Gove A, Grossberg S, Mingolla E
MIT Lincoln Laboratory, Lexington, MA, USA.
Vis Neurosci. 1995 Nov-Dec;12(6):1027-52. doi: 10.1017/s0952523800006702.
A neural network model is developed to explain how visual thalamocortical interactions give rise to boundary percepts such as illusory contours and surface percepts such as filled-in brightnesses. Top-down feedback interactions are needed in addition to bottom-up feed-forward interactions to simulate these data. One feedback loop is modeled between lateral geniculate nucleus (LGN) and cortical area V1, and another within cortical areas V1 and V2. The first feedback loop realizes a matching process which enhances LGN cell activities that are consistent with those of active cortical cells, and suppresses LGN activities that are not. This corticogeniculate feedback, being endstopped and oriented, also enhances LGN ON cell activations at the ends of thin dark lines, thereby leading to enhanced cortical brightness percepts when the lines group into closed illusory contours. The second feedback loop generates boundary representations, including illusory contours, that coherently bind distributed cortical features together. Brightness percepts form within the surface representations through a diffusive filling-in process that is contained by resistive gating signals from the boundary representations. The model is used to simulate illusory contours and surface brightness induced by Ehrenstein disks, Kanizsa squares, Glass patterns, and café wall patterns in single contrast, reverse contrast, and mixed contrast configurations. These examples illustrate how boundary and surface mechanisms can generate percepts that are highly context-sensitive, including how illusory contours can be amodally recognized without being seen, how model simple cells in V1 respond preferentially to luminance discontinuities using inputs from both LGN ON and OFF cells, how model bipole cells in V2 with two colinear receptive fields can help to complete curved illusory contours, how short-range simple cell groupings and long-range bipole cell groupings can sometimes generate different outcomes, and how model double-opponent, filling-in and boundary segmentation mechanisms in V4 interact to generate surface brightness percepts in which filling-in of enhanced brightness and darkness can occur before the net brightness distribution is computed by double-opponent interactions.
开发了一种神经网络模型，以解释视觉丘脑皮质相互作用如何产生诸如虚幻轮廓之类的边界感知以及诸如填充亮度之类的表面感知。除了自下而上的前馈相互作用之外，还需要自上而下的反馈相互作用来模拟这些数据。一个反馈回路在外侧膝状体核（LGN）和皮质区域V1之间建模，另一个在皮质区域V1和V2之间建模。第一个反馈回路实现了一个匹配过程，该过程增强了与活跃皮质细胞活动一致的LGN细胞活动，并抑制了不一致的LGN活动。这种皮质膝状体反馈，由于具有终端抑制和方向选择性，还增强了细暗线末端的LGN ON细胞激活，从而当这些线组合成封闭的虚幻轮廓时，导致增强的皮质亮度感知。第二个反馈回路生成边界表示，包括虚幻轮廓，将分布式皮质特征连贯地绑定在一起。亮度感知通过扩散填充过程在表面表示中形成，该过程由来自边界表示的电阻门控信号所限制。该模型用于模拟由埃伦斯坦圆盘、卡尼萨方块、格拉斯图案和咖啡墙图案在单对比度、反对比度和混合对比度配置下诱导的虚幻轮廓和表面亮度。这些例子说明了边界和表面机制如何产生高度依赖上下文的感知，包括虚幻轮廓如何在未被看到的情况下被无模态识别，V1中的模型简单细胞如何使用来自LGN ON和OFF细胞的输入优先响应亮度不连续性，V2中具有两个共线感受野的模型双极细胞如何有助于完成弯曲的虚幻轮廓，短程简单细胞分组和长程双极细胞分组有时如何产生不同的结果，以及V4中的模型双对立、填充和边界分割机制如何相互作用以产生表面亮度感知，其中在通过双对立相互作用计算净亮度分布之前，可以发生增强亮度和暗度的填充。
