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The close replication of synaptic functions is an important objective for achieving a highly realistic memristor-based cognitive computation. The emulation of neurobiological learning rules may allow the development of neuromorphic systems that continuously learn without supervision. In this work, the Bienenstock-Cooper-Munro learning rule, as a typical case of spike-rate-dependent plasticity, is mimicked using a generalized triplet-spike-timing-dependent plasticity scheme in a WO 3−x memristive synapse. It demonstrates both presynaptic and postsynaptic activities and remedies the absence of the enhanced depression effect in the depression region, allowing a better description of the biological counterpart. The threshold sliding effect of Bienenstock-Cooper-Munro rule is realized using a history-dependent property of the second-order memristor. Rate-based orientation selectivity is demonstrated in a simulated feedforward memristive network with this generalized Bienenstock-Cooper-Munro framework. These findings provide a feasible approach for mimicking Bienenstock-Cooper-Munro learning rules in memristors, and support the applications of spatiotemporal coding and learning using memristive networks. Designing reliable and energy efficient neuromorphic computing systems for spatiotemporal coding remains a challenge. Here, the authors demonstrate a type of spike-rate-dependent plasticity based on a triplet learning scheme in a WO 3−x -based second-order memristor network for spatiotemporal patterns.

The growing prevalence of counterfeit products worldwide poses serious threats to economic security and human health. Developing advanced anti-counterfeiting materials with physical unclonable functions offers an attractive defense strategy. Here, we report multimodal, dynamic and unclonable anti-counterfeiting labels based on diamond microparticles containing silicon-vacancy centers. These chaotic microparticles are heterogeneously grown on silicon substrate by chemical vapor deposition, facilitating low-cost scalable fabrication. The intrinsically unclonable functions are introduced by the randomized features of each particle. The highly stable signals of photoluminescence from silicon-vacancy centers and light scattering from diamond microparticles can enable high-capacity optical encoding. Moreover, time-dependent encoding is achieved by modulating photoluminescence signals of silicon-vacancy centers via air oxidation. Exploiting the robustness of diamond, the developed labels exhibit ultrahigh stability in extreme application scenarios, including harsh chemical environments, high temperature, mechanical abrasion, and ultraviolet irradiation. Hence, our proposed system can be practically applied immediately as anti-counterfeiting labels in diverse fields. Practical anticounterfeiting labels should possess both high-capacity and robustness, and should allow easy fabrication and readout. Here, the authors show how to heterogeneously grow robust and stable chaotic pattern of diamond microparticles - containing SiV defects - on silicon substrates.

Exploration of optoelectronic memristors with the capability to combine sensing and processing functions is required to promote development of efficient neuromorphic vision. In this work, the authors develop a plasmonic optoelectronic memristor that relies on the effects of localized surface plasmon resonance (LSPR) and optical excitation in an Ag–TiO2 nanocomposite film. Fully light‐induced synaptic plasticity (e.g., potentiation and depression) under visible and ultraviolet light stimulations is demonstrated, which enables the functional combination of visual sensing and low‐level image pre‐processing (including contrast enhancement and noise reduction) in a single device. Furthermore, the light‐gated and electrically‐driven synaptic plasticity can be performed in the same device, in which the spike‐timing‐dependent plasticity (STDP) learning functions can be reversibly modulated by visible and ultraviolet light illuminations. Thereby, the high‐level image processing function, i.e., image recognition, can also be performed in this memristor, whose recognition rate and accuracy are obviously enhanced as a result of image pre‐processing and light‐gated STDP enhancement. Experimental analysis shows that the memristive switching mechanism under optical stimulation can be attributed to the oxidation/reduction of Ag nanoparticles due to the effects of LSPR and optical excitation. The authors' work proposes a new type of plasmonic optoelectronic memristor with fully light‐modulated capability that may promote the future development of efficient neuromorphic vision. A novel plasmonic optoelectronic memristor is demonstrated for the first time relying on localized surface plasmon resonance (LSPR) effect. Both fully light‐modulated and light‐gated electrically‐driven synaptic modulation can be implemented in such a single device. Furthermore, combination of visual sensing, low‐level (contrast enhancement and noise reduction), and high‐level image processing (image recognition) promotes the development of efficient neuromorphic vision.

Visual adaptation that can autonomously adjust the response to light stimuli is a basic function of artificial visual systems for intelligent bionic robots. To improve efficiency and reduce complexity, artificial visual systems with integrated visual adaptation functions based on a single device should be developed to replace traditional approaches that require complex circuitry and algorithms. Here, we have developed a single two-terminal opto-sensor based on multilayer γ-InSe flakes, which successfully emulated the visual adaptation behaviors with a new working mechanism combining the photo-pyroelectric and photo-thermoelectric effect. The device can operate in self-powered mode and exhibit good human-eye-like adaptation behaviors, which include broadband light-sensing image adaptation (from ultraviolet to near-infrared), near-complete photosensitivity recovery (99.6%), and synergetic visual adaptation, encouraging the advancement of intelligent opto-sensors and machine vision systems. A single two-terminal opto-sensor based on multilayer γ-InSe flakes was developed and successfully emulated human-eye-like adaptation behaviors, which could motivate the further development of advanced opto-sensors and artificial visual systems.

