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Fred Hamker, Frederik Beuth; A mechanistic cortical microcircuit of attention for amplification, normalization and suppression. Journal of Vision 2015;15(12):1254. doi: 10.1167/15.12.1254.
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© ARVO (1962-2015); The Authors (2016-present)
Computational models of visual attention have replicated a large number of data from visual attention experiments. However, typically each computational model has been shown to account for only a few data sets. Thus, a general account to fully understand the attentive dynamics in the visual cortex is still missing. To reveal a set of general principles that determine attentional selection in visual cortex, we developed a novel model of attention, particularly focused on explaining single cell recordings in multiple brain areas. Among those are spatial- and feature-based biased competition, modulation of the contrast response function, modulation of the neuronal tuning curve and modulation of surround suppression. Neurons are modeled by a dynamic rate code. In contrast to previous models, we use a two layer structure inspired by the layered cortical architecture which implements amplification, divisive normalization and suppression as well as spatial pooling. 12 different attentional experiments have been simulated. As a proof of concept the model has been fitted to those 12 different data sets. Concluding, our model proposes that attentional selection emerges from three basic neural mechanisms which are amplification, normalized feature and surround suppression. We hypothesize that these attentive mechanisms are not distinct from other neural phenomena and thus also contribute to multiple perceptual observations such as crowding and feature inheritance.
Meeting abstract presented at VSS 2015
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