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Updated: Aug 16, 2026

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss
Published on: April 11, 2025
Learning top-down gain control of feature selectivity in a recurrent network model of a visual cortical area
Lars Schwabe1, Klaus Obermayer
1Berlin University of Technology, Department of Computer Science and Electrical Engineering, Franklin Str. 28/29, 10587 Berlin, Germany. schwabe@cs.tu-berlin.de
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.
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