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In studies of the visual system as well as in computer vision, the focus isoften on contrast edges. However, the primate visual system contains a largenumber of cells that are insensitive to spatial contrast and, instead, respondto uniform homogeneous illumination of their visual field. The purpose of thisinformation remains unclear. Here, we propose a mechanism that detects featurehomogeneity in visual areas, based on latency coding and spike timecoincidence, in a purely feed-forward and therefore rapid manner. Wedemonstrate how homogeneity information can interact with information oncontrast edges to potentially support rapid image segmentation. Furthermore, weanalyze how neuronal crosstalk (noise) affects the mechanism's performance. Weshow that the detrimental effects of crosstalk can be partly mitigated throughdelayed feed-forward inhibition that shapes bi-phasic post-synaptic events. Thedelay of the feed-forward inhibition allows effectively controlling the size ofthe temporal integration window and, thereby, the coincidence threshold. Theproposed model is based on single-spike latency codes in a purely feed-forwardarchitecture that supports low-latency processing, making it an attractivescheme of computation in spiking neuronal networks where rapid responses andlow spike counts are desired.

Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code