Visual objects contain rich local high-order patterns such as curvature, corners and junctions. In standard hierarchical model of visual object recognition, V1 neurons were commonly assumed to code local orientation components of those high-order patterns. Tang’s group performed large-scale two-photon imaging in awake macaques, and systematically characterized V1 neuronal responses to an extensive set of stimuli. They found a large percentage of neurons in the V1 superficial layer responded much stronger to complex patterns such as corners, junctions, and curvature than to their oriented line or edge components. These results suggest that those individual V1 neurons could play the role in detecting local high-order visual patterns in the early stage of object recognition hierarchy.

This work was supported by the National Natural Science Foundation of China No. 31730109, National Natural Science Foundation of China Outstanding Young Researcher Award 30525016, a project 985 grant of Peking University, National Basic Research Program of China (2017YFA0105201), Beijing Municipal Commission of Science and Technology under contract No. Z151100000915070.

Figure 1 The complex pattern selectivity of macaque V1 neurons

References:

1) Tang S et al. (2017) Complex Pattern Selectivity in Macaque Primary Visual Cortex Revealed by Large-Scale Two-Photon Imaging. Current Biology. DOI:http://dx.doi.org/10.1016/j.cub.2017.11.039.

2) Li M, Liu F, Jiang H, et al. (2017) Long-Term Two-Photon Imaging in Awake Macaque M&111nkey. Neuron, 2017, 93(5):1049.