Memory analysis for memristors and memristive recurrent neural networks
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 memristors' 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.
Yide Zhang
NIH K99 Postdoctoral Fellow
My research is interdisciplinary and focused on developing new types of optical imaging techniques that could advance the work of other researchers and medical personnel in a wide variety of fields. Currently, I am developing next-generation photoacoustic and ultrafast imaging techniques that can observe biological and physical phenomena that are too fast to be imaged with existing methods. The observation of the ultrafast phenomena could provide a better understanding of the fundamentals of life and physical sciences. I am also developing novel quantum imaging approaches that can investigate biological organisms with an imaging performance that cannot be achieved using classical optical imaging. In my free time, I enjoy cooking, hiking, cycling, and traveling.