Visual input during natural behavior is highly dependent on movements of the eyes and head, but how information about eye and head position is integrated with visual processing during free movement is unknown, as visual physiology is generally performed under head fixation. To address this, we performed single-unit electrophysiology in V1 of freely moving mice while simultaneously measuring the mouse's eye position, head orientation, and the visual scene from the mouse's perspective. From these measures, we mapped spatiotemporal receptive fields during free movement based on the gaze-corrected visual input. Furthermore, we found a significant fraction of neurons tuned for eye and head position, and these signals were integrated with visual responses through a multiplicative mechanism in the majority of modulated neurons. These results provide new insight into coding in the mouse V1 and, more generally, provide a paradigm for investigating visual physiology under natural conditions, including active sensing and ethological behavior.
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http://dx.doi.org/10.1016/j.neuron.2022.08.029 | DOI Listing |
Nutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.
Universal image segmentation aims to handle all segmentation tasks within a single model architecture and ideally requires only one training phase. To achieve task-conditioned joint training, a task token needs to be used in the multi-task training to condition the model for specific tasks. Existing approaches generate the task token from a text input (e.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Department of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia.
: The rapid global spread of the monkeypox virus has led to serious issues for public health professionals. According to related studies, monkeypox and other types of skin conditions can spread through direct contact with infected animals, humans, or contaminated items. This disease can cause fever, headaches, muscle aches, and enlarged lymph nodes, followed by a rash that develops into lesions.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Statistics and Data Science, Nankai University, Tianjin, 300071, China. Electronic address:
Both the single-image exposure correction (SEC) methods and multi-image exposure fusion (MEF) methods aim to obtain a well-exposed image, but from different number of input image(s). Despite their promising performance on the specific SEC or MEF task, few of these methods explores the inherent correlation behind the same goal of the SEC or MEF tasks. In this paper, we propose to unify the SEC and MEF tasks into a unified task of "Arbitrary-Frame Exposure Correction" (AF-EC) with arbitrary number of input frames.
View Article and Find Full Text PDFJ Imaging
January 2025
Istituto di Scienze Applicate e Sistemi Intelligenti (ISASI), Consiglio Nazionale delle Ricerche (CNR), DHITECH, Campus Università del Salento, Via Monteroni s.n., 73100 Lecce, Italy.
Despite significant advancements in the automatic classification of skin lesions using artificial intelligence (AI) algorithms, skepticism among physicians persists. This reluctance is primarily due to the lack of transparency and explainability inherent in these models, which hinders their widespread acceptance in clinical settings. The primary objective of this study is to develop a highly accurate AI-based algorithm for skin lesion classification that also provides visual explanations to foster trust and confidence in these novel diagnostic tools.
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