Interpretable functional specialization emerges in deep convolutional networks trained on brain signals

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11130%2F22%3A10443089" target="_blank" >RIV/00216208:11130/22:10443089 - isvavai.cz</a>
Alternative codes found
RIV/00064203:_____/22:10443089
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=de24omQx6e" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=de24omQx6e</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1741-2552/ac6770" target="_blank" >10.1088/1741-2552/ac6770</a>

Result language
angličtina
Original language name
Interpretable functional specialization emerges in deep convolutional networks trained on brain signals
Original language description
OBJECTIVE: Functional specialization is fundamental to neural information processing. Here, we study whether and how functional specialization emerges in artificial deep convolutional neural networks (CNNs) during a brain-computer interfacing (BCI) task. APPROACH: We trained CNNs to predict hand movement speed from intracranial EEG (iEEG) and delineated how units across the different CNN hidden layers learned to represent the iEEG signal. MAIN RESULTS: We show that distinct, functionally interpretable neural populations emerged as a result of the training process. While some units became sensitive to either iEEG amplitude or phase, others showed bimodal behavior with significant sensitivity to both features. Pruning of highly-sensitive units resulted in a steep drop of decoding accuracy not observed for pruning of less sensitive units, highlighting the functional relevance of the amplitude- and phase-specialized populations. SIGNIFICANCE: We anticipate that emergent functional specialization as uncovered here will become a key concept in research towards interpretable deep learning for neuroscience and BCI applications.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
30103 - Neurosciences (including psychophysiology)

Project
<a href="/en/project/GA20-21339S" target="_blank" >GA20-21339S: The dynamics of brain networks during internally and externally oriented cognitive tasks</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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