title: Bayesian inference and attentional modulation in the visual cortex.
abstract: The responses of neurons in cortical areas V2 and V4 can be significantly modulated by attention to particular locations within an input image. We show that such effects emerge naturally when perception is viewed as a probabilistic inference process governed by Bayesian principles and implemented in hierarchical cortical networks. The proposed model can explain a rich variety of attention-related responses in cortical area V4 including multiplicative modulation of tuning curves, restoration of neural responses in the presence of distracting stimuli, and influence of attention on neighboring unattended locations. Our results suggest a new interpretation of attention as a cortical mechanism for reducing perceptual uncertainty by combining top-down task-relevant information with bottom-up sensory inputs in a probabilistic manner.
authors.affiliation: Department of Computer Science and Engineering, University of Washington, Seattle, Washington 98195-2350, USA. rao@cs.washington.edu
authors.forename: Rajesh P N
authors.initials: RP
authors.lastname: Rao
mesh_terms: D000818:Animals; D001288:Attention / Q000502:physiology*; D001499:Bayes Theorem; D016571:Neural Networks, Computer; D009949:Orientation / Q000502:physiology; D010775:Photic Stimulation / Q000379:methods; D011930:Reaction Time / Q000502:physiology; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*