Recent theoretical studies have shown that probabilistic spiking can be interpreted as
learning and inference in cortical microcircuits. This interpretation creates new opportunities …

This paper investigates the issue of sampled-data stabilization for Takagi-Sugeno fuzzy
memristive neural networks (FMNNs) with time-varying delay. First, the concerned FMNNs …

In this paper, stabilization for a class of Takagi-Sugeno (TS) fuzzy memristive neural
networks (FMNNs) with mixed time delays is investigated. By virtue of theories of differential …

In this work, we propose a physical memristor/resistive switching device SPICE compact
model, that is able to accurately fit both unipolar/bipolar devices settling to its current-voltage …

This paper presents a memristive device model capable of accurately matching a wide
range of characterization data collected from a tantalum oxide memristor. Memristor models …

The paper deals with the modeling of memristors operating in extremely large memristive
networks such as crossbar structures for memory and computational circuits, memristor …

Memristor, a nanoscale device with the advantages of simple structure, excellent scalability,
and complementary metal–oxide–semiconductor (CMOS) process compatibility, has drawn …

The paper introduces a set of models of memristive devices for a reliable, accurate and fast
analysis of large networks in the SPICE (Simulation Program with Integrated Circuit …

The practical realization of neuro-memristive systems requires highly accurate simulation
models, robust devices and validations on device characteristics. This chapter covers the …

In this paper, the application of the theory of Lissajous figures to the creation of pinched
hysteresis loops, considered to be a characteristic of memristive systems, is demonstrated …