Chakrala Anjani Sreeprada, Xiao Jianbo, Huang Xin
Department of Neuroscience, School of Medicine and Public Health, University of Wisconsin - Madison.
bioRxiv. 2024 Sep 19:2023.06.25.546480. doi: 10.1101/2023.06.25.546480.
Segmenting visual scenes into distinct objects and surfaces is a fundamental visual process, with stereoscopic depth and motion serving as crucial cues. However, how the visual system uses these cues to segment multiple objects is not fully understood. We investigated how neurons in the middle-temporal (MT) cortex of macaque monkeys represent overlapping surfaces at different depths, moving in different directions. Neuronal activity was recorded from three male monkeys during discrimination tasks under varying attention conditions. We found that neuronal responses to overlapping surfaces showed a robust bias toward the binocular disparity of one surface over the other. The disparity bias of a neuron was positively correlated with the neuron's disparity preference for a single surface. In two animals, neurons preferring near disparities of single surfaces (near neurons) showed a near bias for overlapping stimuli, while neurons preferring far disparities (far neurons) showed a far bias. In the third animal, both near and far neurons displayed a near bias, though the near neurons showed a stronger near bias. All three animals exhibited an initial near bias across neurons relative to the average of the responses to the individual surfaces. Although attention modulated neuronal responses, the disparity bias was not caused by attention. We also found that the effect of attention was consistent with object-based, rather than feature-based attention. We proposed a model in which the pool size of the neuron population that weighs the responses to individual stimulus components can be variable. This model is a novel extension of the standard normalization model and provides a unified explanation for the disparity bias across animals. Our results reveal how MT neurons encode multiple stimuli moving at different depths and present new evidence of response modulation by object-based attention. The disparity bias allows subgroups of neurons to preferentially represent individual surfaces of multiple stimuli at different depths, thereby facilitating segmentation.
将视觉场景分割为不同的物体和表面是一个基本的视觉过程，立体深度和运动是关键线索。然而，视觉系统如何利用这些线索来分割多个物体尚未完全清楚。我们研究了猕猴颞中（MT）皮层中的神经元如何表征不同深度、沿不同方向移动的重叠表面。在不同注意力条件下的辨别任务中，记录了三只雄性猴子的神经元活动。我们发现，神经元对重叠表面的反应对其中一个表面的双眼视差表现出强烈的偏向。神经元的视差偏向与该神经元对单个表面的视差偏好呈正相关。在两只动物中，偏好单个表面近视差的神经元（近神经元）对重叠刺激表现出近偏向，而偏好远视差的神经元（远神经元）表现出远偏向。在第三只动物中，近神经元和远神经元都表现出近偏向，尽管近神经元的近偏向更强。相对于对单个表面反应的平均值，所有三只动物的神经元最初都表现出近偏向。虽然注意力调节了神经元反应，但视差偏向并非由注意力引起。我们还发现，注意力的作用与基于物体而非基于特征的注意力一致。我们提出了一个模型，其中权衡对各个刺激成分反应的神经元群体的池大小可以是可变的。该模型是标准归一化模型的新颖扩展，为不同动物的视差偏向提供了统一解释。我们的结果揭示了MT神经元如何编码在不同深度移动的多个刺激，并提供了基于物体的注意力对反应进行调节的新证据。视差偏向使神经元亚群能够优先表征不同深度的多个刺激的单个表面，从而促进分割。
