A compact spike-timing-dependent-plasticity circuit for floating gate weight implementation

AbstractSpike timing dependent plasticity (STDP) forms the basis of learning within neural networks. STDP allows for the modification of synaptic weights based upon the relative timing of pre- and post-synaptic spikes. A compact circuit is presented which can implement STDP, including the critical plasticity window, to determine synaptic modification. A physical model to predict the time window for plasticity to occur is formulated and the effects of process variations on the window is analyzed. The STDP circuit is implemented using two dedicated circuit blocks, one for potentiation and one for depression where each block consists of 4 transistors and a polysilicon capacitor. SpectreS simulations of the back-annotated layout of the circuit and experimental results indicate that STDP with biologically plausible critical timing windows over the range from 10µs to 100ms can be implemented. Also a floating gate weight storage capability, with drive circuits, is presented and a detailed analysis correlating weights changes with charging time is given

Decoupling of morphological disparity and taxic diversity during the adaptive radiation of anomodont therapsids

Adaptive radiations are central to macroevolutionary theory. Whether triggered by acquisition of new traits or ecological opportunities arising from mass extinctions, it is debated whether adaptive radiations are marked by initial expansion of taxic diversity or of morphological disparity (the range of anatomical form). If a group rediversifies following a mass extinction, it is said to have passed through a macroevolutionary bottleneck, and the loss of taxic or phylogenetic diversity may limit the amount of morphological novelty that it can subsequently generate. Anomodont therapsids, a diverse clade of Permian and Triassic herbivorous tetrapods, passed through a bottleneck during the end-Permian mass extinction. Their taxic diversity increased during the Permian, declined significantly at the Permo–Triassic boundary and rebounded during the Middle Triassic before the clade's final extinction at the end of the Triassic. By sharp contrast, disparity declined steadily during most of anomodont history. Our results highlight three main aspects of adaptive radiations: (i) diversity and disparity are generally decoupled; (ii) models of radiations following mass extinctions may differ from those triggered by other causes (e.g. trait acquisition); and (iii) the bottleneck caused by a mass extinction means that a clade can emerge lacking its original potential for generating morphological variety

Kronian Magnetospheric Reconnection Statistics Across Cassini's Lifetime

Magnetic reconnection is a fundamental physical process in planetary magnetospheres, in which plasma can be exchanged between the solar wind and a planetary magnetosphere, and material can be disconnected and ultimately lost from a magnetosphere. Magnetic reconnection in a planetary magnetotail can result in the release of plasmoids downtail and dipolarizations planetward of an x-line. The signatures of these products include characteristic deflections in the north-south component of the magnetic field which can be detected by in-situ spacecraft. These signatures have been identified by eye, semi-automated algorithms, and recently machine learning methods. Here, we apply statistical analysis to the most thorough catalogue of Kronian magnetospheric reconnection signatures created through machine learning methods to improve understanding of magnetospheric evolution. This research concludes that no quasi-steady position of the magnetotail x-line exists within 70 RS. This research introduces prediction equations to estimate the distribution of duration of plasmoid passage over the spacecraft (N = 300∆t −1.3 , bin width = 1 min) and north-south field deflection (N = 52∆B −2.1 θ , bin width = 0.25 nT) expected to be identified by an orbiting spacecraft across a year of observations. Furthermore, this research finds a local time asymmetry for reconnection identifications, with a preference for dusk-side over dawn-side. This may indicate a preference for Vasyliunas style reconnection over Dungey style for Saturn. Finally, through these distributions, the reconnection rate of Saturn’s magnetotail can be estimated as 3.22 reconnection events per day, with a resulting maximum mass loss from plasmoids of 44.87 kg s−1 on average, which is comparable with the magnetospheric mass loading from Enceladus (8-250 kg s−1 )

Probing Current Sheet Instabilities from Flare Ribbon Dynamics

The presence of current sheet instabilities, such as the tearing mode instability, are needed to account for the observed rate of energy release in solar flares. Insights into these current sheet dynamics can be revealed by the behavior of flare ribbon substructure, as magnetic reconnection accelerates particles down newly reconnected field lines into the chromosphere to mark the flare footpoints. Behavior in the ribbons can therefore be used to probe processes occurring in the current sheet. In this study, we use high-cadence (1.7 s) IRIS Slit Jaw Imager observations to probe for the growth and evolution of key spatial scales along the flare ribbons—resulting from dynamics across the current sheet of a small solar flare on 2016 December 6. Combining analyses of spatial scale growth with Si iv nonthermal velocities, we piece together a timeline of flare onset for this confined event, and provide evidence of the tearing mode instability triggering a cascade and inverse cascade toward a power spectrum consistent with plasma turbulence

