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To efficiently process the massive amount of sensor data, it is demanding to develop a new paradigm. Inspired by neurobiological systems, an infrared near‐senor reservoir computing (RC) system, consisting of infrared sensors and memristors based on single‐crystalline LiTaO3 and LiNbO3 (LN) thin film respectively, is demonstrated. The analog memristor is used as a reservoir in the RC system to process sensor signals with spatiotemporal characteristics. LN crystal structure stacked with oxygen octahedra provides favorable conditions for reliable Mott variable‐range hopping conduction, which provides the memristor with tens of thousands of reservoir states within a large dynamic range. With the characteristics, the analog sensor signals with high data fidelity can be directly fed to the memristive reservoir, and the spatiotemporal features can be separated and mapped. The system demonstrated a dynamic gesture perception task, achieving an accuracy of 99.6%, which highlights the great application potential of the memristor in signal sensor processing and will advance the application of artificial intelligence in sensor systems. Crystal ion slicing techniques are used to fabricate a single‐crystalline thin film for both the memristor and sensor, which opens up the possibility of realizing monolithic integration of a memristor‐based near‐sensor computing system. A novel infrared near‐sensor reservoir computing (RC) system constructed from an ion‐slicing LiTaO3‐based infrared array and ion‐slicing LiNbO3‐based memristor array is demonstrated. Thanks to the excellent capacities of the memristor reservoir, the infrared near‐senor RC system successfully and robustly implemented a dynamic gesture perception task with spatiotemporal feature fusion.

Inspired by human brain, the emerging analog‐type memristor employed in neuromorphic computing systems has attracted tremendous interest. However, existing analog memristors are still far from accurate tuning of multiple conductance states, which are crucial from the device‐level view. Herein, a reliable analog memristor based on ion‐slicing single‐crystalline LiNbO3 (LNO) thin film is demonstrated. The highly ordered LNO crystal structure provides a stable pathway of oxygen vacancy migration, which is contributed to a stable Mott variable‐range hopping process in trap sites. Excellent analog switching characteristics with high reliability and repeatability, including long retention/great endurance with small fluctuation (fluctuated within 0.22%), a large dynamic range of two orders of magnitude, hundreds of distinguishable conductance states with tunable linearity, and ultralow cyclic variances for multiple weight updating (down to 0.75%), are realized with the proposed memristor. As a result, a multilayer perceptron with a high recognition accuracy of 95.6% for Modified National Institute of Standards and Technology dataset is realized. The proposed analog memristive devices based on ion‐slicing single‐crystalline thin films offer a novel strategy for fabricating high‐performance memristors that combined linear tunability and long‐term repeatability, opening a novel avenue for neuromorphic computing application. A reliable analog memristor based on ion‐slicing single‐crystalline LiNbO3 (LNO) thin film is demonstrated. Relying on the highly ordered crystal structure of LNO single crystal, the reversible Mott variable‐range hopping conduction dominates the device switching behavior and guarantees an analog dynamic range of two orders of magnitude with hundreds of distinguishable intermediate states for high‐performance neuromorphic computing applications.

Memristor chaotic system has been attracted by many researchers because of the rich dynamical behaviors. However, some existed memristor chaotic systems have finite numbers of chaotic attractors. In this paper, a simple, effective method is given for designing the autonomous memristor chaotic systems of infinite chaotic attractors. Autonomous memristor chaotic systems are proposed from the start of memristor chaotic system counterparts. Three-dimensional, four-dimensional, and five-dimensional memristor chaotic systems are given in standard form with sine functions and tangent functions to prove the effectiveness of this method. Eventually, an analog circuit of three-dimensional memristor chaotic system is designed and implemented to prove its feasibility.

Neural networks are favored by academia and industry because of their diversity of dynamics. However, it is difficult for ring neural networks to generate complex dynamical behaviors due to their special structure. In this paper, we present a memristive ring neural network (MRNN) with four neurons and one non-ideal flux-controlled memristor. The memristor is used to describe the effect of external electromagnetic radiation on neurons. The chaotic dynamics of the MRNN is investigated in detail by employing phase portraits, bifurcation diagrams, Lyapunov exponents and attraction basins. Research results show that the MRNN not only can generate abundant chaotic and hyperchaotic attractors but also exhibits complex multistability dynamics. Meanwhile, an analog MRNN circuit is experimentally implemented to verify the numerical simulation results. Moreover, a medical image encryption scheme is constructed based on the MRNN from a perspective of practical engineering application. Performance evaluations demonstrate that the proposed medical image cryptosystem has several advantages in terms of keyspace, information entropy and key sensitivity, compared with cryptosystems based on other chaotic systems. Finally, hardware experiment using the field-programmable gate array (FPGA) is carried out to verify the designed cryptosystem.

