Memristor-Based Neural Network Circuit of Delay and Simultaneous Conditioning

Most conventional memristor-based Pavlov associative memory neural network circuits have been working on realizing the learning and forgetting functions of simultaneous conditioning. However, the time interval between unconditional stimulus and conditional stimulus is a critical variable in classical conditioning. Different unconditional and conditional stimulus intervals evoke associative memory of brains with different rates. For example, learning in simultaneous conditioning is less effective than delay conditioning. Therefore, a memristor-based neural network circuit of delay and simultaneous conditioning is designed. The proposed circuit consists of learning states detection module, voltage control module, and synapse module. Many functions, such as short-delay conditioning learning, long-delay conditioning learning, simultaneous conditioning learning, experience learning, and two types of forgetting are implemented by the circuit. In particular, the so-called experience learning is that learning the forgotten knowledge will be faster than before and learning again will forget more slowly. This function is more bionic. The correctness of the design is demonstrated through simulations using PSPICE.