Center for the Neural Basis of Cognition and Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
J Neurosci. 2013 Feb 13;33(7):2934-46. doi: 10.1523/JNEUROSCI.2952-12.2013.
Disparity tuning measured in the primary visual cortex (V1) is described well by the disparity energy model, but not all aspects of disparity tuning are fully explained by the model. Such deviations from the disparity energy model provide us with insight into how network interactions may play a role in disparity processing and help to solve the stereo correspondence problem. Here, we propose a neuronal circuit model with recurrent connections that provides a simple account of the observed deviations. The model is based on recurrent connections inferred from neurophysiological observations on spike timing correlations, and is in good accord with existing data on disparity tuning dynamics. We further performed two additional experiments to test predictions of the model. First, we increased the size of stimuli to drive more neurons and provide a stronger recurrent input. Our model predicted sharper disparity tuning for larger stimuli. Second, we displayed anticorrelated stereograms, where dots of opposite luminance polarity are matched between the left- and right-eye images and result in inverted disparity tuning in the disparity energy model. In this case, our model predicted reduced sharpening and strength of inverted disparity tuning. For both experiments, the dynamics of disparity tuning observed from the neurophysiological recordings in macaque V1 matched model simulation predictions. Overall, the results of this study support the notion that, while the disparity energy model provides a primary account of disparity tuning in V1 neurons, neural disparity processing in V1 neurons is refined by recurrent interactions among elements in the neural circuit.
在初级视皮层（V1）中测量的视差调谐很好地符合视差能量模型，但该模型并不能完全解释视差调谐的所有方面。这些对视差能量模型的偏离为我们提供了一些关于网络相互作用如何在视差处理中发挥作用的见解，并有助于解决立体对应问题。在这里，我们提出了一个具有递归连接的神经元电路模型，该模型可以简单地解释观察到的偏离。该模型基于从尖峰时间相关性的神经生理学观察中推断出的递归连接，并且与现有的关于视差调谐动力学的数据很好地吻合。我们进一步进行了两项额外的实验来测试模型的预测。首先，我们增大了刺激的大小，以驱动更多的神经元并提供更强的递归输入。我们的模型预测，对于较大的刺激，视差调谐会更加锐利。其次，我们显示了反相关的立体图，其中左右眼图像中的相反亮度极性的点匹配，导致视差能量模型中的视差调谐反转。在这种情况下，我们的模型预测反转的视差调谐的锐度和强度降低。对于这两个实验，从猕猴 V1 的神经生理学记录中观察到的视差调谐动力学与模型模拟预测相匹配。总体而言，这项研究的结果支持这样的观点，即虽然视差能量模型提供了 V1 神经元中视差调谐的主要解释，但 V1 神经元中的神经视差处理通过神经回路中元素之间的递归相互作用得到了改进。
