Feedforward deep neural networks for object recognition are a promising model of visual processing and can accurately predict firing-rate responses along the ventral stream. Yet, these networks have limitations as models of various aspects of cortical processing related to recurrent connectivity, including neuronal synchronization and the integration of sensory inputs with spatio-temporal context. We trained self-supervised, generative neural networks to predict small regions of natural images based on the spatial context (i.e. inpainting). Using these network predictions, we determined the spatial predictability of visual inputs into (macaque) V1 receptive fields (RFs), and distinguished low- from high-level predictability. Spatial predictability strongly modulated V1 activity, with distinct effects on firing rates and synchronization in gamma- (30-80Hz) and beta-bands (18-30Hz). Furthermore, firing rates, but not synchronization, were accurately predicted by a deep neural network for object recognition. Neural networks trained to specifically predict V1 gamma-band synchronization developed large, grating-like RFs in the deepest layer. These findings suggest complementary roles for firing rates and synchronization in self-supervised learning of natural-image statistics.

中文翻译：

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自然图像的可预测性决定了V1的点火速度和同步性：一种深度神经网络方法

用于对象识别的前馈深层神经网络是一种有前途的视觉处理模型，可以准确地预测腹侧流的射击速率响应。然而，这些网络作为与循环连接相关的皮质处理各个方面的模型具有局限性，包括神经元同步以及感觉输入与时空环境的整合。我们训练了自我监督的生成神经网络，以基于空间上下文（即，修复）来预测自然图像的小区域。使用这些网络预测，我们确定了（猕猴）V1感受野（RF）的视觉输入的空间可预测性，并从高水平可预测性中区分出低。空间可预测性强烈调节了V1的活动，对伽玛（30-80Hz）和β频段（18-30Hz）的发射速率和同步具有明显的影响。此外，通过用于对象识别的深层神经网络可以准确地预测发射速率，但不能同步。经过训练以专门预测V1伽玛波段同步的神经网络在最深的层中形成了大型的类似光栅的RF。这些发现表明，在自然图像统计的自我监督学习中，射击速率和同步性具有互补作用。