Long short-term memory (LSTM) with significantly increased complexity and a large number of parameters have a bottleneck in computing power resulting from limited memory capacity. Hardware acceleration of LSTM using memristor circuit is an effective solution. This paper presents a complete design of memristive LSTM network system. Both the LSTM cell and the fully connected layer circuit are implemented through memristor crossbars, and the 1T1R design avoids the influence of the sneak current which helps to improve the accuracy of network calculation. To reduce the power consumption, the word embedding dimensionality was reduced using the GloVe model, and the number of features in the hidden layer was reduced. The effectiveness of the proposed scheme is verified by performing the text classification task on the IMDB dataset and the hardware training accuracy reached as high as 88.58%.

In this paper, a new memcapacitor model and its corresponding circuit emulator are proposed, based on which, a chaotic oscillator is designed and the system dynamic characteristics are investigated, both analytically and experimentally. Extreme multistability and coexisting attractors are observed in this complex system. The basins of attraction, multistability, bifurcations, Lyapunov exponents, and initial-condition-triggered similar bifurcation are analyzed. Finally, the memcapacitor-based chaotic oscillator is realized via circuit implementation with experimental results presented.

Most operant conditioning circuits predominantly focus on simple feedback process, few studies consider the intricacies of feedback outcomes and the uncertainty of feedback time. This paper proposes a neuromorphic circuit based on operant conditioning with addictiveness and time memory for automatic learning. The circuit is mainly composed of hunger output module, neuron module, excitement output module, memristor-based decision module, and memory and feedback generation module. In the circuit, the process of output excitement and addiction in stochastic feedback is achieved. The memory of interval between the two rewards is formed. The circuit can adapt to complex scenarios with these functions. In addition, hunger and satiety are introduced to realize the interaction between biological behavior and exploration desire, which enables the circuit to continuously reshape its memories and actions. The process of operant conditioning theory for automatic learning is accomplished. The study of operant conditioning can serve as a reference for more intelligent brain-inspired neural systems.

A physical ${\text{Sr}}_{0.95}{\text{Ba}}_{0.05}{\text{TiO}}_3$ memristor-based modified Chua’s circuit is proposed, which is studied in this paper by means of both theoretical analysis and numerical simulations. The stability of this memristor-based modified Chua’s circuit is analyzed. A systematic investigation of the dynamic behaviors and their dependence on the initial states and circuit parameters is performed, presenting the Lyapunov exponents spectra, bifurcation diagrams, phase diagrams, and Poincaré maps. The circuit emerges multiple dynamic behaviors, including stable points, periodic cycles, chaos, and some transient chaos. Specifically, the system has multistability, i.e., coexisting periods and coexisting chaotic attractors with the change of initial states and circuit parameters. This study is conducive to the subsequent design and analysis of memristor-based circuits for potential practical applications.