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A neural mechanism for detecting object motion during self-motion.
Kim, HyungGoo R; Angelaki, Dora E; DeAngelis, Gregory C.
Afiliación
  • Kim HR; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
  • Angelaki DE; Department of Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, United States.
  • DeAngelis GC; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea.
Elife ; 112022 06 01.
Article en En | MEDLINE | ID: mdl-35642599
Detection of objects that move in a scene is a fundamental computation performed by the visual system. This computation is greatly complicated by observer motion, which causes most objects to move across the retinal image. How the visual system detects scene-relative object motion during self-motion is poorly understood. Human behavioral studies suggest that the visual system may identify local conflicts between motion parallax and binocular disparity cues to depth and may use these signals to detect moving objects. We describe a novel mechanism for performing this computation based on neurons in macaque middle temporal (MT) area with incongruent depth tuning for binocular disparity and motion parallax cues. Neurons with incongruent tuning respond selectively to scene-relative object motion, and their responses are predictive of perceptual decisions when animals are trained to detect a moving object during self-motion. This finding establishes a novel functional role for neurons with incongruent tuning for multiple depth cues.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Percepción de Movimiento Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: Elife Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Percepción de Movimiento Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: Elife Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido