Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today’s best supervised methods and that the mapping of these neural network models’ hidden layers is neuroanatomically consistent across the ventral stream. Strikingly, we find that these methods produce brain-like representations even when trained solely with real human child developmental data collected from head-mounted cameras, despite the fact that these datasets are noisy and limited. We also find that semisupervised deep contrastive embeddings can leverage small numbers of labeled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results illustrate a use of unsupervised learning to provide a quantitative model of a multiarea cortical brain system and present a strong candidate for a biologically plausible computational theory of primate sensory learning.

深度神经网络目前提供了整个灵长类动物腹侧视觉流中神经元反应模式的最佳定量模型。然而，这样的网络作为腹侧流发育的模型仍然令人难以置信，部分原因是它们采用监督方法进行训练，需要比婴儿在发育过程中可访问的标签更多的标签。在这里，我们报告最近无监督学习的快速进展在很大程度上缩小了这一差距。我们发现，使用深度无监督对比嵌入方法学习的神经网络模型在多个腹侧视觉皮层区域中实现的神经预测精度等于或超过使用当今最好的监督方法导出的模型，并且这些神经网络模型的隐藏层的映射是神经解剖学的整个腹侧流一致。引人注目的是，我们发现，即使仅使用从头戴式摄像机收集的真实人类儿童发育数据进行训练，这些方法也会产生类似大脑的表征，尽管这些数据集充满噪音且有限。我们还发现，半监督深度对比嵌入可以利用少量标记示例来生成表示，从而大大提高了与人类行为的错误模式一致性。总而言之，这些结果说明了如何使用无监督学习来提供多区域皮质脑系统的定量模型，并为灵长类感觉学习的生物学合理的计算理论提供了强有力的候选者。