PC-SNN: Supervised Learning with Local Hebbian Synaptic Plasticity based on Predictive Coding in Spiking Neural Networks

Deemed as the third generation of neural networks, the event-driven Spiking Neural Networks(SNNs) combined with bio-plausible local learning rules make it promising to build low-power, neuromorphic hardware for SNNs. However, because of the non-linearity and discrete property of spiking neural networks, the training of SNN remains difficult and is still under discussion. Originating from gradient descent, backprop has achieved stunning success in multi-layer SNNs. Nevertheless, it is assumed to lack biological plausibility, while consuming relatively high computational resources. In this paper, we propose a novel learning algorithm inspired by predictive coding theory and show that it can perform supervised learning fully autonomously and successfully as the backprop, utilizing only local Hebbian plasticity. Furthermore, this method achieves a favorable performance compared to the state-of-the-art multi-layer SNNs: test accuracy of 99.25% for the Caltech Face/Motorbike dataset, 84.25% for the ETH-80 dataset, 98.1% for the MNIST dataset and 98.5% for the neuromorphic dataset: N-MNIST. Furthermore, our work provides a new perspective on how supervised learning algorithms are directly implemented in spiking neural circuitry, which may give some new insights into neuromorphological calculation in neuroscience.Comment: 15 pages, 11fig

A preliminary study of in vitro and in vivo synergistic effects of ciprofloxacin and D-tyrosine against Pseudomonas aeruginosa isolates

Purpose: To investigate the synergistic antimicrobial effects of ciprofloxacin and D-tyrosine against drug-resistant bacteria.Method: The antimicrobial effects of ciprofloxacin and D-tyrosine on clinical isolates of multidrugresistant (MDR) Pseudomonas aeruginosa (P. aeruginosa) no. 3556 were determined in vitro based on time-kill curve, and in vivo in P. aeruginosa-zebrafish infection model. Furthermore, 30 clinical isolates of multidrug-resistant P. aeruginosa were used in vitro to ascertain the synergistic effect of the two agents.Results: Combined use of ciprofloxacin and D-tyrosine produced synergistic effects against the clinical isolate of P. aeruginosa no. 3556 in vitro and in vivo. Synergism occurred in 96.67 % (95 % CI, range 83.33 - 99.41 %) of the clinical isolates, and ciprofloxacin dose was reduced in 90 % (95 % CI, range 74.38 - 96.54 %) of the clinical isolates in vitro.Conclusion: These preliminary results suggest that the combination of ciprofloxacin and D-tyrosine is a promising therapeutic strategy against MDR P. aeruginosa infections. Keywords: Ciprofloxacin, D-tyrosine, Synergistic, P. aeruginosa, Zebrafish infection model, Time-killing curv

A reduced-order model for dynamic simulation of district heating networks

This study concerns the development of a data-based compact model for the prediction of the fluid temperature evolution in district heating (DH) pipeline networks. This so-called "reduced-order model" (ROM) is obtained from reduction of the conservation law for energy for each pipe segment to a semi-analytical input-output relation between the pipe outlet temperature and the pipe inlet and ground temperatures that can be identified from training data. The ROM basically is valid for generic pipe configurations involving 3D unsteady heat transfer and 3D steady flow as long as heat-transfer mechanisms are linearly dependent on the temperature field. Moreover, the training data can be generated by physics-based computational "full-order" models (FOMs) yet also by (calibration) experiments or field measurements. Performance tests using computational training data for a single 1D pipe configuration demonstrate that the ROM (i) can be successfully identified and (ii) can accurately describe the response of the outlet temperature to arbitrary input profiles for inlet and ground temperatures. Application of the ROM to two case studies, i.e. fast simulation of a small DH network and design of a controller for user-defined temperature regulation of a DH system, demonstrate its predictive ability and efficiency also for realistic systems. Dedicated cost analyses further reveal that the ROM may significantly reduce the computational costs compared to FOMs by (up to) orders of magnitude for higher-dimensional pipe configurations. These findings advance the proposed ROM as a robust and efficient simulation tool for practical DH systems with a far greater predictive ability than existing compact models.Comment: 30 pages, 19 figure

Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition

Cross-Database Micro-Expression Recognition (CDMER) aims to develop the Micro-Expression Recognition (MER) methods with strong domain adaptability, i.e., the ability to recognize the Micro-Expressions (MEs) of different subjects captured by different imaging devices in different scenes. The development of CDMER is faced with two key problems: 1) the severe feature distribution gap between the source and target databases; 2) the feature representation bottleneck of ME such local and subtle facial expressions. To solve these problems, this paper proposes a novel Transfer Group Sparse Regression method, namely TGSR, which aims to 1) optimize the measurement and better alleviate the difference between the source and target databases, and 2) highlight the valid facial regions to enhance extracted features, by the operation of selecting the group features from the raw face feature, where each region is associated with a group of raw face feature, i.e., the salient facial region selection. Compared with previous transfer group sparse methods, our proposed TGSR has the ability to select the salient facial regions, which is effective in alleviating the aforementioned problems for better performance and reducing the computational cost at the same time. We use two public ME databases, i.e., CASME II and SMIC, to evaluate our proposed TGSR method. Experimental results show that our proposed TGSR learns the discriminative and explicable regions, and outperforms most state-of-the-art subspace-learning-based domain-adaptive methods for CDMER

