Spiking mode-based neural networks

Spiking neural networks play an important role in brain-like neuromorphic computations and in studying working mechanisms of neural circuits. One drawback of training a large scale spiking neural network is that an expensive cost of updating all weights is required. Furthermore, after training, all information related to the computational task is hidden into the weight matrix, prohibiting us from a transparent understanding of circuit mechanisms. Therefore, in this work, we address these challenges by proposing a spiking mode-based training protocol. The first advantage is that the weight is interpreted by input and output modes and their associated scores characterizing importance of each decomposition term. The number of modes is thus adjustable, allowing more degrees of freedom for modeling the experimental data. This reduces a sizable training cost because of significantly reduced space complexity for learning. The second advantage is that one can project the high dimensional neural activity in the ambient space onto the mode space which is typically of a low dimension, e.g., a few modes are sufficient to capture the shape of the underlying neural manifolds. We analyze our framework in two computational tasks -- digit classification and selective sensory integration tasks. Our work thus derives a mode-based learning rule for spiking neural networks.Comment: 23 pages, 5 figure

Soil Organic Carbon Content and Microbial Functional Diversity Were Lower in Monospecific Chinese Hickory Stands than in Natural Chinese Hickory–Broad-Leaved Mixed Forests

To assess the effects of long-term intensive management on soil carbon cycle and microbial functional diversity, we sampled soil in Chinese hickory (Carya cathayensis Sarg.) stands managed intensively for 5, 10, 15, and 20 years, and in reference Chinese hickory–broad-leaved mixed forest (NMF) stands. We analyzed soil total organic carbon (TOC), microbial biomass carbon (MBC), and water-soluble organic carbon (WSOC) contents, applied 13C-nuclear magnetic resonance analysis for structural analysis, and determined microbial carbon source usage. TOC, MBC, and WSOC contents and the MBC to TOC ratios were lower in the intensively managed stands than in the NMF stands. The organic carbon pool in the stands managed intensively for twenty years was more stable, indicating that the easily degraded compounds had been decomposed. Diversity and evenness in carbon source usage by the microbial communities were lower in the stands managed intensively for 15 and 20 years. Based on carbon source usage, the longer the management time, the less similar the samples from the monospecific Chinese hickory stands were with the NMF samples, indicating that the microbial community compositions became more different with increased management time. The results call for changes in the management of the hickory stands to increase the soil carbon content and restore microbial diversity

Soil Organic Carbon Content and Microbial Functional Diversity Were Lower in Monospecific Chinese Hickory Stands than in Natural Chinese Hickory–Broad-Leaved Mixed Forests

To assess the effects of long-term intensive management on soil carbon cycle and microbial functional diversity, we sampled soil in Chinese hickory (Carya cathayensis Sarg.) stands managed intensively for 5, 10, 15, and 20 years, and in reference Chinese hickory–broad-leaved mixed forest (NMF) stands. We analyzed soil total organic carbon (TOC), microbial biomass carbon (MBC), and water-soluble organic carbon (WSOC) contents, applied 13C-nuclear magnetic resonance analysis for structural analysis, and determined microbial carbon source usage. TOC, MBC, and WSOC contents and the MBC to TOC ratios were lower in the intensively managed stands than in the NMF stands. The organic carbon pool in the stands managed intensively for twenty years was more stable, indicating that the easily degraded compounds had been decomposed. Diversity and evenness in carbon source usage by the microbial communities were lower in the stands managed intensively for 15 and 20 years. Based on carbon source usage, the longer the management time, the less similar the samples from the monospecific Chinese hickory stands were with the NMF samples, indicating that the microbial community compositions became more different with increased management time. The results call for changes in the management of the hickory stands to increase the soil carbon content and restore microbial diversity

Time series-based groundwater level forecasting using gated recurrent unit deep neural networks

In this research, the mean monthly groundwater level with a range of 3.78 m in Qoşaçay plain, Iran, is forecast. Regarding three different layers of gated recurrent unit (GRU) structures and a hybrid of variational mode decomposition with gated recurrent unit (VMD-GRU), deep learning-based neural network models are developed. As the base model for performance comparison, the general single-long short-term memory-layer network model is developed. In all models, the module of sequence-to-one is used because of the lack of meteorological variables recorded in the study area. For modeling, 216 monthly datasets of the mean monthly water table depth of 33 different monitoring piezometers in the period April 2002–March 2020 are utilized. To boost the performance of the models and reduce the overfitting problem, an algorithm tuning process using different types of hyperparameter accompanied by a trial-and-error procedure is applied. Based on performance evaluation metrics, the total learnable parameters value and especially the model grading process, the new double-GRU model coupled with multiplication layer (×) (GRU2× model) is chosen as the best model. Under the optimal hyperparameters, the GRU2× model results in an R 2 of 0.86, a root mean square error (RMSE) of 0.18 m, a corrected Akaike’s information criterion (AICc) of −280.75, a running time for model training of 87 s and a total grade (TG) of 6.21 in the validation stage; and the hybrid VMD-GRU model yields an RMSE of 0.16 m, an R 2 of 0.92, an AICc of −310.52, a running time of 185 s and a TG of 3.34. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

