Laminar subnetworks of response suppression in macaque primary visual cortex.

Cortical inhibition plays an important role in information processing in the brain. However, the mechanisms by which inhibition and excitation are coordinated to generate functions in the six layers of the cortex remain unclear. Here, we measured laminar-specific responses to stimulus orientations in primary visual cortex (V1) of awake monkeys (male, Macaca mulatta). We distinguished inhibitory effects (suppression) from excitation, by taking advantage of the separability of excitation and inhibition in the orientation and time domains. We found two distinct types of suppression governing different layers. Fast suppression (FS) was strongest in input layers (4C and 6), and slow suppression (SS) was three times stronger in output layers (2/3 and 5). Interestingly, the two types of suppression were correlated with different functional properties measured with drifting gratings. FS was primarily correlated with orientation selectivity in input layers (r = -0.65, p < 10-9) whereas SS was primarily correlated with surround suppression in output layers (r = 0.61, p < 10-4). The earliest SS in layer 1 indicates the origin of cortical feedback for SS, in contrast to the feed-forward/recurrent origin of FS. Our results reveal two V1 laminar sub-networks with different response suppression that may provide a general framework for laminar processing in other sensory cortices.SignificanceThis study sought to understand inhibitory effects (suppression) and their relationships with functional properties in the six different layers of the cortex. We found that the diversity of neural responses across layers in primary visual cortex (V1) could be fully explained by one excitatory and two suppressive components (fast and slow suppression). The distinct laminar distributions, origins and functional roles of the two types of suppression provided a simplified representation of the differences between two V1 sub-networks (input network and output network). These results not only help to elucidate computational principles in macaque V1, but also provide a framework for general computation of cortical laminae in other sensory cortices.