Effects of heat dissipation on unipolar resistance switching in Pt/NiO/Pt capacitors

We fabricated Pt/NiO/Pt capacitor structures with various bottom electrode thicknesses, $t_{BE}$, and investigated their resistance switching behaviors. The capacitors with $t_{BE} \geq 50$ nm exhibited typical unipolar resistance memory switching, while those with $t_{BE} \leq 30$ nm showed threshold switching. This interesting phenomenon can be explained in terms of the temperature-dependent stability of conducting filaments. In particular, the thinner $t_{BE}$ makes dissipation of Joule heat less efficient, so the filaments will be at a higher temperature and become less stable. This study demonstrates the importance of heat dissipation in resistance random access memory.Comment: 14 pages, 3 figure

A Geometric Fractal Growth Model for Scale Free Networks

We introduce a deterministic model for scale-free networks, whose degree distribution follows a power-law with the exponent $\gamma$. At each time step, each vertex generates its offsprings, whose number is proportional to the degree of that vertex with proportionality constant m-1 (m>1). We consider the two cases: first, each offspring is connected to its parent vertex only, forming a tree structure, and secondly, it is connected to both its parent and grandparent vertices, forming a loop structure. We find that both models exhibit power-law behaviors in their degree distributions with the exponent $\gamma=1+\ln (2m-1)/\ln m$. Thus, by tuning m, the degree exponent can be adjusted in the range, $2 <\gamma < 3$. We also solve analytically a mean shortest-path distance d between two vertices for the tree structure, showing the small-world behavior, that is, $d\sim \ln N/\ln {\bar k}$, where N is system size, and $\bar k$ is the mean degree. Finally, we consider the case that the number of offsprings is the same for all vertices, and find that the degree distribution exhibits an exponential-decay behavior

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Deformable devices with integrated functional nanomaterials for wearable electronics

As the market and related industry for wearable electronics dramatically expands, there are continuous and strong demands for flexible and stretchable devices to be seamlessly integrated with soft and curvilinear human skin or clothes. However, the mechanical mismatch between the rigid conventional electronics and the soft human body causes many problems. Therefore, various prospective nanomaterials that possess a much lower flexural rigidity than their bulk counterparts have rapidly established themselves as promising electronic materials replacing rigid silicon and/or compound semiconductors in next-generation wearable devices. Many hybrid structures of multiple nanomaterials have been also developed to pursue both high performance and multifunctionality. Here, we provide an overview of state-of-the-art wearable devices based on one-or two-dimensional nanomaterials (e.g., carbon nanotubes, graphene, single-crystal silicon and oxide nanomembranes, organic nanomaterials and their hybrids) in combination with zero-dimensional functional nanomaterials (e.g., metal/oxide nanoparticles and quantum dots). Starting from an introduction of materials strategies, we describe device designs and the roles of individual ones in integrated systems. Detailed application examples of wearable sensors/actuators, memories, energy devices, and displays are also presented