47 research outputs found

    Stochastic switching of TiO2 based memristive devices with identical initial memory states

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    In this work, we show that identical TiO2-based memristive devices that possess the same initial resistive states are only phenomenologically similar as their internal structures may vary significantly, which could render quite dissimilar switching dynamics. We experimentally demonstrated that the resistive switching of practical devices with similar initial states could occur at different programming stimuli cycles. We argue that similar memory states can be transcribed via numerous distinct active core states through the dissimilar reduced TiO2-x filamentary distributions. Our hypothesis was finally verified via simulated results of the memory state evolution, by taking into account dissimilar initial filamentary distribution

    Mechanical characterization of individual polycrystalline carbon tubes for use in electrical nano-interconnects

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    Polycrystalline carbon tubes were generated by CVD inside electrochemically prepared nano-porous anodic aluminium oxide membranes. This method produced nano-tubes without catalyst, featuring polycrystalline and a few layer thick walls. Individual tubes could be isolated and suspended on microfabricated substrates such that they formed single-side clamped beams. These beams were then used to investigate their mechanical properties employing electrostatic forces for bending the tubes beyond their mechanical stability where pull-in occurs, which could be detected by monitoring the current flowing from the tube to the substrate

    Reversible optical switching memristors with tunable STDP synaptic plasticity: a route to hierarchical control in artificial intelligent systems

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    Optical control of memristors opens the route to new applications in optoelectronic switching and neuromorphic computing. Motivated by the need for reversible and latched optical switching we report on the development of a memristor with electronic properties tunable and switchable by wavelength and polarization specific light. The device consists of an optically active azobenzene polymer, poly(disperse red 1 acrylate), overlaying a forest of vertically aligned ZnO nanorods. Illumination induces trans- cis isomerization of the azobenzene molecules, which expands or contracts the polymer layer and alters the resistance of the off/on states, their ratio and retention time. The reversible optical effect enables dynamic control of a memristors learning properties including control of synaptic potentiation and depression, optical switching between short -term and long-term memory and optical modulation of the synaptic efficacy via spike timing dependent plasticity. The work opens the route to the dynamic patterning of memristor networks both spatially and temporally by light, thus allowing the development of new optically reconfigurable neural networks and adaptive electronic circuits

    Image-guided focused ultrasound ablation of breast cancer: current status, challenges, and future directions

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    Image-guided focussed ultrasound (FUS) ablation is a non-invasive procedure that has been used for treatment of benign or malignant breast tumours. Image-guidance during ablation is achieved either by using real-time ultrasound (US) or magnetic resonance imaging (MRI). The past decade phase I studies have proven MRI-guided and US-guided FUS ablation of breast cancer to be technically feasible and safe. We provide an overview of studies assessing the efficacy of FUS for breast tumour ablation as measured by percentages of complete tumour necrosis. Successful ablation ranged from 20% to 100%, depending on FUS system type, imaging technique, ablation protocol, and patient selection. Specific issues related to FUS ablation of breast cancer, such as increased treatment time for larger tumours, size of ablation margins, methods used for margin assessment and residual tumour detection after FUS ablation, and impact of FUS ablation on sentinel node procedure are presented. Finally, potential future applications of FUS for breast cancer treatment such as FUS-induced anti-tumour immune response, FUS-mediated gene transfer, and enhanced drug delivery are discussed. Currently, breast-conserving surgery remains the gold standard for breast cancer treatment

    Metabolic changes in concussed American football players during the acute and chronic post-injury phases

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    <p>Abstract</p> <p>Background</p> <p>Despite negative neuroimaging findings many athletes display neurophysiological alterations and post-concussion symptoms that may be attributable to neurometabolic alterations.</p> <p>Methods</p> <p>The present study investigated the effects of sports concussion on brain metabolism using <sup>1</sup>H-MR Spectroscopy by comparing a group of 10 non-concussed athletes with a group of 10 concussed athletes of the same age (mean: 22.5 years) and education (mean: 16 years) within both the acute and chronic post-injury phases. All athletes were scanned 1-6 days post-concussion and again 6-months later in a 3T Siemens MRI.</p> <p>Results</p> <p>Concussed athletes demonstrated neurometabolic impairment in prefrontal and motor (M1) cortices in the acute phase where NAA:Cr levels remained depressed relative to controls. There was some recovery observed in the chronic phase where Glu:Cr levels returned to those of control athletes; however, there was a pathological increase of m-I:Cr levels in M1 that was only present in the chronic phase.</p> <p>Conclusions</p> <p>These results confirm cortical neurometabolic changes in the acute post-concussion phase as well as recovery and continued metabolic abnormalities in the chronic phase. The results indicate that complex pathophysiological processes differ depending on the post-injury phase and the neurometabolite in question.</p

    Implementation of a spike-based perceptron learning rule using TiO<sub>2-x </sub>memristors

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    Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic “cognitive” capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO2-x memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP) rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episod
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