Emulating key synaptic functions in electronic devices is quite significant in bioinspired applications. Artificial synaptic thin film transistors (TFT) offer a promising solution for efficient synapse simulation. Herein, artificial synapses based on indium–gallium–zinc oxide (IGZO) TFT are fabricated and the photoelectric plasticity is investigated. Versatile synaptic functions including paired‐pulse facilitation, paired‐pulse depression, and short‐term memory to long‐term memory transition are emulated. More importantly, these synaptic functions can be mediated by modulating the composition ratio of IGZO film. These achievements represent a major advance toward implementation of full synaptic functionality in neuromorphic hardware and the strategy that combines the photonics and the electrics has great prospects in optoelectronic applications.

Neuromorphic engineering is a promising technology for developing new computing systems owing to the low-power operation and the massive parallelism similarity to the human brain. Optimal function of neuronal networks requires interplay between rapid forms of Hebbian plasticity and homeostatic mechanisms that adjust the threshold for plasticity, termed metaplasticity. Metaplasticity has important implications in synapses and is barely addressed in neuromorphic devices. An understanding of metaplasticity might yield new insights into how the modification of synapses is regulated and how information is stored by synapses in the brain. Here, we propose a method to imitate the metaplasticity inhibition of long-term potentiation (MILTP) for the first time based on memristors. In addition, the metaplasticity facilitation of long-term potentiation (MFLTP) and the metaplasticity facilitation of long-term depression (MFLTD) are also achieved. Moreover, the mechanisms of metaplasticity in memristors are discussed. Additionally, the proposed method to mimic the metaplasticity is verified by three different memristor devices including oxide-based resistive memory (OxRAM), interface switching random access memory, and conductive bridging random access memory (CBRAM). This is a further step toward developing fully bio-realistic artificial synapses using memristors. The findings in this study will deepen our understanding of metaplasticity, as well as provide new insight into bio-realistic neuromorphic engineering.

Resistive random access memory (RRAM) has attracted significant interest for next-generation nonvolatile memory applications. However, it is somehow difficult to design a high speed RRAM device with enhanced data reliability. This paper deals with the improvement of high speed durable switching in nanocrystals based RRAM (NC-RRAM) devices. The high performance RRAM devices were prepared by incorporating the NCs into the HfOx oxide layer. As compared to the without (w/o) NC devices, the NC-RRAM devices are capable to execute uniform switching with higher set speed of 100 ns and reset speed of 150 ns, longer retention time and higher endurance of 108 cycles at 85 °C. The possible switching mechanism is due to the formation and rupture of the conductive filaments (CFs) inside the oxide film. The improvement of the NC-RRAM devices is due to the enhanced electric field intensity on the surface of the NCs, which can effectively facilitate the formation and rupture of the CFs.

In article number https://doi.org/10.1002/aelm.201800556, Hong Wang, Ling Li and co‐workers successfully emulate versatile functions of a biological synapse based on indium–gallium–zinc oxide (IGZO) thin film transistors through a new design by combining photonic and electric stimuli. Meanwhile, the synaptic functions can be mediated by modulating the composition ratio of IGZO film. The work contributes to the development of neuromorphic electronics and the combination of photonics and electric has great prospects in optoelectronic applications.

Simultaneous blockade of more than one pathway is considered to be a promising approach to overcome the low efficacy and acquired resistance of cancer therapies. Thus, a novel series of c-Met/HDAC bifunctional inhibitors was designed and synthesized by merging pharmacophores of c-Met and HDAC inhibitors. The most potent compound, , inhibited c-Met kinase and HDAC1, with IC values of 0.71 and 38 nM, respectively, and showed efficient antiproliferative activities against both EBC-1 and HCT-116 cells with greater potency than the reference drug Chidamide. Western blot analysis revealed that compound inhibited phosphorylation of c-Met and c-Met downstream signaling proteins and increased expression of Ac-H3 and p21 in EBC-1 cells in a dose-dependent manner. Our study presents novel compounds for the further exploration of dual c-Met/HDAC pathway inhibition achieved with a single molecule.

A novel neural-network-based method of time series forecasting is presented in this paper. The method combines the optimal partition algorithm (OPA) with the radial basis function (RBF) neural network. OPA for ordered samples is used to perform the clustering for the samples. The centers and widths of the RBF neural network are determined based on the clustering. The difference of the objective functions of the clustering is used to adjust the structure of the neural network dynamically. Thus, the number of the hidden nodes is selected adaptively. The method is applied to stock price prediction. The results of numerical simulations demonstrate the effectiveness of the method. Comparisons with the hard c-means (HCM) algorithm show that the proposed OPA method possesses obvious advantages in the precision of forecasting, generalization, and forecasting trends. Simulations also show that the OPA-orthogonal least squares (OPA-OLS) algorithm, which combines OPA with the OLS algorithm, results in better performance for forecasting trends.

In atomically-thin two-dimensional (2D) semiconductors, the nonuniformity in current flow due to its edge states may alter and even dictate the charge transport properties of the entire device. However, the influence of the edge states on electrical transport in 2D materials has not been sufficiently explored to date. Here, we systematically quantify the edge state contribution to electrical transport in monolayer MoS 2 /WSe 2 field-effect transistors, revealing that the charge transport at low temperature is dominated by the edge conduction with the nonlinear behavior. The metallic edge states are revealed by scanning probe microscopy, scanning Kelvin probe force microscopy and first-principle calculations. Further analyses demonstrate that the edge-state dominated nonlinear transport shows a universal power-law scaling relationship with both temperature and bias voltage, which can be well explained by the 1D Luttinger liquid theory. These findings demonstrate the Luttinger liquid behavior in 2D materials and offer important insights into designing 2D electronics.

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