Microsaccades are involuntary and very small eye movements during fixation. Recently, the microsaccade-related neural dynamics have been extensively investigated both in experiments and by constructing neural network models. Experimentally, microsaccades also exhibit many behavioral properties. It's well known that the behavior properties imply the underlying neural dynamical mechanisms, and so are determined by neural dynamics. The behavioral properties resulted from neural responses to microsaccades, however, are not yet understood and are rarely studied theoretically. Linking neural dynamics to behavior is one of the central goals of neuroscience. In this paper, we provide behavior predictions on spatiotemporal properties of microsaccades according to microsaccade-induced neural dynamics in a cascading network model, which includes both retinal adaptation and short-term depression (STD) at thalamocortical synapses. We also successfully give experimental tests in the statistical sense. Our results provide the first behavior description of microsaccades based on neural dynamics induced by behaving activity, and so firstly link neural dynamics to behavior of microsaccades. These results indicate strongly that the cascading adaptations play an important role in the study of microsaccades. Our work may be useful for further investigations of the microsaccadic behavioral properties and of the underlying neural dynamical mechanisms responsible for the behavioral properties.
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http://dx.doi.org/10.1038/srep35255 | DOI Listing |
J Biol Chem
January 2025
Department of Biochemistry, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, Abu Dhabi, UAE; Zayed Center for Health Sciences, United Arab Emirates University, Al Ain, Abu Dhabi, UAE; ASPIRE Precision Medicine Research Institute Abu Dhabi (PMRI-AD), United Arab Emirates University, Al Ain, UAE. Electronic address:
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January 2025
International Research Center for Cognitive Applied Neuroscience (IrcCAN), Università Cattolica del Sacro Cuore, Milan, Italy; Research Unit in Affective and Social Neuroscience, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy.
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View Article and Find Full Text PDFNeural Netw
January 2025
School of Computer Science and Technology, East China Normal University, 200062, Shanghai, China.
Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation. Existing methods rely on the heavy SR models to enhance low-resolution (LR) images of different degradation levels, which significantly restricts their practical deployments on resource-limited devices. In this paper, we propose a novel Dynamic Channel Splitting scheme for efficient Real-world Image Super-Resolution, termed DCS-RISR.
View Article and Find Full Text PDFNeural Netw
January 2025
Department of Electronic Engineering, Tsinghua University, Beijing, China. Electronic address:
Out-of-graph node representation learning aims at learning about newly arrived nodes for a dynamic graph. It has wide applications ranging from community detection, recommendation system to malware detection. Although existing methods can be adapted for out-of-graph node representation learning, real-world challenges such as fixed in-graph node embedding and data diversity essentially limit the performance of these methods.
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January 2025
Medical Big Data Lab, Shenzhen Research Institute of Big Data, Shenzhen, 518172, China. Electronic address:
Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced prediction accuracy and alleviated neurosurgeons from the burden of manual prognosis assessment. However, uni-modal methods have shown suboptimal performance due to the intricate pathophysiology of the ICH.
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