Binocular stereo vision relies on imaging disparity between two hemispherical retinas, which is essential to acquire image information in three dimensional environment. Therefore, retinomorphic electronics with structural and functional similarities to biological eyes are always highly desired to develop stereo vision perception system. In this work, a hemispherical optoelectronic memristor array based on Ag‐TiO2 nanoclusters/sodium alginate film is developed to realize binocular stereo vision. All‐optical modulation induced by plasmonic thermal effect and optical excitation in Ag‐TiO2 nanoclusters is exploited to realize in‐pixel image sensing and storage. Wide field of view (FOV) and spatial angle detection are experimentally demonstrated owing to the device arrangement and incident‐angle‐dependent characteristics in hemispherical geometry. Furthermore, depth perception and motion detection based on binocular disparity have been realized by constructing two retinomorphic memristive arrays. The results demonstrated in this work provide a promising strategy to develop all‐optically controlled memristor and promote the future development of binocular vision system with in‐sensor architecture. A hemispherical optoelectronic memristive array is demonstrated, which relies on localized surface plasmon resonance (LSPR) effect in Ag‐TiO2 nanocluster /sodium alginate nanocomposite. Both fully light‐modulated synaptic plasticity and wide field of view can be implemented in the hemispherical array. Furthermore, depth perception and motion detection based on binocular disparity have been demonstrated by constructing two retinomorphic arrays.

Electronic devices with biomaterials have paved a way toward “green electronics” to create a sustainable future. Memristors are drawing growing attention with integrated sensing, memory, and computing for future artificial intelligence applications. Biomaterial is an emerging class of memristive materials (the device is called as biomemristor) for transient and/or biodegradable purpose. Importantly, several unique features such as faithful synaptic behaviors, bimodal switching, and biovoltage operations are observed in biomemristors. Moreover, the biomemristors are suitable for human‐related applications due to the inherent biocompatibility of biomaterials and flexibility of the device with ultrathin thickness. These features make the biomemristors promising for biorealistic neuromorphic applications. Herein, the state of the art of biomemristors are comprehensively summarized and systematically discussed with particular attention on their unique biorealistic features. Challenges and prospects toward the further development of biomemristors are also provided and discussed. As emerging memristive materials, biomaterials hold great promise for “green” electronics to create a sustainable future with unique biodegradable and biocompatible properties. This review systematically outlines the state‐of‐the‐art development of biomemristor devices with a focus on their unique features for biorealistic neuromorphic applications. Future key challenges and opportunities are suggested.

Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an R2cv of 0.63 and RMSEcv of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of R2cv = 0.66 and RMSEcv = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an R2cv = 0.61 and RMSEcv = 6.415%. The estimation was improved by an SVR model with the same input predictors (R2cv = 0.67, RMSEcv = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with R2cv = 0.69 and RMSEcv = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.

Resistance random access memory (RRAM) based true random number generator (TRNG) has great potential to be applied to hardware security owing to its intrinsic switching variability. Especially the high resistance state (HRS) variation is usually taken as the entropy source of RRAM-based TRNG. However, the small HRS variation of RRAM may be introduced owing to fabrication process fluctuations, which may lead to error bits and be vulnerable to noise interference. In this work, we propose an RRAM-based TRNG with a 2T1R architecture scheme, which can effectively distinguish the resistance values of HRS with an accuracy of 1.5 kΩ. As a result, the error bits can be corrected to a certain extent while the noise is suppressed. Finally, a 2T1R RRAM-based TRNG macro is simulated and verified using the 28 nm CMOS process, which suggests its potential for hardware security applications.

The inhibitory effect of compound soaking solution on the quality deterioration of fish fillets during storage and its repair effect on a cell oxidative damage model were investigated. Water holding capacity, cooking loss, thawing loss, thiobarbituric acid and sensory evaluation were used to verify that the composite soaking solution could improve the water loss and quality deterioration of fillets during frozen storage. At 180 d, water holding capacity was increased by 4.59% in the compound soaking solution group compared with the control. Cooking loss decreased by 6.47%, and thawing loss decreased by 13.06% (p < 0.05). The TBA value was reduced by 50%, and the degree of lipid oxidation was lower (p < 0.05). The results of the microstructure analysis showed that the tissue structure of fillets treated by the compound soaking solution was more orderly. The oxidative damage model of cells was achieved by soaking in treated fish fillet digestive juice, which inhibited the increase in reactive oxygen species content, maintained the integrity of the cell structure, and increased cell viability by 32.24% (p < 0.05). Compound soaking solution treatment could inhibit the quality deterioration of fish fillets during storage, and the digestive solution of fish fillets could improve the oxidative stress injury of Caco-2 cells induced by H2O2.

Spiking neural network (SNN), widely known as the third-generation neural network, has been frequently investigated due to its excellent spatiotemporal information processing capability, high biological plausibility, and low energy consumption characteristics. Analogous to the working mechanism of human brain, the SNN system transmits information through the spiking action of neurons. Therefore, artificial neurons are critical building blocks for constructing SNN in hardware. Memristors are drawing growing attention due to low consumption, high speed, and nonlinearity characteristics, which are recently introduced to mimic the functions of biological neurons. Researchers have proposed multifarious memristive materials including organic materials, inorganic materials, or even two-dimensional materials. Taking advantage of the unique electrical behavior of these materials, several neuron models are successfully implemented, such as Hodgkin–Huxley model, leaky integrate-and-fire model and integrate-and-fire model. In this review, the recent reports of artificial neurons based on memristive devices are discussed. In addition, we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices. Finally, the future challenges and outlooks of memristor-based artificial neurons are discussed, and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected. Multifarious neuronal models and their functions were discussed. Remarkable progress in memristive neurons for multisensory neuron and brain-machine interface was presented. The remaining challenges and suggested future directions for the further development of memristor-based neuromorphic computing and brain-like intelligent system were illustrated.