New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources:
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis Electronic eBook |
| Language: | English |
| Published: |
Berlin
2022
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| Subjects: | |
| Online Access: | Volltext |
| Physical Description: | 1 Online-Ressource (118 Seiten) Illustrationen, Diagramme |
| DOI: | 10.14279/depositonce-16416 |
Staff View
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Record in the Search Index
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| indexdate | 2024-07-10T09:41:24Z |
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| language | English |
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| spelling | Jamshidi Idaji, Mina Verfasser (DE-588)1272554465 aut New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources Berlin 2022 1 Online-Ressource (118 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Dissertation Technische Universität Berlin 2022 Signaltrennung (DE-588)4181278-5 gnd rswk-swf Nervennetz (DE-588)4041638-0 gnd rswk-swf Encephalographie (DE-588)4297453-7 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Encephalographie (DE-588)4297453-7 s Nervennetz (DE-588)4041638-0 s Signaltrennung (DE-588)4181278-5 s DE-604 Erscheint auch als Online-Ausgabe 10.14279/depositonce-16416 Erscheint auch als Druck-Ausgabe (DE-604)BV048553346 https://doi.org/10.14279/depositonce-16416 Resolving-System kostenfrei Volltext |
| spellingShingle | Jamshidi Idaji, Mina New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources Signaltrennung (DE-588)4181278-5 gnd Nervennetz (DE-588)4041638-0 gnd Encephalographie (DE-588)4297453-7 gnd |
| subject_GND | (DE-588)4181278-5 (DE-588)4041638-0 (DE-588)4297453-7 (DE-588)4113937-9 |
| title | New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources |
| title_auth | New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources |
| title_exact_search | New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources |
| title_exact_search_txtP | New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources |
| title_full | New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources |
| title_fullStr | New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources |
| title_full_unstemmed | New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources |
| title_short | New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources |
| title_sort | new machine learning methods for modeling nonlinear interactions in neural data towards separation and detection of genuine cross frequency synchronized sources |
| topic | Signaltrennung (DE-588)4181278-5 gnd Nervennetz (DE-588)4041638-0 gnd Encephalographie (DE-588)4297453-7 gnd |
| topic_facet | Signaltrennung Nervennetz Encephalographie Hochschulschrift |
| url | https://doi.org/10.14279/depositonce-16416 |
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