Elucidating neuronal circuits and their plasticity in the cerebral cortex is one of the important questions in neuroscience research. Here we report novel axonal trajectories and their plasticity in the mouse somatosensory barrel cortex. We selectively visualized layer 2/3 neurons using in utero electroporation and examined the axonal trajectories of layer 2/3 neurons. We found that the axons of layer 2/3 neurons preferentially run in the septal regions of layer 4 and named this axonal pattern "barrel nets." The intensity of green fluorescent protein in the septal regions was markedly higher compared with that in barrel hollows. Focal in utero electroporation revealed that the axons in barrel nets were indeed derived from layer 2/3 neurons in the barrel cortex. During development, barrel nets became visible at postnatal day 10, which was well after the initial appearance of barrels. When whisker follicles were cauterized within 3 d after birth, the whisker-related pattern of barrel nets was altered, suggesting that cauterization of whisker follicles results in developmental plasticity of barrel nets. Our results uncover the novel axonal trajectories of layer 2/3 neurons with whisker-related patterns and their developmental plasticity in the mouse somatosensory cortex. Barrel nets should be useful for investigating the pattern formation and axonal reorganization of intracortical neuronal circuits.
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http://dx.doi.org/10.1523/JNEUROSCI.6096-09.2010 | DOI Listing |
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Department of Pharmacology, University of Oxford, Oxford, UK.
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Institute of Chemistry, Military University of Technology, Kaliskiego 2, PL-00908 Warsaw, Poland.
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View Article and Find Full Text PDFPLoS One
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Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, China.
Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method.
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