Abstract
The papers in this special section explore the use of large scale memristive systems and neurochips for computational intelligence. The interest in the application of memristor device as a memory element, neuromorphic device, as a sensor and as a switch for programmable logic circuits has been growing in the last decade. The small size, ease of programmability, low leakage currents, ability to maintain resistance states and CMOS compatibility make the memristor a useful device. The possibility of using memristors to mimic neural circuits and architectures as well as to implement learning for in-memory computing tasks makes it further a versatile device. However, in this early stages of development and exploration, modeling and simulation of the realistic memristors in application to simulating large-scale arrays remains a challenging issue among others. The development of memristive device models that reflect the realistic characteristic of memristor devices is an ongoing challenge. While most memristor models close to reflect the electrical characteristics of the memristors, the time dynamics properties under non-ideal conditions, accurately modeling variability of fabricated memristors, and faster convergence for large circuit simulations are open problems. The electromagnetic issues, thermal issues, and signal integrity challenges are yet to be adequately modeled for these devices.