Topic: object segmentation

Develop and analyse an integrate-and-fire neural-network model of visual perception using the LAMINART framework. The project studies multiple-object tracking, object segmentation and how model dynamics explain experimental behaviour. The appointment lasts up to 36 months in the Research Unit of Health and Cognitive Psychology at the Esbjerg campus, with collaboration in Odense and teaching duties. Applications are submitted electronically in English.

Visual textures efficiently represent real-world information that is essential for perceptual tasks like pattern detection, object segmentation and classification. This information appears to be encoded in intermediate areas along the primate ventral visual stream (areas V2-V4), but how textural selectivity emerges at the circuit level, and whether the neural architectures that support texture processing are shared across mammalian species is currently unknown. We addressed these questions in the mouse by first examining the mouse’s perceptual ability to discriminate higher order texture stat

Retinal projections often poorly represent the structure of the physical world: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. Visual cortex is equipped with specialized mechanisms for sorting these two types of features according to their utility in interpreting the scene, however we know little or nothing about their perceptual computations. I will present novel paradigms for the characterization of these processes in human vision, alongside ex