The synaptic weight modification depends not only on interval of the pre-/ postspike pairs according to spike-timing dependent plasticity (classical pair-STDP), but also on the timing of the preceding spike (triplet-STDP). Triplet-STDP reflects the unavoidable interaction of spike pairs in natural spike trains through the short-term suppression effect of preceding spikes. Second-order memristors with one state variable possessing short-term dynamics work in a way similar to the biological system. In this work, the suppression triplet-STDP learning rule is faithfully demonstrated by experiments and simulations using second-order memristors. Furthermore, a leaky-integrate-and-fire (LIF) neuron is simulated using a circuit constructed with second-order memristors. Taking the advantage of the LIF neuron, various neuromimetic dynamic processes, including local graded potential leaking out, postsynaptic impulse generation and backpropagation, and synaptic weight modification according to the suppression triplet-STDP rule, are realized. The realized weight-dependent pairand triplet-STDP rules are clearly in line with findings in biology. The physically realized triplet-STDP rule is powerful in developing direction and speed selectivity for complex pattern recognition and tracking tasks. These scalable artificial synapses and neurons realized in second-order memristors can intrinsically capture the neuromimetic dynamic processes; they are the promising building blocks for constructing brain-inspired computation systems.

Nowadays, there is a lot of study on memristorbased systems with multistability. However, there is no study on memristor with multistability. This brief constructs a mathematical memristor model with multistability. The origin of the multi-stable dynamics is revealed using standard nonlinear theory as well as circuit and system theory. Moreover, the multi-stable memristor is applied to simulate a synaptic connection in a Hopfield neural network. The memristive neural network successfully generates infinitely many coexisting chaotic attractors unobserved in previous Hopfield-type neural networks. The results are also confirmed in analog circuits based on commercially available electronic elements.

Over the past decade, the rapid development of e-commerce and express industries in China has resulted in huge environmental costs. Compared with manufacturing industries, the values of green innovation are less recognized in logistics industries. To promote the green practices in logistic enterprises, it is imperative to have a thorough understanding of the determinants of green innovation adoption. To this end, this paper performs an empirical investigation into the intentions to adopt green innovation from 196 Chinese express companies. The determinant variables were constructed from the perspective of technology characteristics (perceived green usefulness and perceived integration ease of use), stakeholder pressure (government, customer, and platform pressures), and social influence. Then, a 20-item scale was designed based on the literature review and expert opinions. The results revealed the significant positive effects of technology characteristics and social influence on the intentions to adopt green innovation. Meanwhile, only the platform pressure was significant with the adopting intentions among the variables from stakeholder pressure. Moreover, variables from technology characteristics were found to have meditation effects between social influence and adopting intentions. Based on the findings, theoretical and practical implications are proposed to promote the green and sustainable development of express companies in China.

Polynomial functions have been the main barrier restricting the circuit realization and engineering application of multi-wing chaotic systems (MWCSs). To eliminate this bottleneck, we construct a simple MWCS without polynomial functions by introducing a sinusoidal function in a Sprott C system. Theoretical analysis and numerical simulations show that the MWCS can not only generate multi-butterfly attractors with an arbitrary number of butterflies, but also adjust the number of the butterflies by multiple ways including self-oscillating time, control parameters, and initial states. To further explore the advantage of the proposed MWCS, we realize its analog circuit using commercially available electronic elements. The results demonstrate that in comparison to traditional MWCSs, our circuit implementation greatly reduces the consumption of electronic components. This makes the MWCS more suitable for many chaos-based engineering applications. Furthermore, we propose an application of the MWCS to chaotic image encryption. Histogram, correlation, information entropy, and key sensitivity show that the simple image encryption scheme has high security and reliable encryption performance. Finally, we develop a field-programmable gate array (FPGA) test platform to implement the MWCS-based image cryptosystem. Both theoretical analysis and experimental results verify the feasibility and availability of the proposed MWCS.

Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.