Recent work has shown that convolutional neural networks (CNNs) trained on image recognition tasks can serve as valuable models for predicting neural responses in primate visual cortex. However, these models typically require biologically infeasible levels of labelled training data, so this similarity must at least arise via different paths. In addition, most popular CNNs are solely feedforward, lacking a notion of time and recurrence, whereas neurons in visual cortex produce complex time-varying responses, even to static inputs. Towards addressing these inconsistencies with biology, here we study the emergent properties of a recurrent generative network that is trained to predict future video frames in a self-supervised manner. Remarkably, the resulting model is able to capture a wide variety of seemingly disparate phenomena observed in visual cortex, ranging from single-unit response dynamics to complex perceptual motion illusions, even when subjected to highly impoverished stimuli. These results suggest potentially deep connections between recurrent predictive neural network models and computations in the brain, providing new leads that can enrich both fields.

最近的研究表明，在图像识别任务上训练的卷积神经网络（CNN）可以作为预测灵长类动物视觉皮层神经反应的有价值的模型。然而，这些模型通常需要生物学上不可行的标记训练数据水平，因此这种相似性必须至少通过不同的路径产生。此外，大多数流行的 CNN 都是前馈的，缺乏时间和循环的概念，而视觉皮层中的神经元会产生复杂的时变响应，甚至对静态输入也是如此。为了解决这些与生物学的不一致问题，我们在这里研究了循环生成网络的新兴特性，该网络经过训练以自我监督的方式预测未来的视频帧。值得注意的是，所得到的模型能够捕捉在视觉皮层中观察到的各种看似不同的现象，从单一单元反应动力学到复杂的知觉运动错觉，即使受到高度贫乏的刺激也是如此。这些结果表明循环预测神经网络模型和大脑计算之间存在潜在的深层联系，为丰富这两个领域提供了新的线索。