Farzmahdi Amirhossein, Kohn Adam, Coen-Cagli Ruben
Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA.
Zuckerman Institute, Columbia University, New York, NY, USA.
Nat Commun. 2025 Jul 22;16(1):6757. doi: 10.1038/s41467-025-62086-1.
Determining how the brain encodes sensory information requires understanding the structure of cortical activity, including how its variability is shared among neurons. The role of this covariability in cortical representations of natural visual inputs is unclear. Here, we adopt the neural sampling hypothesis and extend a well-established generative model of image statistics, to explain pairwise activity as representing joint probabilistic inferences about latent features of images. According to the theory, variability reflects uncertainty about those latent features. In natural images, some sources of uncertainty are shared between features and lead to covariability between neurons, whereas other independent sources contribute to private variability. Our analysis shows that spatial context in images reduces shared uncertainty for overlapping features, whereas it reduces independent uncertainty for non-overlapping features. As a result, the model predicts that increasing the size of an image reduces correlations for pairs with overlapping receptive fields and increases correlations for pairs with offset receptive fields. This prediction was confirmed by recordings from male macaque primary visual cortex (V1). Our study establishes a precise connection between V1 correlations and natural scene statistics, suggesting patterns of covariability are a feature of probabilistic representations of scenes.
确定大脑如何编码感觉信息需要了解皮层活动的结构，包括其变异性在神经元之间是如何共享的。这种协变性在自然视觉输入的皮层表征中的作用尚不清楚。在这里，我们采用神经采样假说并扩展了一个成熟的图像统计生成模型，将成对活动解释为代表对图像潜在特征的联合概率推断。根据该理论，变异性反映了关于这些潜在特征的不确定性。在自然图像中，一些不确定性来源在特征之间是共享的，并导致神经元之间的协变性，而其他独立来源则导致个体变异性。我们的分析表明，图像中的空间上下文减少了重叠特征的共享不确定性，而减少了非重叠特征的独立不确定性。因此，该模型预测，增加图像大小会降低具有重叠感受野的神经元对之间的相关性，并增加具有偏移感受野的神经元对之间的相关性。这一预测通过对雄性猕猴初级视觉皮层（V1）的记录得到了证实。我们的研究在V1相关性与自然场景统计之间建立了精确的联系，表明协变模式是场景概率表征的一个特征。
