; [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/aaaond10_sheet07_appendix.pdf|Appendix]] | [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/aaaond10_sheet07_material.zip|Material]] | Solution: [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/solution_sheet07.m|script]], [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/trainFD.m|part II]] * Lecture #08: [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/aaaond10_lecture08.pdf|Script]] | [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/aaaond10_sheet08.pdf|Sheet]] | [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/arteVPal.mat|Material]] | Solution: [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/solution_sheet08.m|script]] * Lecture #09: [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/aaaond10_lecture09.pdf|Script]] | [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/aaaond10_sheet09.pdf|Sheet]] | [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/erp_hexVPsag.mat|Material]] | Solution: [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/solution_sheet09.m|script]], [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/signed_r_square.m|part II]] * Lecture #10: [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/aaaond10_lecture10.pdf|Script]] | [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/aaaond10_sheet10.pdf|Sheet]] | [[http://ml.cs.tu-berlin.de/~blanker/SS10_AnalysisOfNeuronalData/imagVPaw.mat|Material]] === Projects === To obtain the full 5 ECTS for the tutorial, every individual student has to complete an additional small programming project. Project topics will be distributed at the end of the lecture series, and every student should work on her/his topic in the lecture-free time (July/August/September 2010). Written reports should be turned in via E-mail in September 2010 for evaluation. Reports must be turned in at least 14 days before the registration to the oral exam on the module "Acquisition and Analysis of Neuronal Data", which will take place at the beginning of October 2010. A positively evaluated report is a prerequisite for registration to the oral exam! Please consider that corrections might become necessary, and the corrected report needs to be evaluated again before the registration to the oral exam. Students are required to turn in a short written report, that is, a self-contained description of results. The report should comprise a single PDF file including a short Introduction, a Results/Discussion section, labeled Figures with caption, and the program code as an attachment. [[http://www.bccn-berlin.de/digitalAssets/0/543_GuidelineProjectReport_Compendium.pdf|Guidelines]] for Writing a Scientific Report might be helpful. |
| Line 44: | Line 60: |
| * Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Proc Magazine, 25(1):41-56, 2008. [[http://ida.first.fhg.de/publications/BlaTomLemKawMue08.pdf|pdf]] | * Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Proc Magazine, 25(1):41-56, 2008. [[http://ml.cs.tu-berlin.de/publications/BlaTomLemKawMue08.pdf|pdf]] * Blankertz B, Lemm S, Treder MS, Haufe S, Müller KR, Single-trial analysis and classification of ERP components - a tutorial, NeuroImage, 2010 (in press). [[http://ml.cs.tu-berlin.de/publications/BlaLemTreHauMue10.pdf|pdf]] |
Acquisition and Analysis of Neuronal Data
Integrated lecture and tutorials
Dates, Lecturers, and Location
Dates and Rooms: |
Lecture: Fridays from 10:00 to 11:30 am in the lecture hall 102 |
Tutorials: Fridays from 12:30 to 14:00 pm in rooms 115 and/or 215 |
|
Lecturers: |
Part 1: Richard Kempter |
Part 2: Benjamin Blankertz, Carmen Vidaurre |
|
Location: |
Bernstein Center for Computational Neurosciences Berlin, Haus 6, Philippstr. 13 |
Further Information on the web page of the BCCN-B.
Topics
This part of the module "Acquisition and Analysis of Neural Data" of the Master Program in Computational Neuroscience provides knowledge on statistical analyses of neural data:
Part 1: Analysis of Spike Trains (spike statistics, neural coding, theory of point processes, linear systems theory, correlation analysis, spike-triggered average, reverse correlation, STRF, neural decoding, signal detection theory, infomation theory, signal-to-noise ratio analysis) - see here for details on the first part.
Part 2: Statistical analysis of electroencephalogram (EEG) data, e.g., investigation of event-related potentials (ERPs) and event-related desynchronization (ERD); spatial filters; classification, adaptive classifiers.
Required background knowledge: Basic knowledge in Neurobiology and Mathematics at the level of the first year of the Masters Program in Computational Neuroscience.
Course Certificates
To obtain course certificates, at least 75% of the points in the exercises (5 ECTS) must be obtained.
To obtain the full 5 ECTS for the tutorial, every individual student has to complete an additional small programming project, see below.
The final oral exam on the module "Acquisition and Analysis of Neuronal Data" will take place in the week from October 4 to October 8, 2010.
Material
Lecture #07: Script | Sheet; Appendix | Material | Solution: script, part II
Lecture #09: Script | Sheet | Material | Solution: script, part II
Projects
To obtain the full 5 ECTS for the tutorial, every individual student has to complete an additional small programming project.
Project topics will be distributed at the end of the lecture series, and every student should work on her/his topic in the lecture-free time (July/August/September 2010). Written reports should be turned in via E-mail in September 2010 for evaluation.
Reports must be turned in at least 14 days before the registration to the oral exam on the module "Acquisition and Analysis of Neuronal Data", which will take place at the beginning of October 2010. A positively evaluated report is a prerequisite for registration to the oral exam! Please consider that corrections might become necessary, and the corrected report needs to be evaluated again before the registration to the oral exam.
Students are required to turn in a short written report, that is, a self-contained description of results. The report should comprise a single PDF file including a short Introduction, a Results/Discussion section, labeled Figures with caption, and the program code as an attachment. Guidelines for Writing a Scientific Report might be helpful.
Background material
Part 1 (spike trains)
P. Dayan and L.F. Abbott (2001) Theoretical Neuroscience. MIT Press, Cambridge, Massachusetts. Online
Part 2 (EEG)
Dornhege G, Millán J del R, Hinterberger T, McFarland DJ, Müller KR, editors. Toward Brain-Computer Interfacing. MIT Press, Cambridge, MA, 2007.
Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol, 110(11):1842-1857, Nov 1999. pdf
Key AP, Dove GO, Maguire MJ. Linking brainwaves to the brain: an ERP primer. Dev Neuropsychol. 2005;27(2):183-215. pdf
Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin. Neurophysiol., 113:767-791, 2002. pdf
Parra LC, Spence CD, Gerson AD, Sajda P. Recipes for the linear analysis of EEG. NeuroImage, 28(2):326-341, 2005. pdf
Parra LC, Christoforou C, Gerson AD, Dyrholm M, Luo A, Wagner M, Philastides M, Sajda P. Spatiotemporal Linear Decoding of Brain State. IEEE Signal Proc Magazine, 25(1): 107-115, 2008. pdf
Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Proc Magazine, 25(1):41-56, 2008. pdf
Blankertz B, Lemm S, Treder MS, Haufe S, Müller KR, Single-trial analysis and classification of ERP components - a tutorial, NeuroImage, 2010 (in press). pdf