Investigating the chaotic dynamics in neural networks holds significant importance in elucidating brain-like neural activities and guiding brain-like learning. The multi-scroll chaos, due to its intricate topological structure, has garnered interest in the study of brain-like chaotic neural networks. Previous researches have primarily focused on ordinary multi-scroll attractors, while there has been little research on symmetric multi-scroll attractors. Symmetric attractors are typically more diverse and have more flexible evolutionary and higher stability which may lead to more stable system responses. The purpose of this paper is to investigate the symmetric multi-scroll phenomenon generated under the influence of the memristor controlled by multi-level-logic pulse in Hopfield Neural Network (HNN). Firstly, a memristive HNN capable of generating multi-scroll is proposed, serving as the foundation for studying the influence of multi-level-logic pulse. Through theoretical and numerical analysis, the dynamic behavior of the proposed memristive HNN is examined and simulation results reveal the emergence of multi-scroll attractors and initial offset coexisting behavior. Subsequently, a multi-level-logic pulse is introduced into the memristor to simulate one of its parameters. The experimental results reveal that the introduction of multi-level-logic pulse expands the original multi-scroll structure into a symmetric structure. Furthermore, it enlarges the chaotic parameter range of the system, which holds significant implications for the study of neural dynamics. Finally, the correctness of the proposed model is verified through hardware experiments. This study provides valuable guidance for neural dynamics researches and the application of memristors.

In this paper, a meminductor emulator and an active memristor emulator are designed to construct a new chaotic circuit. The initial-condition-triggered amplitude, frequency and parameter space boosting are investigated. The system owns homogenous, heterogeneous and extreme multistabilities at the same time. Various coexisting attractors with different offsets, amplitudes and frequencies are observed and analyzed. Furthermore, the presented circuit is implemented by analog circuit and DSP platform. The mentioned unique dynamic features are confirmed in the experiments. Experimental results indicate the presented system and its initial-condition-triggered features can be realized in DSP digital system. Since the system owns variable amplitude, frequency and parameter space, it has great potential value in encryption engineering fields.

In this paper, we investigate the occurrence of superextreme spiking (SES) oscillations and multistability behavior in a memristor-based Hindmarsh–Rose neuron model. The presence of SES oscillations has been identified as arising due to the occurrence of an interior crisis. As the membrane current I ( t ), considered as the control parameter is varied, the system transits from bounded chaotic spiking (BCS) oscillations to SES oscillations. These transitions are captured numerically using geometrical representations like time series plots, phase portraits and inter-spikes interval return maps. The characterization of SES from the BCS oscillations is made using statistical tools such as phase shift analysis and probability density distribution function. The multistability nature has been observed using bifurcation analysis and confirmed by the Lyapunov exponents for two different sets of initial conditions. The numerical simulations are substantiated through real-time hardware experiments realized through a nonlinear circuit constructed using an analog model of the memristor.

When taking a memristor as a coupler to connect two memristive systems, the intricate initial condition-dependent coexisting and synchronous behaviors could be achieved, which have not been comprehensively concerned in literature. This work presents a memristor-coupled homogeneous network consisting of two identical non-autonomous memristive Fitzhugh–Nagumo models and investigates its coexisting and synchronous behaviors. Kinetic analysis shows that the network can exhibit hidden extreme multistability similar to that of the individual non-autonomous memristive Fitzhugh–Nagumo model. Coexisting hidden hyperchaotic, chaotic, periodic, and quasi-periodic attractors are numerically revealed, and their synchronicities are controlled by the initial condition and coupling strength of the coupling memristor. The synchronous effects of the coupling strength and initial conditions of the network are numerically revealed using normalized mean synchronization errors. Complete and parallel-offset synchronous behaviors are realized with a large positive coupling strength and a negative initial condition of the coupling memristor. In addition to these two synchronous behaviors, phase synchronization is easily achieved due to the existence of external stimuli. These synchronous states are flexibly controlled by the initial conditions. Furthermore, an analog circuit is designed for the memristor-coupled homogenous network and circuit simulations are performed to verify the numerical results.

It is believed that local activation is the origin of all complexities, and the locally active memristive synaptic neural network can generate complex chaotic dynamic behaviors, such as hyperchaotic, multi-scroll, multi-stability and hidden dynamical behaviors. However, there are few studies on the simultaneous occurrence of multiple complex dynamic behaviors in neural networks. No chaotic system of multi-scroll hyperchaotic hidden attractors based on neural network has been found yet. To solve the problem, in this paper, we propose a new locally active memristive Hopfield neural network (HNN) model based on a multi-segment function, which is affected by electromagnetic radiation and external current. The multi-scroll hyperchaotic hidden attractors are found in the memristive HNN for the first time. Theoretical analysis and numerical simulation results show that the memristive HNN model has no equilibrium point, and the number of multi-scroll attractors is controlled by the state equation parameters of the memristive synapse. In addition, the structures and number of scrolls are also affected by electromagnetic radiation and external current. At the same time, under the appropriate parameter conditions, by modifying the initial value of the system, the memristive HNN has a controllable number of coexisting attractors, showing extreme multi-stability. Finally, a memristive HNN analog circuit is designed. The hardware experiment results reproduce the multi-scroll dynamics phenomenon, which verifies the correctness of the theoretical analysis and numerical simulation.

In this paper, a S-type memristor with tangent nonlinearity is proposed. The introduced memristor can generate two kinds of stable pinched hysteresis loops with initial conditions from two flanks of the initial critical point. The power-off plot verifies that the memristor is nonvolatile, and the DC V - I plot shows that the memristor is locally active with the locally active region symmetrical about the origin. The equivalent circuit of the memristor, derived by small-signal analysis method, is used to study the dynamics near the operating point in the locally active region. Owing to the bistable and locally active properties and S-type DC V - I curve, this memristor is called S-type BLAM for short. Then, a new Wien-bridge oscillator circuit is designed by substituting one of its resistances with S-type BLAM. It finds that the circuit system can produce chaotic oscillation and complex dynamic behavior, which is further confirmed by analog circuit experiment.