Masked Autoencoders Learn Perception-Relevant Representations from Resting State Neural Data
For clinical neuroprosthetics, this work addresses the data bottleneck of scarce labeled perception trials by leveraging abundant unlabeled spontaneous neural data to improve decoding performance.
The paper shows that pretraining a masked autoencoder on 14.6 hours of spontaneous neural activity from a blind participant's V1 improves perception decoding, achieving 84.1% accuracy on a general psychometric task and 64.0% on a threshold-level task, demonstrating that spontaneous activity contains task-relevant structure.
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 performance on the data with stimulation. Perception decoding accuracy reached 84.1% on a general psychometric task. On the more difficult threshold-level task, accuracy reached 64.0%. This work shows that spontaneous cortical activity is not noise; it contains rich, task-relevant structure. Unsupervised pretraining on this data is a promising strategy to improve neural decoding.