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Learning Top-Down Gain Control in a Recurrent Network Model of a Visual Cortical Area
Citation key Schwabe2005d
Author Schwabe, L. and Obermayer, K.
Pages 3202 – 3209
Year 2005
DOI doi:10.1016/j.visres.2005.05.028
Journal Vision Research
Volume 45
Number 25 – 26
Publisher Elsevier
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.
Bibtex Type of Publication Selected:adaptation
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