In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism’s performance. We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neuronal networks where rapid responses and low spike counts are desired.

在视觉系统和计算机视觉的研究中，重点通常是对比边缘。然而，灵长类视觉系统包含大量对空间对比度不敏感的细胞，而是对其视野的均匀均匀照明做出反应。此信息的目的尚不清楚。在这里，我们提出了一种机制，该机制基于延迟编码和尖峰时间巧合，以纯粹的前馈方式检测视觉区域中的特征同质性，因此是快速的。我们展示了同质性信息如何与对比边缘的信息相互作用以潜在地支持快速图像分割。此外，我们分析了神经元串扰（噪声) 影响机制的性能。我们表明，串扰的不利影响可以通过形成双相突触后事件的延迟前馈抑制来部分减轻。前馈抑制的延迟允许有效地控制时间积分窗口的大小，从而控制重合阈值。所提出的模型基于支持低延迟处理的纯前馈架构中的单尖峰延迟代码，使其成为需要快速响应和低尖峰计数的尖峰神经元网络中的有吸引力的计算方案。