Revealing nonlinear neural decoding by analyzing choices
Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. Here, the authors present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information which indicates near-optimal nonlinear decoding.
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-26793-9 |
| _version_ | 1819006810964623360 |
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| author | Qianli Yang Edgar Walker R. James Cotton Andreas S. Tolias Xaq Pitkow |
| author_facet | Qianli Yang Edgar Walker R. James Cotton Andreas S. Tolias Xaq Pitkow |
| author_sort | Qianli Yang |
| collection | DOAJ |
| description | Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. Here, the authors present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information which indicates near-optimal nonlinear decoding. |
| first_indexed | 2024-12-21T00:14:36Z |
| format | Article |
| id | doaj.art-8a6c8800814c4391be68ee95e74e47af |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-21T00:14:36Z |
| publishDate | 2021-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-8a6c8800814c4391be68ee95e74e47af2022-12-21T19:22:16ZengNature PortfolioNature Communications2041-17232021-11-0112111310.1038/s41467-021-26793-9Revealing nonlinear neural decoding by analyzing choicesQianli Yang0Edgar Walker1R. James Cotton2Andreas S. Tolias3Xaq Pitkow4Department of Electrical and Computer Engineering, Rice UniversityDepartment of Neuroscience, Baylor College of MedicineShirley Ryan Ability LabDepartment of Electrical and Computer Engineering, Rice UniversityDepartment of Electrical and Computer Engineering, Rice UniversitySensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. Here, the authors present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information which indicates near-optimal nonlinear decoding.https://doi.org/10.1038/s41467-021-26793-9 |
| spellingShingle | Qianli Yang Edgar Walker R. James Cotton Andreas S. Tolias Xaq Pitkow Revealing nonlinear neural decoding by analyzing choices Nature Communications |
| title | Revealing nonlinear neural decoding by analyzing choices |
| title_full | Revealing nonlinear neural decoding by analyzing choices |
| title_fullStr | Revealing nonlinear neural decoding by analyzing choices |
| title_full_unstemmed | Revealing nonlinear neural decoding by analyzing choices |
| title_short | Revealing nonlinear neural decoding by analyzing choices |
| title_sort | revealing nonlinear neural decoding by analyzing choices |
| url | https://doi.org/10.1038/s41467-021-26793-9 |
| work_keys_str_mv | AT qianliyang revealingnonlinearneuraldecodingbyanalyzingchoices AT edgarwalker revealingnonlinearneuraldecodingbyanalyzingchoices AT rjamescotton revealingnonlinearneuraldecodingbyanalyzingchoices AT andreasstolias revealingnonlinearneuraldecodingbyanalyzingchoices AT xaqpitkow revealingnonlinearneuraldecodingbyanalyzingchoices |