Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory–inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function—fast sampling-based inference—and predict further properties of these motifs that can be tested in future experiments.

感觉皮层显示出一系列无处不在的动态特征，例如持续的噪声可变性、瞬态超调和振荡，到目前为止，这些特征都逃脱了一个共同的、有原则的理论解释。我们通过训练视觉皮层超柱的周期性兴奋-抑制神经回路模型来执行基于采样的概率推理，从而为这些现象开发了一个统一模型。优化后的网络显示出几个关键的生物学特性，包括分裂归一化和刺激调制噪声可变性、刺激开始时以抑制为主的瞬变和强伽马振荡。这些动态特征在加速推理和做出预测方面具有独特的功能作用，我们在对清醒猴子的记录进行的新颖分析中证实了这一点。