Ultra-fast detection of salient contours through horizontal connections in the primary visual cortex

Salient features instantly attract visual attention to their location and are crucial for object recognition. Experiments in ultra-fast visual perception have shown that object recognition can be surprisingly accurate given only ~20 ms of observation. Such short times exclude neural dynamics of top-down feedback and require fast mechanisms of low-level feature detection. We derive a neural model of the primary visual cortex with physiologically parameterized horizontal connections that reinforce salient features, and apply it to detect salient contours on ultra-fast time scales. Model performance qualitatively matches experimental results for human perception of contours, suggesting rapid neural mechanisms involving feedforward horizontal connections can be used to distinguish low-level objects.