Event-driven continuous STDP learning with deep structure for visual pattern recognition

Human beings can achieve reliable and fast visual pattern recognition with limited time and learning samples. Underlying this capability, ventral stream plays an important role in object representation and form recognition. Modeling the ventral steam may shed light on further understanding the visual brain in humans and building artificial vision systems for pattern recognition. The current methods to model the mechanism of ventral stream are far from exhibiting fast, continuous and event-driven learning like the human brain. To create a visual system similar to ventral stream in human with fast learning capability, in this study, we propose a new spiking neural system with an event-driven continuous spike timing dependent plasticity (STDP) learning method using specific spiking timing sequences. Two novel continuous input mechanisms have been used to obtain the continuous input spiking pattern sequence. With the event-driven STDP learning rule, the proposed learning procedure will be activated if the neuron receive one pre- or post-synaptic spike event. The experimental results on MNIST database show that the proposed method outperforms all other methods in fast learning scenarios and most of the current models in exhaustive learning experiments

Fast unsupervised learning for visual pattern recognition using spike timing dependent plasticity

Real-time learning needs algorithms operating in a fast speed comparable to human or animal, however this is a huge challenge in processing visual inputs. Research shows a biological brain can process complicated real-life recognition scenarios at milliseconds scale. Inspired by biological system, in this paper, we proposed a novel real-time learning method by combing the spike timing-based feed-forward spiking neural network (SNN) and the fast unsupervised spike timing dependent plasticity learning method with dynamic post-synaptic thresholds. Fast cross-validated experiments using MNIST database showed the high e�ciency of the proposed method at an acceptable accuracy

Parthenolide attenuates LPS-induced activation of NF-κB in a time-dependent manner in rat myocardium.

Parthenolide (PTN), a selective nuclear factor kappa B (NF-κB) inhibitor, has been used extensively to inhibit NF-κB activation. The duration of the inhibitory effect of PTN on NF-κB in vivo remains unclear. This study was to determine whether a lipopolysaccharide (LPS) challenge 6, 12 and 24 h after the administration of PTN could activate NF-κB. Rats were devided into five groups. The rats in the PTN, PTN+LPS and DMSO groups were injected intraperitoneally with PTN or DMSO. After 6, 12 or 24 h, LPS was administered in LPS and PTN+LPS groups. The expressions of NF-κB p50, IκBα and p-IκBα were inhibited in both PTN and PTN+LPS group at end of 6 and 12 h and no effects at 24 h. In summary, myocardial NF-κB expression occurs 1 h after the administration of LPS. PTN blocks this effect given at 6 h and no inhibitory effect 24 h after administration in vivo

Competitive Strategy, Market Entry Mode and International Performance: The Case of Construction Firms in China

As an important participant in the international construction market, Chinese construction firms (CCFs) are confronted with the tasks of keeping themselves competitive. To help CCFs maintain and improve their competitiveness, this research builds a conceptual model to investigate the relationship between competitive strategy, market entry mode and performance within CCFs. Based on data collected from CCFs, this research has confirmed the importance of cost leadership strategy, differentiation strategy and business scope diversification, to achieve their superior performance. Moreover, there are positive relationships among entry mode strategies with CCFs’ international performance

Deficient data classification with fuzzy learning

This thesis first proposes a novel algorithm for handling both missing values and imbalanced data classification problems. Then, algorithms for addressing the class imbalance problem in Twitter spam detection (Network Security Problem) have been proposed. Finally, the security profile of SVM against deliberate attacks has been simulated and analysed.<br /

Deep Spiking Neural Network for Video-based Disguise Face Recognition Based on Dynamic Facial Movements

With the increasing popularity of social media andsmart devices, the face as one of the key biometrics becomesvital for person identification. Amongst those face recognitionalgorithms, video-based face recognition methods could make useof both temporal and spatial information just as humans do toachieve better classification performance. However, they cannotidentify individuals when certain key facial areas like eyes or noseare disguised by heavy makeup or rubber/digital masks. To thisend, we propose a novel deep spiking neural network architecturein this study. It takes dynamic facial movements, the facial musclechanges induced by speaking or other activities, as the sole input.An event-driven continuous spike-timing dependent plasticitylearning rule with adaptive thresholding is applied to train thesynaptic weights. The experiments on our proposed video-baseddisguise face database (MakeFace DB) demonstrate that theproposed learning method performs very well - it achieves from95% to 100% correct classification rates under various realisticexperimental scenario

Pulse-duration dependence of high-order harmonic generation with coherent superposition state

We make a systematic study of high-order harmonic generation (HHG) in a He$^+$-like model ion when the initial states are prepared as a coherent superposition of the ground state and an excited state. It is found that, according to the degree of the ionization of the excited state, the laser intensity can be divided into three regimes in which HHG spectra exhibit different characteristics. The pulse-duration dependence of the HHG spectra in these regimes is studied. We also demonstrate evident advantages of using coherent superposition state to obtain high conversion efficiency. The conversion efficiency can be increased further if ultrashort laser pulses are employed

Navigational Drift Analysis for Visual Odometry

Visual odometry estimates a robot's ego-motion with cameras installed on itself. With the advantages brought by camera being a sensor, visual odometry has been widely adopted in robotics and navigation fields. Drift (or error accumulation) from relative motion concatenation is an intrinsic problem of visual odometry in long-range navigation, as visual odometry is a sensor based on relative measurements. General error analysis using ``mean'' and ``covariance'' of positional error in each axis is not fully capable to describe the behavior of drift. Moreover, no theoretic drift analysis is available for performance evaluation and algorithms comparison. Drift distribution is established in the paper, as a function of the covariance matrix from positional error propagation model. To validate the drift model, experiment with a specific setting is conducted