Laboratory of Neurobiology, The Rockefeller University, New York, NY 10065, USA.
Proc Natl Acad Sci U S A. 2011 Jun 14;108(24):9739-46. doi: 10.1073/pnas.1105855108. Epub 2011 May 13.
The ability to derive meaning from complex sensory input requires the integration of information over space and time, as well as cognitive mechanisms to shape that integration. We studied these processes in the primary visual cortex (V1), where neurons are thought to integrate visual inputs along contours defined by an association field (AF). We recorded extracellularly from single cells in macaque V1 to map the AF, by using an optimization algorithm to find the contours that maximally activated individual cells. We combined the algorithm with a delayed-match-to-sample task, to test how the optimal contours might be molded by the monkey's expectation for particular cue shapes. We found that V1 neurons were selective for complex shapes, a property previously ascribed to higher cortical areas. Furthermore, the shape selectivity was reprogrammed by perceptual task: Over the whole network, the optimal modes of geometric selectivity shifted between distinct subsets of the AF, alternately representing different stimulus features known to predominate in natural scenes. Our results suggest a general model of cortical function, whereby horizontal connections provide a broad domain of potential associations, and top-down inputs dynamically gate these linkages to task switch the function of a network.
从复杂的感官输入中获取意义的能力需要在空间和时间上整合信息，以及认知机制来塑造这种整合。我们在初级视觉皮层（V1）中研究了这些过程，在 V1 中，神经元被认为沿着关联场（AF）定义的轮廓整合视觉输入。我们通过使用优化算法来找到最大程度激活单个细胞的轮廓，从猕猴 V1 中记录细胞外放电以绘制 AF。我们将算法与延迟匹配样本任务相结合，以测试猴子对特定线索形状的期望如何塑造最佳轮廓。我们发现 V1 神经元对复杂形状具有选择性，这一特性以前归因于更高的皮质区域。此外，知觉任务重新编程了形状选择性：在整个网络中，几何选择性的最佳模式在 AF 的不同子集之间交替，分别代表在自然场景中占主导地位的不同刺激特征。我们的结果表明了一种一般的皮质功能模型，其中水平连接提供了广泛的潜在关联域，而自上而下的输入则动态地将这些联系门控到任务切换网络的功能。
