Memory analysis for memristors and memristive recurrent neural networks
Gang Bao, Yide Zhang, Zhigang Zeng
Abstract
Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers. Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis, i.e., the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristorsʼ mathematical models with linear and nonlinear drift. Secondly, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.