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| Describe Main/WS15_SeminarAlgorithmsBrainReading here. | == Block-Seminar "Algorithms for Brain Reading and Writing" == === General Information === Seminar in Neural Networks is an optional course in the module "Machine Learning - Theory and Applications" and is worth 3 LP (3 ECTS credits). ||'''Erster Termin für Themenvergabe'''|| XXX || ||'''Room'''|| XXX || ||'''Trainers'''|| Stefa Haufe (, Irene Winkler) || ||'''Contact''' || stefan.haufe@tu-berlin.de || || || irene.winkler.tu-berlin.de || ||'''ISIS'''|| XXX || === Description === Many results in modern neuroscience are derived from non-invasive neuroimaging data, most notably electrophysiology (electro- and magnetoencephalography, EEG/MEG) and functional magnetic resonance imaging (fMRI). In this seminar, we will review the main processing steps that are needed in order to eventually draw neurophysiological conclusions from such data ("brain reading"). The focus will be on algorithms and mathematical methods used to solve respective subproblems along the processing pipeline. The seminar will cover forward (data generating) models, inverse solutions, MRI segmentation and registration, statistical source separation as well as en- and decoding approaches. As a side topic, we will also cover "brain writing" using transcranial current stimulation and algorithms for optimal targeting. Each student is required to choose a particular topic from a predefined list of topics (available on ISIS) and to present it at the end of the semester. |
Block-Seminar "Algorithms for Brain Reading and Writing"
General Information
Seminar in Neural Networks is an optional course in the module "Machine Learning - Theory and Applications" and is worth 3 LP (3 ECTS credits).
Erster Termin für Themenvergabe |
XXX |
Room |
XXX |
Trainers |
Stefa Haufe (, Irene Winkler) |
Contact |
|
|
irene.winkler.tu-berlin.de |
ISIS |
XXX |
Description
Many results in modern neuroscience are derived from non-invasive neuroimaging data, most notably electrophysiology (electro- and magnetoencephalography, EEG/MEG) and functional magnetic resonance imaging (fMRI). In this seminar, we will review the main processing steps that are needed in order to eventually draw neurophysiological conclusions from such data ("brain reading"). The focus will be on algorithms and mathematical methods used to solve respective subproblems along the processing pipeline. The seminar will cover forward (data generating) models, inverse solutions, MRI segmentation and registration, statistical source separation as well as en- and decoding approaches. As a side topic, we will also cover "brain writing" using transcranial current stimulation and algorithms for optimal targeting.
Each student is required to choose a particular topic from a predefined list of topics (available on ISIS) and to present it at the end of the semester.