Institut de Neurosciences de la Timone, UMR 7289, CNRS and Aix-Marseille Université, Marseille, France.
School of Optometry, Université de Montréal, Montréal, Canada.
Commun Biol. 2023 Jun 23;6(1):667. doi: 10.1038/s42003-023-05042-3.
Our daily endeavors occur in a complex visual environment, whose intrinsic variability challenges the way we integrate information to make decisions. By processing myriads of parallel sensory inputs, our brain is theoretically able to compute the variance of its environment, a cue known to guide our behavior. Yet, the neurobiological and computational basis of such variance computations are still poorly understood. Here, we quantify the dynamics of sensory variance modulations of cat primary visual cortex neurons. We report two archetypal neuronal responses, one of which is resilient to changes in variance and co-encodes the sensory feature and its variance, improving the population encoding of orientation. The existence of these variance-specific responses can be accounted for by a model of intracortical recurrent connectivity. We thus propose that local recurrent circuits process uncertainty as a generic computation, advancing our understanding of how the brain handles naturalistic inputs.
我们的日常活动发生在一个复杂的视觉环境中，其内在的可变性挑战了我们整合信息做出决策的方式。通过处理无数并行的感官输入，我们的大脑理论上能够计算环境的方差，这一线索被认为可以指导我们的行为。然而，这种方差计算的神经生物学和计算基础仍知之甚少。在这里，我们量化了猫初级视觉皮层神经元的感官方差调制的动力学。我们报告了两种典型的神经元反应，其中一种对方差变化具有弹性，并共同编码感官特征及其方差，从而提高了方位的群体编码。这种方差特异性反应的存在可以用皮层内递归连接的模型来解释。因此，我们提出局部递归电路将不确定性作为一种通用计算来处理，这推进了我们对大脑如何处理自然输入的理解。
