Learning top-down gain control of feature selectivity in a recurrent network model of a visual cortical area

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Abstract

We propose that the effects of attentional top-down modulations observed in the visual cortex reflect the simple strategy of strengthening currently relevant pathways in a task-dependent manner. To exemplify this idea, we set up a network model of a visual area and simulate the learning of a context-dependent 'go/no-go'-task. The model learns top-down gain-modulations of sensory representations based on reinforcements received from the environment. We also discuss how this idea relates to alternative interpretations like optimal coding hypotheses. © 2005 Elsevier Ltd. All rights reserved.

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Schwabe, L., & Obermayer, K. (2005). Learning top-down gain control of feature selectivity in a recurrent network model of a visual cortical area. Vision Research, 45(25–26), 3202–3209. https://doi.org/10.1016/j.visres.2005.05.028

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