Learning top-down gain control of feature selectivity in a recurrent network model of a visual cortical area.
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.
Schwabe
Lars
L
Berlin University of Technology, Department of Computer Science and Electrical Engineering, Franklin Str. 28/29, 10587 Berlin, Germany. schwabe@cs.tu-berlin.de
Obermayer
Klaus
K
D000222:Adaptation, Physiological; D000465:Algorithms; D001288:Attention; D006801:Humans; D008959:Models, Neurological; D008960:Models, Psychological; D010775:Photic Stimulation / Q000379:methods; D014793:Visual Cortex / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*