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Conventional circuit elements are constrained by limitations in area and power efficiency at processing physical signals. Recently, researchers have delved into high-order dynamics and coupled oscillation dynamics utilizing Mott devices, revealing potent nonlinear computing capabilities. However, the intricate yet manageable population dynamics of multiple artificial sensory neurons with spatiotemporal coupling remain unexplored. Here, we present an experimental hardware demonstration featuring a capacitance-coupled VO2 phase-change oscillatory network. This network serves as a continuous-time dynamic system for sensory pre-processing and encodes information in phase differences. Besides, a decision-making module for special post-processing through software simulation is designed to complete a bio-inspired dynamic sensory system. Our experiments provide compelling evidence that this transistor-free coupling network excels in sensory processing tasks such as touch recognition and gesture recognition, achieving significant advantages of fewer devices and lower energy-delay-product compared to conventional methods. This work paves the way towards an efficient and compact neuromorphic sensory system based on nano-scale nonlinear dynamics. It is challenging to create efficient and compact neuromorphic sensory system based on nano-scale nonlinear dynamics. Yang et al. present a capacitance-coupled VO2 oscillatory network, which serves as a continuous-time dynamic system for sensory pre-processing, enabling tasks such as touch and gesture recognition. [ABSTRACT FROM AUTHOR]

Wireless internet-of-things (WIoT) with data acquisition sensors are evolving rapidly and the demand for transmission efficiency is growing rapidly. Frequency converter that synthesizes signals at different frequencies and mixes them with sensor datastreams is a key component for efficient wireless transmission. However, existing frequency converters employ separate synthesize and mix circuits with complex digital and analog circuits using complementary metal-oxide semiconductor (CMOS) devices, naturally incurring excessive latency and energy consumption. Here we report a highly uniform and calibratable VO2 memristor oscillator, based on which we build memristor-based frequency converter using 8 × 8 VO2 array that can realize in-situ frequency synthesize and mix with help of compact periphery circuits. We investigate the self-oscillation based on negative differential resistance of VO2 memristors and the programmability with different driving currents and calibration resistances, demonstrating capabilities of such frequency converter for in-situ frequency synthesize and mix for 2 ~ 8 channels with frequencies up to 48 kHz for low frequency transmission link. When transmitting classical sensor data (acoustic, vision and spatial) in an end-to-end WIoT experimental setup, our VO2-based memristive frequency converter presents up to 1.45× ~ 1.94× power enhancement with only 0.02 ~ 0.21 dB performance degradations compared with conventional CMOS-based frequency converter. This work highlights the potential in solving frequency converter's speed and energy efficiency problems in WIoT using high crystalline quality epitaxially grown VO2 and calibratable VO2-based oscillator array, revealing a promising direction for next-generation WIoT system design. Frequency converters for wireless internet of things applications typically require separate circuits for different functions, causing energy and performance inefficiencies. Using an epitaxially grown VO2 memristor array, Liu et al. present a frequency converter with in-situ frequency synthesis and mix functionality. [ABSTRACT FROM AUTHOR]

Neuromorphic computing is expected to bridge cognitive behaviors with computing systems in an efficient, expandable, and biologically inspired way. A pivotal cognitive behavior is the attention mechanism, which is highly important in filtering and regulating spatio‐temporal information. Emerging neuromorphic devices hold prospect in utilizing their internal physical mechanisms for dynamic computing resources. Here, a basic top–down attention computing component consisting of a synaptic transistor and a neuron is proposed, where efficient information processing is realized by combining the inherent device dynamics and the feedback loop. A theoretical model is established in simulation to demonstrate the capabilities of such a computing system in information filtering and control. Notably, new dynamic circuit behaviors, such as conductance oscillation and activate function switching, are discovered from appropriate time parameters. The attention computing component contains rich dynamic behaviors, providing a power and area‐saving method to construct high‐complexity neuromorphic systems for spatio‐temporal signal preprocessing and control. [ABSTRACT FROM AUTHOR]

Neuromorphic systems provide a potential solution for overcoming von Neumann bottleneck and realizing computing with low energy consumption and latency. However, the neuromorphic devices utilized to construct the neuromorphic systems always focus on artificial synapses and neurons, and neglected the important role of astrocyte cells. Here, an astrocyte memristor is demonstrated with encapsulated yttria‐stabilized zirconia (YSZ) to emulate the function of astrocyte cells in biology. Due to the high oxygen vacancy concentration and resultant high ionic conductivity of YSZ, significantly lower forming and set voltages are achieved in the artificial astrocyte, along with high endurance (>1011). More importantly, the nonlinearity in current‐voltage characteristics that usually emerge as the testing cycle increases can be depressed in the astrocyte memristor, and the nonlinearity can also be reversed by applying a refresh operation, which implements the role of biological astrocyte in maintaining the normal activity of neurons. The recovery of linearity can dramatically improve the accuracy of Modified National Institute of Standards and Technology dataset classification from 62.98% to 94.75% when the inputs are encoded in voltage amplitudes. The astrocyte memristor in this work with improved performance and linearity recovery characteristics can well emulate the function of astrocyte cells in biology and have great potential for neuromorphic computing. [ABSTRACT FROM AUTHOR]