Spontaneous breaking and re-making of the RS-Au-SR staple in self-assembled ethylthiolate/Au(111) interface

The stability of the self-assembled RS–Au–SR (R = CH₂CH₃)/Au­(111) interface at room temperature has been investigated using scanning tunneling microscopy (STM) in conjunction with density functional theory (DFT) and MD calculations. The RS–Au–SR staple, also known as Au-adatom-dithiolate, assembles into staple rows along the [112̅] direction. STM imaging reveals that while the staple rows are able to maintain a static global structure, individual staples within the row are subjected to constant breaking and remaking of the Au–SR bond. The C₂S–Au–SC₂/Au­(111) interface is under a dynamic equilibrium and it is far from rigid. DFT/MD calculations show that a transient RS–Au–Au–SR complex can be formed when a free Au atom is added to the RS–Au–SR staple. The relatively high reactivity of the RS–Au–SR staple at room temperature could explain the reactivity of thiolate-protected Au nanoclusters, such as their ability to participate in ligand exchange and intercluster reactions

Peripheral anti-inflammatory effects explain the ginsenosides paradox between poor brain distribution and anti-depression efficacy

<p>Abstract</p> <p>Background</p> <p>The effectiveness of ginseng in preventing and treating various central nervous system (CNS) diseases has been widely confirmed. However, ginsenosides, the principal components of ginseng, are characterized by poor accessibility to the brain, and this pharmacokinetic-pharmacological paradox remains poorly explained. Anti-inflammatory approaches are becoming promising therapeutic strategies for depression and other CNS diseases; however, previous studies have focused largely on anti-inflammatory therapies directed at the central nervous system. It is thus of interest to determine whether ginsenosides, characterized by poor brain distribution, are also effective in treating lipopolysaccharide- (LPS) induced depression-like behavior and neuroinflammation.</p> <p>Methods</p> <p>In an LPS-induced depression-like behavior model, the antidepressant effects of ginseng total saponins (GTS) were assessed using a forced swimming test, a tail suspension test, and a sucrose preference test. The anti-inflammatory efficacies of GTS in brain, plasma, and LPS-challenged RAW264.7 cells were validated using ELISA and quantitative real-time PCR. Moreover, indoleamine 2,3-dioxygenase (IDO) activity in the periphery and brain were also determined by measuring levels of kynurenine/tryptophan.</p> <p>Results</p> <p>GTS significantly attenuated LPS-induced depression-like behavior. Moreover, LPS-induced increases in 5-HT and tryptophane turnover in the brain were significantly reduced by GTS. IDO activities in brain and periphery were also suppressed after pretreatment with GTS. Furthermore, GTS-associated recovery from LPS-induced depression-like behavior was paralleled with reduced mRNA levels for IL-1β, IL-6, TNF-α, and IDO in hippocampus. Poor brain distribution of ginsenosides was confirmed in LPS-challenged mice. GTS treatment significantly decreased production of various proinflammatory cytokines in both LPS-challenged mice and RAW264.7 cells.</p> <p>Conclusion</p> <p>This study suggests that the anti-depression efficacy of GTS may be largely attributable to its peripheral anti-inflammatory activity. Our study also strengthens an important notion that peripheral anti-inflammation strategies may be useful in the therapy of inflammation-related depression and possibly other CNS diseases.</p

Difference between Pb and Cd Accumulation in 19 Elite Maize Inbred Lines and Application Prospects

In the last two decades, the accumulation of heavy metal in crop grains has become the study hotspot. In this study, 19 representative elite maize inbred lines and 3 hybrid varieties were investigated at the seedling stage, which can accumulate Pb and Cd in the stems and leaves, respectively. The results demonstrated that significant differences are among inbred lines for accumulation of heavy metals, implying that the Cd accumulation is significant correlation between the male parents and their hybrids and some inbred lines have been selected for cross-breeding with low Pb or Cd accumulation, such as S37, 9782, and ES40; Moreover, some inbred lines could be suitable for phytoremediation species for soil bioremediation with high levels of Pb and Cd accumulation, including 178, R08, 48-2, and Mo17ht