Masked Autoencoders Learn Perception-Relevant Representations from Resting State Neural Data
Clinical neuroprosthetics face a data bottleneck: labeled perception trials are scarce while hours of spontaneous neural activity are largely underutilized. Here, we test whether self-supervised learning can use these unlabeled datasets to improve perception decoding. We pretrained a masked autoencoder on 14.6 hours of spontaneous multiunit activity from an intracortical array in a blind participant's V1. The model captured interpretable brain structure without supervision: V1's spatial organization and perceptual state separation both emerged purely from its latent representations. To test these features, we used linear probing (logistic regression on the frozen latents) to measure perfo...
| Authors | Aleksandr Kovalev, Antonio Lozano, Fabrizio Grani et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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