Memristive neuromorphic systems are emerging potential hardware platforms to implement artificial neural networks. Combining features of memristive neuromorphic systems with associative memory, this article proposes an associative-memory-based reconfigurable memristive neuromorphic system. In the proposed system, there are two neural networks: 1) the neural network for computing acceleration and 2) the neural network mimicking associative memory. Then, a case study of the system is presented, which includes an associative memory network to realize apple recognition and a computing acceleration network for iris classification. The associative memory network depends on associative learning to achieve the recognition function. In addition, during the corresponding forgetting process, the connections of the related synaptic circuits are cut off and sent to a synaptic circuit block, realizing variable circuit topology. Further, the synaptic circuits in the block are applied to construct the iris classification network, accomplishing the reconfiguration of the proposed system. The circuit structure of this classification network matches backpropagation (BP) algorithm well. Meanwhile, the network reaches a relatively high classification accuracy after training. In an iteration of the training, all the synaptic circuits that need to change can adjust weights synchronously, which improves training speed.

Currently, studies on memristor-based operant conditioning circuits concentrate on the learning process of the single behaviors, without attention to the chaining process composed of multiple behaviors. This paper proposes a memristor-based circuit design of biological behavior chain, which consists of behavior modules, learning modules, delay modules, and satiety modules. After learning, the mouse can complete a chain process consisting of three behaviors: pressing button A to eliminate electrical stimulation, pressing button B to obtain food, and pressing button C to open the cage. Behavior and delay modules are used to simulate these three behaviors, the learning module is used to simulate the learning process of chaining between two behaviors and chaining between three behaviors. In addition, the study also explored the effects of mouse satiety on the experiment, as well as processes such as natural forgetting and relearning. The simulation results in PSPICE indicate that the circuit is capable of effectively simulating the aforementioned functions. Furthermore, Monte Carlo analysis and temperature simulation analysis are conducted on the circuit. The simulation results confirm that the circuit exhibits good stability.

The performance improvement of CMOS computer fails to meet the enormous data processing requirement of artificial intelligence progressively. The memristive neural network is one of the most promising circuit hardwares to make a breakthrough. This paper proposes a novel memristive synaptic circuit that is composed of four MOS transistors and one memristor (4T1M). The 4T1M synaptic circuit provides flexible control strategies to change memristance or respond to the input signal. Applying the 4T1M synaptic circuit as the cell of memristive crossbar array, based on the structure and algorithm of the back-propagation (BP) neural network, this paper proposes circuit design of the memristive crossbar-based BP neural network. By reusing the 4T1M memristive crossbar array, the computations in the forward-propagation process and back-propagation process of BP neural network are accomplished on the memristive crossbar-based circuit to accelerate the computing speed. The 4T1M memristive crossbar array can change all the cells’ memristance at a time, accordingly, the memristive crossbar-based BP neural network can realize synchronous memristance adjustment. The proposed memristive crossbar-based BP neural network is then evaluated through experiments involving XOR logic operation, iris classification, and MNIST handwritten digit recognition. The experimental results present fewer iterations or higher classification accuracies. Further, the comprehensive comparisons with the existing memristive BP neural networks highlight the advantages of the proposed memristive crossbar-based BP neural network, which achieves the fastest memristance adjustment speed using relatively few components.