Le Yang

Genetic programming (GP) has achieved promising results without relying on the extraction of prior knowledge, e.g., fixed network architecture. However, most existing GP methods guide the evolutionary process by optimizing only the fitness function. In the few-shot image classification task, this single optimization approach can cause the model to converge prematurely to a local optimum, resulting in overfitting. Recently, knowledge learning-assisted evolutionary algorithms have demonstrated remarkable performance by making full use of evolutionary information to guide the evolutionary process. However, existing methods cannot be directly applied to a GP-based method. To address these limitations, we propose a novel framework called Probabilistic Genetic Programming with Knowledge Learning (POGP). Specifically, we design the Redress-Forget Mechanism of External Archiving to explore the potential of global information and to re-update external archives using a validation set, effectively utilizing evolutionary information while maintaining population diversity. Furthermore, the Probabilistic Initialization Strategy is designed to initialize the population using evolutionary information when it falls into a local optimum, aiming to enhance the population’s exploration ability. Moreover, we introduce Knowledge Learning-guide Evo-operations to improve the algorithm’s ability to develop optimal solutions by fusing local and global information to guide the population’s search direction. The experiments on benchmark datasets indicate that POGP outperforms state-of-the-art neural network-based and GP-based methods in almost all comparisons, demonstrating the effectiveness of the proposed POGP framework.

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size.

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.

Most memristor-based Pavlovian associative memory neural networks explore the impact of relearning and forgetting on associative memory rates. In this paper, a memristor-based neural network circuit with unconditioned response mechanisms is proposed. The circuit is composed of neuron modules, synapse modules, forgetting voltage control modules, and effective time judgment modules. The proposed circuit achieves control of the forgetting rate, which can be adjusted based on the interval of the unconditioned stimuli. Additionally, the concept of effective time judgment has been introduced. If the stimulus interval exceeds the range of effective time, the circuit will automatically proceed with natural forgetting. Furthermore, the synaptic weight between the food neuron and the salivation neuron is no longer fixed but can change based on the method of food presentation. When only looking at the food but not eating it, the connection between the visual neuron and the salivation neuron decreases, resulting in reduced salivation when seeing the food. However, after eating the food, the connection between the visual neuron and the salivation neuron quickly strengthens. This circuit further refines the functionality of neural network circuits based on the mechanisms of unconditional reflexes. Finally, these functions are validated using PSPICE.

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.

Memristors, as a novel non-volatile circuit component, hold significant promise in electronic circuit applications. This article presents a novel memristor-based multiplier design, consisting of three key stages: an absolute value circuit, a multiplication circuit, and a sign circuit. The multiplier is capable of performing multiplication operations within the range of 1V to 10V, and it can achieve multiplication even when both input values are set to zero through deliberate circuit design, thereby eliminating the need for analog-to-digital signal conversion. This innovation reduces circuit complexity and energy consumption, enhancing overall circuit efficiency.

Pavlov conditioning theory can explain part of biological behavior accurately, and has a profound influence on the development of behavioral psychology. Reinforcement, extinction, generalization and differentiation are the four basic principles of conditional reflex. Among them, reinforcement and extinction respectively correspond to learning and forgetting in Pavlov associative memory, and have been widely discussed. However, there are few studies on generalization and differentiation, and the circuits designed in the past cannot precisely reflect the experimental phenomenon. In this paper, a generalization and differentiation circuit based on neural mechanisms is designed using memristors. This circuit improves on the previous generalization and differentiation circuits, so that it can distinguish similar but different tactile stimulus, and regularly generate different outputs according to the degree of similarity. The difference in the output corresponds to the variation in saliva secretion in response to different tactile stimuli in the biological system. At the same time, the number of tactile stimuli is increased, which helps to improve the resolution of the circuit to distinguish similar stimuli. In addition, this paper considers the connections of neurons from the perspective of neural mechanisms, which makes the circuit structure more conform to biological properties. Finally, the above-mentioned functions have been verified in PSPICE.