Interpretable functional specialization emerges in deep convolutional networks trained on brain signals
Jiří Hammer, Robin Tibor Schirrmeister, Karl Hartmann, Petr Marusič, Andreas Schulze‐Bonhage, Tonio Ball
Abstract
Abstract 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 electroencephalography (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.