Memristor Cellular Automata and Memristor Discrete-Time Cellular Neural Networks

By M. Itoh, Leon Ong Chua. International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, 2009

310 citations recorded by the source corpus.

In this paper, we design a cellular automaton and a discrete-time cellular neural network (DTCNN) using nonlinear passive memristors. They can perform a number of applications, such as logical operations, image processing operations, complex behaviors, higher brain functions, etc. By modifying the characteristics of nonlinear memristors, the memristor DTCNN can perform almost all functions of memristor cellular automaton. Furthermore, it can perform more than one function at the same time, that is, it allows multitasking.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8efe9c41f003c3a260b7aff09fdaa5fa1b87df6f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Andy Thomas
2. Zhigang Zeng
3. Shiping Wen

In Computer Networks and Communications

1. Andy Thomas
2. Zhigang Zeng
3. Shiping Wen

In Electrical and Electronic Engineering

1. Andy Thomas
2. Zhigang Zeng
3. Shiping Wen
4. Ailong Wu
5. Hyongsuk Kim
6. Maheshwar Prasad Sah
7. Leon Ong Chua
8. Changju Yang

Citations in the corpus

• Memristor-based neural networks (2013, background): “…a more computer science view on learning in neural networks, Carpenter provides a good review [106], whereas Snider describes instar/outstar learning with memristive devices in particular [107], and Itoh and Chua discuss logical operations, image processing operations and complex behaviors [108].”
• Neural Synaptic Weighting With a Pulse-Based Memristor Circuit (2012, background): “models [14]–[19] and various applications of memristor devices [20]–[27].”
• Memristor Emulator for Memristor Circuit Applications (2012, background): “2188957 Due to such unusual features of the memristor, many scientists had started to exploit the memristor for analog [6], [7] and digital information processing [8], [9] applications.”
• Synchronization control of a class of memristor-based recurrent neural networks (2012, background): “Motivated by the above discussions, based on the works in [2,5,11,14,15], our objective in this paper is to study the exponential synchronization for memristor-based recurrent neural networks (4).”