Center for the Neural Basis of Cognition and Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
J Neurosci. 2009 Dec 16;29(50):15780-95. doi: 10.1523/JNEUROSCI.2305-09.2009.
Inferring depth from binocular disparities is a difficult problem for the visual system because local features in the left- and right-eye images must be matched correctly to solve this "stereo correspondence problem." Cortical architecture and computational studies suggest that lateral interactions among neurons could help resolve local uncertainty about disparity encoded in individual neurons by incorporating contextual constraints. We found that correlated activity among pairs of neurons in primary visual cortex depended both on disparity-tuning relationships and the stimuli displayed within the receptive fields of the neurons. Nearby pairs of neurons with distinct disparity tuning exhibited a decrease in spike correlation at competing disparities soon after response onset. Distant neuronal pairs of similar disparity tuning exhibited an increase in spike correlation at mutually preferred disparities. The observed correlated activity and response dynamics suggests that local competitive and distant cooperative interactions improve disparity tuning of individual neurons over time. Such interactions could represent a neural substrate for the principal constraints underlying cooperative stereo algorithms.
从双目视差推断深度是视觉系统的一个难题，因为必须正确匹配左眼和右眼图像中的局部特征，以解决这个“立体对应问题”。皮层结构和计算研究表明，神经元之间的横向相互作用可以通过整合上下文约束来帮助解决单个神经元中关于视差的局部不确定性。我们发现，初级视觉皮层中神经元对的相关活动既取决于视差调谐关系，也取决于神经元感受野内显示的刺激。具有不同视差调谐的邻近神经元对在反应起始后不久，在竞争视差处的尖峰相关性下降。具有相似视差调谐的远距离神经元对在相互偏好的视差处尖峰相关性增加。观察到的相关活动和反应动力学表明，随着时间的推移，局部竞争和远距离合作相互作用改善了单个神经元的视差调谐。这种相互作用可能代表了合作立体算法的主要约束的神经基础。
