In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous i ...
Traditional models posit that visual processing is local and feedforward. In this vein, crowding is explained to be the result of pooling of target and flanker features in early visual areas. Specifically, it has been suggested that when the target and fla ...
Objective. Optic nerve's intraneural stimulation is an emerging neuroprosthetic approach to provide artificial vision to totally blind patients. An open question is the possibility to evoke individual non-overlapping phosphenes via selective intraneural op ...
Retinal prostheses hold the promise of restoring vision in totally blind people. However, a decade of clinical trials highlighted quantitative limitations hampering the possibility of reaching this goal. A key challenge in retinal stimulation is to indepen ...
Sight restoration through retinal prostheses was still a mere dream a century ago. Current challenges are even greater: providing a quantitatively and qualitatively useful artificial vision to late blind patients. Existing approaches all face engineering a ...
Thanks to recent technological advances in microelectronics and bioengineering, it is now possible to restore lost or impaired sensory modalities by interfering the nervous system with elec-tronic devices and artificially reproducing the electrical encodin ...
Among our five senses, we rely mostly on audition and vision to perceive an environment. Our ears are able to detect stimuli from all directions, especially from obstructed and far-away objects. Even in smoke, harsh weather conditions, or at night â situ ...
Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural ...
In this paper, we study how to extract visual concepts to understand landscape scenicness. Using visual feature representations from a Convolutional Neural Network (CNN), we learn a number of Concept Activation Vectors (CAV) aligned with semantic concepts ...
