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Reconstructing huge connection cpa networks via scientific occasion sequence information.
By realizing the intrinsic gain via proportionally scaled device area, we show that the big and small synapse can be implemented at the device-level without modifications to the operational scheme. Through neural network simulations, we confirm that RRAM-based hybrid synapse with the proposed learning method achieves maximum accuracy of 97 %, comparable to floating-point implementation (97.92%) of the software even with only 50 conductance states in each device. RZ-2994 Our results promise training efficiency and inference accuracy by using existing RRAM devices.Spiking Neuromorphic systems have been introduced as promising platforms for energy-efficient spiking neural network (SNNs) execution. SNNs incorporate neuronal and synaptic states in addition to the variant time scale into their computational model. Since each neuron in these networks is connected to many others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, a precise communication latency is also needed, although SNN is tolerant to the spike delay variation in some limits when it is seen as a whole. The two-dimensional packet-switched network-on-chip was proposed as a solution to provide a scalable interconnect fabric in large-scale spike-based neural networks. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. Combining these two emerging technologies provides a new horizon for IC design to satisfy the high requirements of low power and small footprint in emerging AI applications. Moreover, ault recovery with graceful performance degradation.Electroacupuncture (EA) is a promising clinical approach to treating posttraumatic stress disorder (PTSD), yet the mechanisms whereby EA can alleviate anxiety and other PTSD symptoms have yet to be clarified. In the present report, rats underwent EA for 14 consecutive days following modified single prolonged stress (MSPS) exposure. These animals were then evaluated in open field and elevated plus maze tests (OFT and EPM), while Fos immunohistochemical staining was performed to assess ventromedial prefrontal cortex (vmPFC) functional activation. In addition, an extracellular recording and stimulation system was used to analyze vmPFC inputs into the ventral tegmental area (VTA) in these rats. Temporary vmPFC inactivation was further performed to assess whether this was sufficient to reverse the anxiolytic effects of EA. Overall, rats that underwent EA treatment spent more time in the central region (OFT) and the open arm (EPM) relative to MSPS model animals (P less then 0.05). These MSPS model animals also exhibited significantly fewer activated Fos-positive nuclei in the vmPFC following behavioral testing, while EA was associated with a significant relative increase in c-Fos expression in this region. The transient inactivation of the vmPFC was sufficient to reverse the effects of EA treatment on anxiety-like behaviors in MSPS model rats. MSPS and SEA rats exhibiting no differences in bursting activity between baseline and vmPFC stimulation, whereas bursting activity rose relative to baseline upon ventral mPFC stimulation in EA treated and control rats. Together, these findings indicate that the vmPFC and its inputs into the VTA are functionally linked to the anxiolytic activity of EA, implicating this pathway in the EA-mediated treatment of PTSD.Postoperative neurocognitive disorder (PND) is one of the most common postoperative neurological complications in aged patients, characterized by mental disorder, anxiety, personality changes, and impaired memory. At present, the molecular mechanism of PND remains largely unclear, and the ideal biomarker for clinical diagnosis and prognosis are lacking. Circular RNA (circRNA) and microRNA (miRNA), as unique non-coding RNAs, affecting the regulation of miRNAs on genes and further intervening in the progression of diseases through the sponge action between the two. Besides, it could be served as novel biomarkers in various diseases. In order to detect the differential expression profiles of genes caused by PND, a total of 26 18-month-old male C57BL/6 mice were randomly assigned to control group and PND group. Behavioral tests showed that mice in the PND group had impaired cognitive function compared with the control group. Three mice in each group were randomly selected to harvest the brain for analysis the expressions of circRNAs, miRNAs, and mRNAs in the prefrontal cortex by next-generation sequencing (NGS) technology. Differentially expressed genes, including 1192 circRNAs, 27 miRNAs, and 266 mRNAs were identified, and its accuracy was further confirmed by qRT-PCR. Bioinformatics analysis results suggested that neuroinflammation was the main pathological mechanism of PND. The construction of competitive endogenous RNA (ceRNA) networks and the identification of hub genes provided possible therapeutic targets for PND. Cinnarizine and Clemastine were predicted to have the potential therapeutic effects on PND. This is the first study to explore the differential expression profiles of genes and their regulation mechanisms in PND, our results provided new clues and targets for the treatment of this refractory disease.Semantic segmentation of mitochondria from electron microscopy (EM) images is an essential step to obtain reliable morphological statistics about mitochondria. However, automatically delineating plenty of mitochondria of varied shapes from complex backgrounds with sufficient accuracy is challenging. To address these challenges, we develop a hierarchical encoder-decoder network (HED-Net), which has a three-level nested U-shape architecture to capture rich contextual information. Given the irregular shape of mitochondria, we introduce a novel soft label-decomposition strategy to exploit shape knowledge in manual labels. Rather than simply using the ground truth label maps as the unique supervision in the model training, we introduce additional subcategory-aware supervision by softly decomposing each manual label map into two complementary label maps according to mitochondria's ovality. The three label maps are integrated with our HED-Net to supervise the model training. While the original label map guides the network to segment all the mitochondria of varied shapes, the auxiliary label maps guide the network to segment subcategories of mitochondria of circular shape and elliptic shape, respectively, which are much more manageable tasks.
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