Optogenetics-inspired manipulation of synaptic memory using all-optically controlled memristors.

Memristive synapses compatible with optogenetic techniques allow for the fast and low-power manipulation of memory activities using light in artificial neural systems. However, most of the optoelectronic memristors operate in the hybrid optic-electric mode; the reversible regulation of memristive states solely using light for optogenetic emulation is difficult. In this work, an all-optical controlled optoelectronic memristor (Au/Cs2AgBiBr6/Au) is developed for mimicking optogenetics-tuned memory formation and erasing behaviors in biological synapses. We show that the memristor exhibits positive and negative persistent photoconductivity effects under different light wavelengths, attributed to light-regulated carrier de-trapping/trapping at the Au/Cs2AgBiBr6 interface. This device can emulate both excitatory and inhibitory synaptic plasticity and associated learning and memory effects under light illumination. We constructed a prototype optoelectronic synaptic array and implemented the all-optically controlled memory implantation, erasing, and modification, demonstrating the light-reconfigured cognition capabilities. Our findings will inspire the development of all-optically controlled artificial neural systems with good reconfigurability for efficient neuromorphic computing and machine vision.

Near-Sensor Reservoir Computing for Gait Recognition via a Multi-Gate Electrolyte-Gated Transistor.

The recent emergence of various smart wearable electronics has furnished the rapid development of human-computer interaction, medical health monitoring technologies, etc. Unfortunately, processing redundant motion and physiological data acquired by multiple wearable sensors using conventional off-site digital computers typically result in serious latency and energy consumption problems. In this work, a multi-gate electrolyte-gated transistor (EGT)-based reservoir device for efficient multi-channel near-sensor computing is reported. The EGT, exhibiting rich short-term dynamics under voltage modulation, can implement nonlinear parallel integration of the time-series signals thus extracting the temporal features such as the synchronization state and collective frequency in the inputs. The flexible EGT integrated with pressure sensors can perform on-site gait information analysis, enabling the identification of motion behaviors and Parkinson's disease. This near-sensor reservoir computing system offers a new route for rapid analysis of the motion and physiological signals with significantly improved efficiency and will lead to robust smart flexible wearable electronics.

Reservoir Computing-Based Design of ZnO Memristor-Type Digital Identification Circuits.

Reservoir Computing (RC) is a network architecture inspired by biological neural systems that maps time-dimensional input features to a high-dimensional space for computation. The key to hardware implementation of the RC system is whether sufficient reservoir states can be generated. In this paper, a laboratory-prepared zinc oxide (ZnO) memristor is reported and modeled. The device is found to have nonlinear dynamic responses and characteristics of simulating neurosynaptic long-term potentiation (LTP) and long-term depression (LTD). Based on this, a novel two-level RC structure based on the ZnO memristor is proposed. Novel synaptic encoding is used to maintain stress activity based on the characteristics of after-discharge and proneness to fatigue during synaptic transmission. This greatly alleviates the limitations of the self-attenuating characteristic reservoir of the duration and interval of the input signal. This makes the reservoir, in combination with a fully connected neural network, an ideal system for time series classification. The experimental results show that the recognition rate for the complete MNIST dataset is 95.08% when 35 neurons are present as hidden layers while achieving low training consumption.

A flexible dual-gate hetero-synaptic transistor for spatiotemporal information processing.

Artificial synapses based on electrolyte gated transistors with conductance modulation characteristics have demonstrated their great potential in emulating the memory functions in the human brain for neuromorphic computing. While previous studies are mostly focused on the emulation of the basic memory functions of homo-synapses using single-gate transistors, multi-gate transistors offer opportunities for the mimicry of more complex and advanced memory formation behaviors in biological hetero-synapses. In this work, we demonstrate an artificial hetero-synapse based on a dual-gate electrolyte transistor that can implement in situ spatiotemporal information integration and storage. We show that electric pulses applied on a single gate or unsynchronized electric pulses applied on dual gates only induce volatile conductance modulation for short-term memory emulation. In contrast, the device integrates the electric pulses coincidently applied on the dual gates in a supralinear manner and exhibits nonvolatile conductance modulation, enabling long-term memory emulation. Further studies prove that artificial neural networks based on such hetero-synaptic transistors can autonomously filter the random noise signals in the dual-gate inputs during spatiotemporal integration, facilitating the formation of accurate and stable memory. Compared to the single-gate synaptic transistor, the classification accuracy of MNIST handwritten digits using the hetero-synaptic transistor is improved from 89.3% to 99.0%. These findings demonstrate the great potential of multi-gate hetero-synaptic transistors in simulating complex spatiotemporal information processing functions and provide new platforms for the design of advanced neuromorphic computing systems.

The Relationship Between K-Workers' Leader-Member Exchange, Organizational Citizenship Behavior and Task Performance-Evidence From Chinese Hospitals.

Aiming to reduce the difficulty of managing and motivating knowledge workers (k-workers), and promote the psychological well-being of them in Chinese hospitals, this study examines how k-workers' leader-member exchange (LMX) influences their task performance and the mediation effect of organizational citizenship behavior (OCB). Through a self-administered survey, valid questionnaires were collected from 384 k-workers in Chinese hospitals, and partial least squares structural equation modeling was employed for data analysis. The findings show that LMX is positively related to OCB and task performance, and that OCB mediates the relationship between LMX and task performance. This research has theoretical implications and also provides practical suggestions on how to manage, motivate, and inspire k-workers, and promote the psychological well-being of them, and finally enhance the organizational performance in Chinese hospitals.

A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition.

The interference of noise will cause the degradation of image quality, which can have a negative impact on the subsequent image processing and visual effect. Although the existing image denoising algorithms are relatively perfect, their computational efficiency is restricted by the performance of the computer, and the computational process consumes a lot of energy. In this paper, we propose a method for image denoising and recognition based on multi-conductance states of memristor devices. By regulating the evolution of Pt/ZnO/Pt memristor wires, 26 continuous conductance states were obtained. The image feature preservation and noise reduction are realized via the mapping between the conductance state and the image pixel. Furthermore, weight quantization of convolutional neural network is realized based on multi-conductance states. The simulation results show the feasibility of CNN for image denoising and recognition based on multi-conductance states. This method has a certain guiding significance for the construction of high-performance image noise reduction hardware system.