Figure-ground discrimination refers to the perception of an object, the figure, against a nondescript background. Neural mechanisms of figure-ground detection have been associated with feedback interactions between higher centers and primary visual cortex and have been held to index the effect of global analysis on local feature encoding. Here, in recordings from visual thalamus of alert primates, we demonstrate a robust enhancement of neuronal firing when the figure, as opposed to the ground, component of a motion-defined figure-ground stimulus is located over the receptive field. In this paradigm, visual stimulation of the receptive field and its near environs is identical across both conditions, suggesting the response enhancement reflects higher integrative mechanisms. It thus appears that cortical activity generating the higher-order percept of the figure is simultaneously reentered into the lowest level that is anatomically possible (the thalamus), so that the signature of the evolving representation of the figure is imprinted on the input driving it in an iterative process.
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http://dx.doi.org/10.1073/pnas.1405162112 | DOI Listing |
Sci Rep
January 2025
Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, 224001, Jiangsu, China.
Convolutional Neural Networks (CNNs) have achieved remarkable segmentation accuracy in medical image segmentation tasks. However, the Vision Transformer (ViT) model, with its capability of extracting global information, offers a significant advantage in contextual information compared to the limited receptive field of convolutional kernels in CNNs. Despite this, ViT models struggle to fully detect and extract high-frequency signals, such as textures and boundaries, in medical images.
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January 2025
College of Computer Science and Software Engineering, Hohai University, Nanjing, 211100, China.
Crowd counting aims to estimate the number, density, and distribution of crowds in an image. While CNN-based crowd counting methods have been effective, head-scale variation and complex background remain two major challenges for crowd counting. Therefore, we propose a multiscale region calibration network called MRCNet to effectively address these challenges.
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January 2025
School of Electronics and Information Engineering, Wuyi University, Jiangmen, 529020, Guangdong, China.
Facial beauty prediction (FBP) is a leading area of research in artificial intelligence. Currently, there is a small amount of labeled data and a large amount of unlabeled data in the FBP database. The features extracted by the model based on supervised training are limited, resulting in low prediction accuracy.
View Article and Find Full Text PDFJ Comput Neurosci
January 2025
Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, USA.
Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in the absence of visual inputs, width scales with the peak time for time cells and with distance for place cells. Building on earlier computational work, we examined how neurons with such properties can emerge through self-supervised learning.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
School of Computer and Control Engineering, Yantai University, Yantai, China.
Background: Skin lesion segmentation plays a significant role in skin cancer diagnosis. However, due to the complex shapes, varying sizes, and different color depths, precise segmentation of skin lesions is a challenging task. Therefore, the aim of this study was to design a customized deep learning (DL) model for the precise segmentation of skin lesions, particularly for complex shapes and small target lesions.
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