 A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field StructureThomas Miconi and Rufin VanRullen PLOS Computational Biology, 2016, vol. 12, issue 2, 1-18 Abstract: Visual attention has many effects on neural responses, producing complex changes in firing rates, as well as modifying the structure and size of receptive fields, both in topological and feature space. Several existing models of attention suggest that these effects arise from selective modulation of neural inputs. However, anatomical and physiological observations suggest that attentional modulation targets higher levels of the visual system (such as V4 or MT) rather than input areas (such as V1). Here we propose a simple mechanism that explains how a top-down attentional modulation, falling on higher visual areas, can produce the observed effects of attention on neural responses. Our model requires only the existence of modulatory feedback connections between areas, and short-range lateral inhibition within each area. Feedback connections redistribute the top-down modulation to lower areas, which in turn alters the inputs of other higher-area cells, including those that did not receive the initial modulation. This produces firing rate modulations and receptive field shifts. Simultaneously, short-range lateral inhibition between neighboring cells produce competitive effects that are automatically scaled to receptive field size in any given area. Our model reproduces the observed attentional effects on response rates (response gain, input gain, biased competition automatically scaled to receptive field size) and receptive field structure (shifts and resizing of receptive fields both spatially and in complex feature space), without modifying model parameters. Our model also makes the novel prediction that attentional effects on response curves should shift from response gain to contrast gain as the spatial focus of attention drifts away from the studied cell.Author Summary: Exerting visual attention results in profound changes in the activity of neurons in visual areas of the brain. Attention increases the firing of some neurons, decreases that of others, moves and resizes the receptive fields of individual neurons, and changes their preferred features according to what is being attended. How are these complex, subtle effects generated? While several models explain various subsets of these effects, a consistent explanation compatible with anatomical and physiological observations remains elusive. Here we show that the apparently complex and multifaceted effects of attention on neural responses can be explained as the automatic consequence of a top-down modulation, falling on higher visual areas (as suggested by anatomical observations), and interacting with short-range inhibition and feedback connections between areas. Our model only assumes the existence of well-known features of brain organization (reciprocal inter-area connections, mutual inhibition between neighboring neurons) to explain a wide range of attentional effects, including apparently finely-tuned effects (complex shifts in feature preferences, automatic scaling of competitive effects to receptive field size, resizing or shifting of receptive fields, etc). Our model also makes novel, testable predictions about the effect of certain attentional manipulations on neural responses. Date: 2016 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004770 DOI: 10.1371/journal.pcbi.1004770 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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