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Visual Cortical Mechanisms of Perceptual Grouping: Interacting Layers, Networks, Columns, and Maps

机译:视觉分组的视觉皮层机制:交互层,网络,列和地图

摘要

The visual cortex has a laminar organintion whose circuits form functional columns in cortical maps. How this laminar architecture supports visual percepts is not well understood. A neural model proposes how the laminar circuits of Vl and V2 generate perceptual groupings that maintain sensitivity to the contrasts and spatial organization of scenic cues.The model can decisively choose which groupings cohere and survive, even while balanced excitatory and inhibitory interactions preserve contrast-sensitive measures of local boundary likelihood or strength. In the model, excitatory inputs from LGN activate layers 4 and 6 of Vl. Layer 6 activates an on-center off-surround network of inputs to layer 4. Together these layer 4 inputs preserve analog sensitivity to LGN input contrast. Layer 4 cells excite pyramidal cells in layer 2/3 which activate monosynaptic long-range horizontal excitory connections between layer 2/3 pyramidal cells, and short-range disynaptic inhibitory connections mediated by smooth stellate cells. These interactions support inward perceptual grouping between two or more boundary inducetd, but not outward grouping from a single inducer. These lJOUHlary signals feed back to layer 4 via the layer 6-to-4 on-center off-surround network. This folded feecdback joind cells in different layers into functional columnns while selecting winning groupings. Layer G in V1 also sends top-dlown signals to LGN using an on-center off-surround network, which suppresses LGN cells that do not receive feedback, while selecting, enhaneing, and synchronizing activity of those that do. The model is used to simulate psychophysical and neurophysiological data about perceptual grouping, including various Gestalt grouping laws.
机译:视觉皮层具有层状组织,其回路形成皮层图中的功能性列。这种层状结构如何支持视觉感知还没有很好地理解。一个神经模型提出了V1和V2的层流如何生成对风景线索的对比度和空间组织保持敏感性的感知分组,即使平衡的兴奋性和抑制性相互作用保持了对比敏感性,该模型也可以决定性地选择哪些分组保持一致并生存。局部边界可能性或强度的度量。在模型中,来自LGN的兴奋性输入激活了V1的第4层和第6层。第6层激活了第4层输入的中心偏心环绕网络。这些第4层输入共同保持了对LGN输入对比度的模拟灵敏度。第4层细胞激发第2/3层中的锥体细胞,后者激活第2/3层锥体细胞之间的单突触远程水平兴奋性连接,以及由平滑星状细胞介导的短距离突触抑制性连接。这些相互作用支持两个或多个边界诱导之间的向内感知分组,但不支持单个诱导者的向外分组。这些lJO UHlary信号通过中心上的6至4层非环绕网络反馈到第4层。这种折叠的Feecdback将不同层次的单元合并为功能列,同时选择了获胜分组。 V1中的G层还使用中心非环绕网络向LGN发送上达信号,从而抑制了未接收反馈的LGN单元,同时选择,增强和同步了那些活动的LGN单元。该模型用于模拟有关知觉分组的心理生理和神经生理数据,包括各种格式塔定律。

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