Keywords: fMRI, brain signal, compression, redundancy
TL;DR: We perform a systematic study with deep learning models regarding fMRI signal redundancy and dependency, and report many interesting findings and insights.
Abstract: How many signals in the brain activities can be erased before the encoded information is lost? Surprisingly, we found that both reconstruction and classification of voxel activities can still achieve relatively good performance even after losing 80%-90% of the signals. This leads to questions regarding how the brain performs encoding in such a robust manner. This paper investigates the redundancy and dependency of brain signals using two deep learning models with minimal inductive bias (linear layers). Furthermore, we explored the alignment between the brain and semantic representations, how redundancy differs for different stimuli and regions, as well as the dependency between brain voxels and regions.
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