Statistical Characterization of the Dynamic Near‐Earth Plasma Sheet Relative to Ultra‐Low Frequency (ULF) Wave Growth at Substorm Onset

Magnetospheric substorms are a complex phenomenon. During the initial stages of a substorm a variety of important processes occur in near-Earth space within a span of several minutes. The relative timing and links between these processes are critical to understanding how, where and when substorms may occur. One of the first observed signatures at substorm onset is the exponential increase in ULF (Ultra-Low Frequency) wave power in the near-Earth magnetotail (e.g., −7.5 ≤ XGSM ≤ −12.5 RE). We use the Time History of Events and Macroscale Interactions during Substorms spacecraft to examine the conditions in the magnetotail plasma sheet before, during and after local ULF wave growth. Prior to the ULF wave growth, the magnetotail stretches with convectional flows dominating. We then find strong earthward and azimuthal flows that peak at a similar time to the peak ULF wave power. These flows are found to be faster in the mid-tail (−10 ≤ XGSM ≤ −12.5 RE) than the near-tail (−7.5 ≤ XGSM ≤ −10 RE). Examining the local plasma energy density (magnetic, thermal and kinetic), we find no statistical decrease that could explain the exponentially growing ULF waves, in fact the local energy density is found to increase. This suggests that there may be an injection of energy from elsewhere in the magnetotail. Following the peak ULF wave power the tail is seen to dipolarize, and the local energy density is enhanced

Information Security as Strategic (In)effectivity

Security of information flow is commonly understood as preventing any information leakage, regardless of how grave or harmless consequences the leakage can have. In this work, we suggest that information security is not a goal in itself, but rather a means of preventing potential attackers from compromising the correct behavior of the system. To formalize this, we first show how two information flows can be compared by looking at the adversary's ability to harm the system. Then, we propose that the information flow in a system is effectively information-secure if it does not allow for more harm than its idealized variant based on the classical notion of noninterference

Lipidomic profiling in Crohn's disease: abnormalities in phosphatidylinositols, with preservation of ceramide, phosphatidylcholine and phosphatidylserine composition.

Crohn's disease is a chronic inflammatory condition largely affecting the terminal ileum and large bowel. A contributing cause is the failure of an adequate acute inflammatory response as a result of impaired secretion of pro-inflammatory cytokines by macrophages. This defective secretion arises from aberrant vesicle trafficking, misdirecting the cytokines to lysosomal degradation. Aberrant intestinal permeability is also well-established in Crohn's disease. Both the disordered vesicle trafficking and increased bowel permeability could result from abnormal lipid composition. We thus measured the sphingo- and phospholipid composition of macrophages, using mass spectrometry and stable isotope labelling approaches. Stimulation of macrophages with heat-killed Escherichia coli resulted in three main changes; a significant reduction in the amount of individual ceramide species, an altered composition of phosphatidylcholine, and an increased rate of phosphatidylcholine synthesis in macrophages. These changes were observed in macrophages from both healthy control individuals and patients with Crohn's disease. The only difference detected between control and Crohn's disease macrophages was a reduced proportion of newly-synthesised phosphatidylinositol 16:0/18:1 over a defined time period. Shotgun lipidomics analysis of macroscopically non-inflamed ileal biopsies showed a significant decrease in this same lipid species with overall preservation of sphingolipid, phospholipid and cholesterol composition

Using Regular Languages to Explore the Representational Capacity of Recurrent Neural Architectures

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. The degree of LDDs within the generated data can be controlled through the k parameter, length of the generated strings, and by choosing appropriate forbidden strings. In this paper, we explore the capacity of different RNN extensions to model LDDs, by evaluating these models on a sequence of SPk synthesized datasets, where each subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple languages, the presence of LDDs does have significant impact on the performance of recurrent neural architectures, thus making them prime candidate in benchmarking tasks.Comment: International Conference of Artificial Neural Networks (ICANN) 201