Most of the classical conditioning laws implemented by existing circuits are involved in learning and forgetting between only three neurons, and the problems between multiple neurons are not considered. In this article, a multimode generalization and differentiation circuit for the Pavlov associative memory is proposed based on memristors. The designed circuit is mainly composed of voltage control modules, synaptic neuron modules, and inhibition modules. The secondary differentiation is accomplished through the process of associative learning and forgetting among multiple neurons. The process of multiple generalization and differentiation is realized based on the nonvolatility and thresholding properties of memristors. The extinction inhibition and differentiation inhibition in forgetting is considered through the inhibition modules. The Pavlov associative memory neural network with multimodal generalization and differentiation may provide a reference for the further development of brain-like intelligence.

The 1-D and 2-D emotion models realized by hardware circuits have been studied. However, 3-D emotional model which is most suitable for human emotion has not been considered. In this article, a bionic circuit of 3-D emotional space model is proposed, which can generate brain-like emotions according to the information of visual, speech, and text. The designed memristor circuits are based on the brain emotion theory of limbic system, including thalamus, sensory cortex, orbitofrontal cortex, cingulate gyrus, amygdala, and other circuit modules. Moreover, the perceptual brain and rational brain in the memristor circuits are also considered. The 3-D model in this article is a PAD emotional space model composed of three dimensions: 1) pleasure (P); 2) arousal (A); and 3) dominance (D). Many emotions can be expressed by using the PAD emotional model. In addition, the PAD emotional model composed of memristor circuits is applied to mood congruity, which considers the relationship between emotion and learning. The PAD emotion designed by memristor circuits may provide a reference for bionic robot to realize human–computer emotional companionship.

Most of the operant conditioning only consider the basic theory, but the influencing factors such as immediacy and satiety are ignored. In this paper, a memristor neural network circuit based on operant conditioning with immediacy and satiety is proposed. The designed circuit is mainly composed of voltage control modules, synapse modules and promotion-suppression modules. The various processes of operant conditioning theory are realized. The relationship between positive reward and negative punishment is implemented through promotion-suppression modules and synapse modules. The influence of immediacy on operant conditioning is achieved through voltage control module. The effect of satiety on operant conditioning is discussed using memristive non-volatility. The study of operant conditioning may provide a reference for smarter brain-like nerves.

The factors that affect learning efficiency in different environments have been considered in many studies, but multimode mood-consistent learning has not been considered specifically. In this paper, a neural network circuit based on memristors to determine multimode mood consistency and mood dependency was constructed. The circuit is composed of a voltage control module, an emotion module, and a synaptic neuron module. Through the voltage control module and emotion module, learning materials with different properties are input into the synaptic neurons. The learning efficiency of different learning materials under different emotions was analyzed in detail. A dual-channel mood-consistent learning was realized, and the mood dependency was further considered. The influence of different channels on the learning was studied to provide ideas for the future development of intelligent brain-like neural networks.