A data-based reduced-order model for dynamic simulation and control of district-heating networks

This study concerns the development of a data-based compact model for the prediction of the fluid temperature evolution in district heating (DH) pipeline networks. This so-called “reduced-order model” (ROM) is obtained from reduction of the conservation law for energy for each pipe segment to a semi-analytical input–output relation between the pipe outlet temperature and the pipe inlet and ground temperatures that can be identified from training data. The ROM basically is valid for generic pipe configurations involving 3D unsteady heat transfer and 3D steady flow as long as heat-transfer mechanisms are linearly dependent on the temperature field. Moreover, the training data can be generated by physics-based computational “full-order” models (FOMs) yet also by (calibration) experiments or field measurements. Performance tests using computational training data for a single-pipe configuration demonstrate that the ROM (i) can be successfully identified and (ii) can accurately describe the response of the outlet temperature to arbitrary input profiles for inlet and ground temperatures. Application of the ROM to two case studies, i.e. fast simulation of a small DH network and design of a controller for user-defined temperature regulation of a DH system, demonstrate its predictive ability and efficiency also for realistic systems. Dedicated cost analyses further reveal that the ROM may significantly reduce the computational costs compared to FOMs by (up to) orders of magnitude for higher-dimensional pipe configurations. These findings advance the proposed ROM as a robust and efficient simulation tool for practical DH systems with a far greater predictive ability than existing compact models

CRIT:Identifying RNA-binding protein regulator in circRNA life cycle via non-negative matrix factorization

Circular RNAs (circRNAs) are endogenous non-coding RNAs that regulate gene expression and participate in carcinogenesis. However, the RNA-binding proteins (RBPs) involved in circRNAs biogenesis and modulation remain largely unclear. We developed the circRNA regulator identification tool (CRIT), a non-negative matrix-factorization-based pipeline to identify regulating RBPs in cancers. CRIT uncovered 73 novel regulators across thousands of samples by effectively leveraging genomics data and functional annotations. We demonstrated that known RBPs involved in circRNA control are significantly enriched in these predictions. Analysis of circRNA-RBP interactions using two large cross-linking immunoprecipitation (CLIP) databases, we validated the consistency between CRIT prediction and the CLIP experiments. Furthermore, newly discovered RBPs are functionally connected with authentic circRNA regulators by various biological associations, such as physical interaction, similar binding motifs, common transcription factor modulation, and co-expression. When analyzing RNA sequencing (RNA-seq) datasets after short hairpin RNA (shRNA)/small interfering RNA (siRNA) knockdown, we found several novel RBPs that can affect global circRNA expression, which strengthens their role in the circRNA life cycle. The above evidence provided independent confirmation that CRIT is a useful tool to capture RBPs in circRNA processing. Finally, we show that authentic regulators are more likely the core splicing proteins and peripheral factors and usually harbor more alterations in the vast majority of cancers

Successional change in species composition alters climate sensitivity of grassland productivity.

Succession theory predicts altered sensitivity of ecosystem functions to disturbance (i.e., climate change) due to the temporal shift in plant community composition. However, empirical evidence in global change experiments is lacking to support this prediction. Here, we present findings from an 8-year long-term global change experiment with warming and altered precipitation manipulation (double and halved amount). First, we observed a temporal shift in species composition over 8 years, resulting in a transition from an annual C3 -dominant plant community to a perennial C4 -dominant plant community. This successional transition was independent of any experimental treatments. During the successional transition, the response of aboveground net primary productivity (ANPP) to precipitation addition magnified from neutral to +45.3%, while the response to halved precipitation attenuated substantially from -17.6% to neutral. However, warming did not affect ANPP in either state. The findings further reveal that the time-dependent climate sensitivity may be regulated by successional change in species composition, highlighting the importance of vegetation dynamics in regulating the response of ecosystem productivity to precipitation change

Binocular balance across spatial frequency in anisomyopia

PurposeAnisomyopia is prevalent in myopia and studies have reported it exhibits impaired binocular function. We investigated the binocular balance across spatial frequency in adults with anisomyopia and compared it to in individuals with less differences in refractive error, and examined whether ocular characteristics can predict binocular balance in anisomyopia.MethodsFifteen anisomyopes, 15 isomyopes and 12 emmetropes were recruited. Binocular balance was quantitatively measured at 0.5, 1, 2 and 4 c/d. The first two groups of the observers were tested with and without optical correction with contact lenses. Emmetropes were tested without optical correction.ResultsBinocular balance across spatial frequency in optically corrected anisomyopes and isomyopes, as well as emmetropes were found to be similar. Their binocular balance nevertheless still got worse as a function of spatial frequency. However, before optical correction, anisomyopes but not isomyopes showed significant imbalance at higher spatial frequencies. There was a significant correlation between the dependence on spatial frequency of binocular imbalance in uncorrected anisomyopia and interocular difference in visual acuity, and between the dependence and interocular difference in spherical equivalent refraction.ConclusionAnisomyopes had intact binocular balance following correction across spatial frequency compared to those in isomyopes and emmetropes. Their balance was weakly correlated with their refractive status after optical correction. However, their binocular balance before correction and binocular improvement following optical correction were strongly correlated with differences in ocular characteristics between eyes