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The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adma.202105485.Human behavior (e.g., the response to any incoming information) has very complex forms and is based on the response to consecutive external stimuli entering varied sensory receptors. Sensory adaptation is an elementary form of the sensory nervous system known to filter out irrelevant information for efficient information transfer from consecutive stimuli. As bioinspired neuromorphic electronic system is developed, the functionality of organs shall be emulated at a higher level than the cell. Because it is important for electronic devices to possess sensory adaptation in spiking neural networks, the authors demonstrate a dynamic, real-time, photoadaptation process to optical irradiation when repeated light stimuli are presented to the artificial photoreceptor. The filtered electrical signal generated by the light and the adapting signal produces a specific range of postsynaptic states through the neurotransistor, demonstrating changes in the response according to the environment, as normally perceived by the human brain. This successfully demonstrates plausible biological sensory adaptation. Further, the ability of this circuit design to accommodate changes in the intensity of bright or dark light by adjusting the sensitivity of the artificial photoreceptor is demonstrated. Thus, the proposed artificial photoreceptor circuits have the potential to advance neuromorphic device technology by providing sensory adaptation capabilities.

The development of brain-inspired neuromorphic computing, including artificial intelligence (AI) and machine learning, is of considerable importance because of the rapid growth in hardware and software capacities, which allows for the efficient handling of big data. Devices for neuromorphic computing must satisfy basic requirements such as multilevel states, high operating speeds, low energy consumption, and sufficient endurance, retention and linearity. In this study, inorganic perovskite-type amorphous strontium vanadate (a-SrVO x : a-SVO) synthesized at room temperature is utilized to produce a high-performance memristor that demonstrates nonvolatile multilevel resistive switching and synaptic characteristics. Analysis of the electrical characteristics indicates that the a-SVO memristor illustrates typical bipolar resistive switching behavior. Multilevel resistance states are also observed in the off-to-on and on-to-off transition processes. The retention resistance of the a-SVO memristor is shown to not significantly change for a period of 2 × 10 4 s. The conduction mechanism operating within the Ag/a-SVO/Pt memristor is ascribed to the formation of Ag-based filaments. Nonlinear neural network simulations are also conducted to evaluate the synaptic behavior. These results demonstrate that a-SVo-based memristors hold great promise for use in high-performance neuromorphic computing devices. open Scientific RepoRtS | (2020) 10:5761 | https://doi.

Organic–inorganic halide perovskites have been widely investigated for the fabrication of photodetectors because of their excellent optoelectronic properties. However, the toxicity of Pb‐based perovskites and the lack of manufacturing technology in air limits the commercialization of perovskite‐based photodetectors. Tin halide perovskite (THP) is a promising candidate to replace Pb‐based perovskites due to its low toxicity and potential commercialization. However, THP suffers poor stability in air because Sn2+ is easily oxidized to Sn4+. This “self‐doping” effect not only degrades device performance rapidly, but also makes it difficult to fabricate THP‐based devices in air. Here, Pb‐free Sn‐based 2D perovskite PEA2SnI4 films with tin fluoride (SnF2) additive are prepared in air using a nitrogen quenching hot casting method. The photodetectors with an optimal SnF2 concentration of 20% exhibit a fast response speed of 0.56 ms and an excellent specific detectivity of 6.32 × 1013 Jones, the best combination of THP‐based photodetectors reported hitherto. The SnF2 additive is found to suppress the formation of Sn vacancies and improve the crystallinity of PEA2SnI4 films. The work provides an air‐processing technology of Pb‐free perovskite, which is easy to commercialize, and SnF2 additive engineering offering an effective way to demonstrate high performance photodetectors and image recognition applications.

Abstract:In this paper, we found the usefulness of the deep belief network (DBN) in the fields of brain-computer interface (BCI), especially in relation to imagined speech. In recent years, the growth of interest in the BCI field has led to the development of a number of useful applications, such as robot control, game interfaces, exoskeleton limbs, and so on. However, while imagined speech, which could be used for communication or military purpose devices, is one of the most exciting BCI applications, there are some problems in implementing the system. In the previous paper, we already handled some of the issues of imagined speech when using the International Phonetic Alphabet (IPA), although it required complementation for multi class classification problems. In view of this point, this paper could provide a suitable solution for vowel classification for imagined speech. We used the DBN algorithm, which is known as a deep learning algorithm for multi-class vowel classification, and selected four vowel pronunciations:, /a/, /i/, /o/, /u/ from IPA. For the experiment, we obtained the required 32 channel raw electroencephalogram (EEG) data from three male subjects, and electrodes were placed on the scalp of the frontal lobe and both temporal lobes which are related to thinking and verbal function. Eigenvalues of the covariance matrix of the EEG data were used as the feature vector of each vowel. In the analysis, we provided the classification results of the back propagation artificial neural network (BP-ANN) for making a comparison with DBN. As a result, the classification results from the BP-ANN were 52.04%, and the DBN was 87.96%. This means the DBN showed 35.92% better classification results in multi class imagined speech classification. In addition, the DBN spent much less time in whole computation time. In conclusion, the DBN algorithm is efficient in BCI system implementation.

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With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electroencephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is necessary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.

In article number 2105485, Tae-Yeon Seong and co-workers experimentally demonstrate dynamic photoadaptation behavior of an integrated optoelectronic device array that mimics the functionality of the biological visual nervous system. The device array, which is designed to adapt to repeated optical stimuli that change according to external conditions, exhibits excellent performance. This autonomic response to stimuli is essential to the improvement of nextgeneration bionic electronics.