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Modeling Tonotopically Resolved Ongoing Neural Activity Using A Backward Encoding Approach by Patrick Reisinger
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1Modeling Tonotopically Resolved Ongoing Neural Activity Using A Backward Encoding Approach
By Patrick Reisinger, Juliane Schubert and Nathan Weisz
The tonotopic representation of sounds is a well established organizational principle of the auditory cortex. However, given the small extent of auditory cortical regions, mapping tonotopic representation using noninvasive tools such as M/EEG is challenging. Resolving ongoing brain activity at a tonotopic level has been deemed virtually impossible due to volume conduction. However, based on previous data showing robust carrier-frequency decoding, it is clear tonotopic information is present at a noninvasive level. In order to also eavesdrop on ongoing activity, we propose that backward encoding models can be tuned to specific sound frequency bands applied to other data sets and to model neural activity in resulting “feature” (here: sound frequency band) channels.
“Modeling Tonotopically Resolved Ongoing Neural Activity Using A Backward Encoding Approach” Metadata:
- Title: ➤ Modeling Tonotopically Resolved Ongoing Neural Activity Using A Backward Encoding Approach
- Authors: Patrick ReisingerJuliane SchubertNathan Weisz
Edition Identifiers:
- Internet Archive ID: osf-registrations-4nef6-v1
Downloads Information:
The book is available for download in "data" format, the size of the file-s is: 0.08 Mbs, the file-s for this book were downloaded 2 times, the file-s went public at Wed Aug 25 2021.
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