Surround suppression explained by long-range recruitment of local competition, in a columnar V1 model
This work addresses a fundamental question in neuroscience about cortical processing mechanisms, but it is incremental as it builds on existing anatomical and physiological knowledge.
The researchers tackled the problem of explaining sparse and uncorrelated neural responses in visual cortex columns despite shared orientation preferences, by developing a computational model that reproduces experimental observations of surround modulation and response properties.
Although neurons in columns of visual cortex of adult carnivores and primates share similar orientation tuning preferences, responses of nearby neurons are surprisingly sparse and temporally uncorrelated, especially in response to complex visual scenes. The mechanisms underlying this counter-intuitive combination of response properties are still unknown. Here we present a computational model of columnar visual cortex which explains experimentally observed integration of complex features across the visual field, and which is consistent with anatomical and physiological profiles of cortical excitation and inhibition. In this model, sparse local excitatory connections within columns, coupled with strong unspecific local inhibition and functionally-specific long-range excitatory connections across columns, give rise to competitive dynamics that reproduce experimental observations. Our results explain surround modulation of responses to simple and complex visual stimuli, including reduced correlation of nearby excitatory neurons, increased excitatory response selectivity, increased inhibitory selectivity, and complex orientation-tuning of surround modulation.