Long-range suppressive influences from the visual surround can be explained by anatomically plausible recruitment of local competition in a model of columnar cortex

Although neurons in columns of tissue in visual cortex of adult carnivores and primates share similar preferences for the orientation of a visual stimulus [1,2], they can nevertheless have sparse and temporally uncorrelated firing visual response properties [3–5]. This effect is supported by the observation that long-range excitatory connections between cortical neurons are functionally specific, because they interconnect columns with similar orientation preference [6,7], and local short-range ones are unspecific and sparse. Coupled with strong local inhibition, this network architecture is a good recipe for local competition arranged within a cortical column [8]. In this paper we propose a model architecture that is consistent with these experimental and anatomical findings, and which explains the emergence of orientation-tuned surround suppression. We explore the effect of local columnar competition, coupled with local and long-range functional specificity, as substrates for integration of responses from the visual surround in columnar visual cortex. In addition, we show how presentation of simulated full-field complex visual stimuli, designed to approximate visual scenes, leads to reduced correlation of local excitatory responses and increased excitatory response selectivity (lifetime sparseness). These effects occurred simultaneously with increased inhibitory activity and decreased inhibitory sparseness, consistent with recordings of excitatory and inhibitory neurons in cortex [9,10]. In our networks competition, reduced correlation and increased sparseness depended on both local and long-range specific excitatory connectivity. The mechanism of local competition implemented in our model explains several aspects of integration of information from the visual surround, and is consistent with experimentally measured spatial profiles of cortical excitation and inhibition. Author Summary Cells in the visual areas of the brain are active when an image appears in a small area of visual space. But the way a cell responds also depends on what images cover the rest of visual space, in complex ways that we don’t yet understand. In experiments, this leads to some cells shutting down when visual patterns are shown to an animal on a screen. We developed a computational model for the visual processing areas of the brain, in animals such as cats, monkeys and humans that explains this phenomenon: we propose that nearby nerve cells in the brain compete with each other when they respond to visual information coming from the eye. In our model, this competition leads to some nerve cells shutting down, in a similar way as is seen in experiments with real brains. Ensuring that only a few nerve cells are active makes the brain more efficient at responding to the visual world. This is a general principle that could very well be in place also in other areas of the brain. We believe that this model will lead to a better understanding of how networks of nerve cells in the brain are connected and share information.