掩码自动编码器从静息态神经数据中学习与感知相关的表示
Masked Autoencoders Learn Perception-Relevant Representations from Resting State Neural Data
AI总结：
研究利用自监督学习，通过在盲人参与者V1区的自发神经活动上预训练掩码自动编码器，测试能否改善感知解码。结果显示该方法有效，表明自发皮层活动有价值，无监督预训练是改善神经解码的好策略。
AI中文摘要：
临床神经假体面临数据瓶颈：有标签的感知试验稀缺，而数小时的自发神经活动大多未被充分利用。在此，我们测试自监督学习能否利用这些无标签数据集来改善感知解码。我们在一名盲人参与者V1区的皮层内阵列的14.6小时自发多单元活动上预训练了一个掩码自动编码器。该模型在无监督情况下捕捉到了可解释的脑结构：V1的空间组织和感知状态分离均纯粹从其潜在表示中浮现。为测试这些特征，我们使用线性探测（对冻结的潜在表示进行逻辑回归）来测量对有刺激数据的性能。在一般心理测量任务上，感知解码准确率达到84.1%。在更难的阈值水平任务上，准确率达到64.0%。这项工作表明自发皮层活动并非噪声，它包含丰富的、与任务相关的结构。对这些数据进行无监督预训练是改善神经解码的一种有前景的策略。
英文摘要：
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.
