Model Of Switching Mechanism Research Articles

Coexistence of volatile and non-volatile resistive switching in Ni/SiO2/Pt memristor device controlled from different current compliances

We fabricated a Ni/SiO2/Pt resistive random-access memory device referred to as a memristor, which demonstrates both volatile and non-volatile switching. A reversible transition between volatile and non-volatile switching is achieved by applying different current compliances (CCs). Volatile switching was observed when the CC is 50 nA, whereas volatile switching changed to non-volatile switching when the CC is 100 nA. The switching mechanism model can be explained by the formation and rupture of conducting filaments by electrochemical metallization effects, which are vital in resistive switching. We demonstrate a simple pulse measurement method for both switching behaviors. In a single Ni/SiO2/Pt memristor device, short-term memory for volatile switching and a long-term memory synaptic function for non-volatile switching is observed under repeated stimuli. Owing to the distinctive characteristics of the Ni/SiO2/Pt device, the short-term synaptic function, i.e. paired-pulse facilitation and depression for volatile switching, is successfully mimicked by the memristor. These electrical results indicate that the Ni/SiO2/Pt memristor device with synaptic behavior has potential for use in brain-inspired computing systems.

Stochastic simulation algorithm for gene regulatory networks with multiple binding sites.

Promoters with multiple binding sites present a regulatory mechanism of several natural biological systems. It has been shown that such systems reflect a higher stability in comparison to the systems with small numbers of binding sites. Regulatory mechanisms with multiple binding sites are therefore used more frequently in artificially designed biological systems in recent years. While the number of possible promoter states increases exponentially with the number of binding sites, it is extremely hard to model such systems accurately. Here we present an adaptation of stochastic simulation algorithm for accurate modeling of gene regulatory networks with multiple binding sites. Small computational complexity of adapted algorithm allows us to model any feasible number of binding sites per promoter. The approach introduced in this work is demonstrated on the model of switching mechanism in Epstein-Barr virus, where 20 binding sites are observed on one of the promoters. We show that the presented approach is easy to adapt to any biological systems based on the regulatory mechanisms with multiple binding sites in order to obtain and analyze their behavior.