Stochastic resonance in 2D materials based memristors

Stochastic resonance is an essential phenomenon in neurobiology, it is connected to the constructive role of noise in the signals that take place in neuronal tissues, facilitating information communication, memory, etc. Memristive devices are known to be the cornerstone of hardware neuromorphic applications since they correctly mimic biological synapses in many different facets, such as short/long-term plasticity, spike-timing-dependent plasticity, pair-pulse facilitation, etc. Different types of neural networks can be built with circuit architectures based on memristive devices (mostly spiking neural networks and artificial neural networks). In this context, stochastic resonance is a critical issue to analyze in the memristive devices that will allow the fabrication of neuromorphic circuits. We do so here with h-BN based memristive devices from different perspectives. It is found that the devices we have fabricated and measured clearly show stochastic resonance behaviour. Consequently, neuromorphic applications can be developed to account for this effect, that describes a key issue in neurobiology with strong computational implications

TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2023. 1271956/full#supplementary-materialFunding The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The authors thank the support of the Consejeria de Conocimiento, Investigacion y Universidad, Junta de Andalucia (Spain), and the FEDER program through project B-TIC-624-UGR20. They also thank the support of the Federal Ministry of Education and Research of Germany under Grant 16ME0092.We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature. It is shown that this effect is important and greatly depends on the noise statistical characteristics.Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain), and the FEDER program through project B-TIC-624-UGR20Federal Ministry of Education and Research of Germany under Grant 16ME009

A thorough investigation of the switching dynamics of TiN/Ti/10 nm-HfO2/W resistive memories

The switching dynamics of TiN/Ti/HfO2/W-based resistive memories is investigated. The analysis consisted in the systematic application of voltage sweeps with different ramp rates and temperatures. The obtained results give clear insight into the role played by transient and thermal effects on the device operation. Both kinetic Monte Carlo simulations and a compact modeling approach based on the Dynamic Memdiode Model are considered in this work with the aim of assessing, in terms of their respective scopes, the nature of the physical processes that characterize the formation and rupture of the filamentary conducting channel spanning the oxide film. As a result of this study, a better understanding of the different facets of the resistive switching dynamics is achieved. It is shown that the temperature and, mainly, the applied electric field, control the switching mechanism of our devices. The Dynamic Memdiode Model, being a behavioral analytic approach, is shown to be particularly suitable for reproducing the conduction characteristics of our devices using a single set of parameters for the different operation regimesFEDER program [PID2022-139586NB-C41, PID2022- 139586NB-C42PID2022-139586NB-C43PID2022-139586NB-C44]The Consejería de Conocimiento, Investigaci´on y UniversidadJunta de Andalucía (Spain) [B-TIC-624-UGR20]Spanish Consejo Superior de Investigaciones Científicas (CSIC) [20225AT012]FEDER fundsRamón y Cajal grant number RYC2020-030150-IEuropean project MEMQuD, code 20FUN06EMPIR programme co-financed by the Participating StatesEuropean Union’s Horizon 2020 research and innovation programm

Influence of physiological variables and comorbidities on plasma Aβ40, Aβ42, and p-tau181 levels in cognitively unimpaired individuals

Plasma biomarkers for Alzheimer's disease (AD) are a promising tool that may help in early diagnosis. However, their levels may be influenced by physiological parameters and comorbidities that should be considered before they can be used at the population level. For this purpose, we assessed the influences of different comorbidities on AD plasma markers in 208 cognitively unimpaired subjects. We analyzed both plasma and cerebrospinal fluid levels of Aβ40, Aβ42, and p-tau181 using the fully automated Lumipulse platform. The relationships between the different plasma markers and physiological variables were studied using linear regression models. The mean differences in plasma markers according to comorbidity groups were also studied. The glomerular filtration rate showed an influence on plasma Aβ40 and Aβ42 levels but not on the Aβ42/Aβ40 ratio. The amyloid ratio was significantly lower in diabetic and hypertensive subjects, and the mean p-tau181 levels were higher in hypertensive subjects. The glomerular filtration rate may have an inverse relationship on plasma Aβ40 and Aβ42 levels but not on the amyloid ratio, suggesting that the latter is a more stable marker to use in the general population. Cardiovascular risk factors might have a long-term effect on the amyloid ratio and plasma levels of p-tau181.Funding: This research received no external funding. Acknowledgments: This study was made possible thanks to donations from Germán González, Unidos por un Reto, Trail Nocturno de Cicero, La Trasmerana, and Primer Memorial Ángel Negrete. We would like to thank the participants of the Valdecilla Cohort for their selfless help and collaboration with research on neurodegenerative diseases. We would like to particularly acknowledge the patients and the Biobank Valdecilla (PT20/00067) in the Spanish Biobank Network for their collaboration

TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance

We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature. It is shown that this effect is important and greatly depends on the noise statistical characteristics

Clinical and Ecological Impact of an Educational Program to Optimize Antibiotic Treatments in Nursing Homes (PROA-SENIOR): A Cluster, Randomized, Controlled Trial and Interrupted Time-Series Analysis

[Background] Antimicrobial stewardship programs (ASPs) are recommended in nursing homes (NHs), although data are limited. We aimed to determine the clinical and ecological impact of an ASP for NHs.[Methods] We performed a cluster, randomized, controlled trial and a before–after study with interrupted time-series analyses in 14 NHs for 30 consecutive months from July 2018 to December 2020 in Andalusia, Spain. Seven facilities implemented an ASP with a bundle of 5 educational measures (general ASP) and 7 added 1-to-1 educational interviews (experimental ASP). The primary outcome was the overall use of antimicrobials, calculated monthly as defined daily doses (DDD) per 1000 resident days (DRD).[Results] The total mean antimicrobial consumption decreased by 31.2% (−16.72 DRD; P = .045) with respect to the preintervention period; the overall use of quinolones and amoxicillin–clavulanic acid dropped by 52.2% (P = .001) and 42.5% (P = .006), respectively; and the overall prevalence of multidrug-resistant organisms (MDROs) decreased from 24.7% to 17.4% (P = .012). During the intervention period, 12.5 educational interviews per doctor were performed in the experimental ASP group; no differences were found in the total mean antimicrobial use between groups (−14.62 DRD; P = .25). Two unexpected coronavirus disease 2019 waves affected the centers increasing the overall mean use of antimicrobials by 40% (51.56 DRD; P < .0001).[Conclusions] This study suggests that an ASP for NHs appears to be associated with a decrease in total consumption of antimicrobials and prevalence of MDROs. This trial did not find benefits associated with educational interviews, probably due to the coronavirus disease 2019 pandemic.[Clinical Trials Registration] NCT03543605.Peer reviewe

An Analysis on the Architecture and the Size of Quantized Hardware Neural Networks Based on Memristors

The authors acknowledge financial support by the German Research Foundation (DFG) under Project 434434223-SFB1461, by the Federal Ministry of Education and Research of Germany under Grant 16ME0092, Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain) and European Regional Development Fund (ERDF) under projects A-TIC-117-UGR18, B-TIC- 624-UGR20 and IE2017-5414, as well as the Spanish Ministry of Science, Innovation and Universities and ERDF fund under projects RTI2018-098983-B-I00 and TEC2017-84321-C4-3-R.We have performed different simulation experiments in relation to hardware neural networks (NN) to analyze the role of the number of synapses for different NN architectures in the network accuracy, considering different datasets. A technology that stands upon 4-kbit 1T1R ReRAM arrays, where resistive switching devices based on HfO2 dielectrics are employed, is taken as a reference. In our study, fully dense (FdNN) and convolutional neural networks (CNN) were considered, where the NN size in terms of the number of synapses and of hidden layer neurons were varied. CNNs work better when the number of synapses to be used is limited. If quantized synaptic weights are included, we observed thatNNaccuracy decreases significantly as the number of synapses is reduced; in this respect, a trade-off between the number of synapses and the NN accuracy has to be achieved. Consequently, the CNN architecture must be carefully designed; in particular, it was noticed that different datasets need specific architectures according to their complexity to achieve good results. It was shown that due to the number of variables that can be changed in the optimization of a NN hardware implementation, a specific solution has to be worked in each case in terms of synaptic weight levels, NN architecture, etc.German Research Foundation (DFG) under Project 434434223-SFB1461Federal Ministry of Education and Research of Germany under Grant 16ME0092Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain) and European Regional Development Fund (ERDF) under projects A-TIC-117-UGR18, B-TIC- 624-UGR20 and IE2017-5414Spanish Ministry of Science, Innovation and Universities and ERDF fund under projects RTI2018-098983-B-I00 and TEC2017-84321-C4-3-

3D simulation of conductive nanofilaments in multilayer h-BN memristors via a circuit breaker approach

Project PID2022-139586NB-44 funded by MCIN/AEI/10.13039/501100011033European Union NextGenerationEU/PRTRProject PP2022.PP-13 funded by “Ayudas del Plan Propio UGR 2022”King Abdullah University of Science and Technolog