A key feature of perception is that the interpretation of a single, continuously available stimulus can change from time to time. This aspect of perception is well illustrated by the use of ambiguous figures that can be seen in two different ways. When people view such a stimulus they almost universally describe what they are seeing as jumping between two states. If it is agreed that this perceptual phenomenon is causally linked to the activity of nerve cells, the state jumps would have to occur in conjunction with changes in neural activity somewhere in the nervous system. Our experiments suggest that hippocampal place cells are part of a perceptual system. We conducted variations of a 'cue-card rotation' experiment on rats in which the angular position of a prominent visual stimulus on the wall of cylinder is changed in the rat's presence. The three main results are that (i) place-cell firing fields remain stationary if the cue is rotated by 180 degrees, so the relation between the cue and the field is altered; (ii) firing fields rotate by 45 degrees when the cue is rotated by 45 degrees, so the relation between the field and the card is maintained; and (iii) if the cue is first rotated by 180 degrees and then rotated in a series of 45 degrees steps, the field winds up at a different angular position relative to the card when the card is back in its original position. Thus, place cells can fire in two different ways in response to a continuously viewed stimulus. We conclude that place cells reveal that the hippocampal mapping system also has properties expected of a perceptual system.
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http://dx.doi.org/10.1098/rstb.1997.0137 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Department of Psychology, City College, City University of New York, New York, NY 10031.
Looking at the world often involves not just seeing things, but feeling things. Modern feedforward machine vision systems that learn to perceive the world in the absence of active physiology, deliberative thought, or any form of feedback that resembles human affective experience offer tools to demystify the relationship between seeing and feeling, and to assess how much of visually evoked affective experiences may be a straightforward function of representation learning over natural image statistics. In this work, we deploy a diverse sample of 180 state-of-the-art deep neural network models trained only on canonical computer vision tasks to predict human ratings of arousal, valence, and beauty for images from multiple categories (objects, faces, landscapes, art) across two datasets.
View Article and Find Full Text PDFVision (Basel)
January 2025
Centre for the Study of Perceptual Experience, Department of Philosophy, University of Glasgow, Glasgow G12 8QQ, UK.
Mental imagery is claimed to underlie a host of abilities, such as episodic memory, working memory, and decision-making. A popular view holds that mental imagery relies on the perceptual system and that it can be said to be 'vision in reverse'. Whereas vision exploits the bottom-up neural pathways of the visual system, mental imagery exploits the top-down neural pathways.
View Article and Find Full Text PDFFront Robot AI
January 2025
Department of Materials and Production, Aalborg University, Aalborg, Denmark.
Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Room 3209, CBIS/BME, 110 8th Street, Troy, NY 12180, USA, Troy, 12180, UNITED STATES.
We strive to overcome the challenges posed by ring artifacts in X-ray computed tomography (CT) by developing a novel approach for generating training data for deep learning-based methods. Training such networks require large, high quality, datasets that are often generated in the data domain, time-consuming and expensive. Our objective is to develop a technique for synthesizing realistic ring artifacts directly in the image domain, enabling scalable production of training data without relying on specific imaging system physics.
View Article and Find Full Text PDFAngew Chem Int Ed Engl
January 2025
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Key Laboratory of Advanced Marine Materials, 1219 Zhongguan West Road, 315201, Ningbo, CHINA.
Many marine organisms feature sensitive sensory-perceptual systems to sense the surrounding environment and respond to disturbance with intense bioluminescence. However, it remains a great challenge to develop artificial materials that can sense external disturbance and simultaneously activate intense luminescence, although such materials are attractive for visual sensing and intelligent displays. Herein, we present a new class of bioinspired smart gels constructed by integrating hydrophilic polymeric networks, metastable supersaturated salt and fluorophores containing heterogenic atoms.
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