Mechanisms of Neural Representation and Segregation of Multiple Spatially Separated Visual Stimuli.

Segregating objects from one another and the background is essential for scene understanding, object recognition, and visually guided action. In natural scenes, it is common to encounter spatially separated stimuli, such as distinct figure-ground regions, adjacent objects, and partial occlusions. Neurons in mid- and high-level visual cortex have large receptive fields (RFs) that often encompass multiple, spatially separated stimuli. It is unclear how neurons represent and segregate these stimuli within their RFs. We investigated this question by recording the neuronal responses in the middle temporal (MT) cortex from two male macaque monkeys to multiple moving stimuli. We placed a motion border between two spatially separated random-dot patches within the RFs that moved in two different directions. We varied the vector average direction of the stimuli to characterize the full direction tuning curves. Across motion directions, responses to multiple stimuli were systematically biased toward the stimulus located at the neuron's more-preferred RF subregion. The sign and magnitude of this spatial-location bias were correlated with the neuron's spatial preference measured with single patches presented in isolation. We demonstrated that neuronal responses to multiple stimuli can be captured by an extended divisive normalization model, as a sum of the responses elicited by individual stimuli, weighted by the neuron's spatial preference. We also proposed a circuit implementation for the extended normalization model. Our results indicate that MT leverages spatial selectivity within the RFs to represent spatially separated moving stimuli. The spatial-location bias in neuronal responses enables individual components of multiple stimuli to be represented by a population of neurons with heterogeneous spatial preferences, providing a neural substrate for segregating multiple visual stimuli.