Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks.

The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights.

Currently recommended skin scores correlate highly in the assessment of patients with Juvenile Dermatomyositis (JDM).

Background: Juvenile Dermatomyositis (JDM) is a rare, chronic, and life-threatening childhood autoimmune disease. Currently, there are recommended, reliable and validated measurement tools for assessment of skin disease activity in JDM including the Disease Activity Score (skinDAS), Cutaneous Assessment Tool (CAT), and the Cutaneous Dermatomyositis Disease Area and Severity Index (CDASI). The Physician's global assessment skin visual analog scale (Skin VAS) is also widely used for skin activity in JDM.

Author Correction: Dynamical nonlinear memory capacitance in biomimetic membranes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Molecular evolution across developmental time reveals rapid divergence in early embryogenesis.

Ontogenetic development hinges on the changes in gene expression in time and space within an organism, suggesting that the demands of ontogenetic growth can impose or reveal predictable pattern in the molecular evolution of genes expressed dynamically across development. Here, we characterize coexpression modules of the transcriptome, using a time series of 30 points from early embryo to adult. By capturing the functional form of expression profiles with quantitative metrics, we find fastest evolution in the distinctive set of genes with transcript abundance that declines through development from a peak in young embryos.

Dynamical nonlinear memory capacitance in biomimetic membranes.

Two-terminal memory elements, or memelements, capable of co-locating signal processing and memory via history-dependent reconfigurability at the nanoscale are vital for next-generation computing materials striving to match the brain's efficiency and flexible cognitive capabilities. While memory resistors, or memristors, have been widely reported, other types of memelements remain underexplored or undiscovered. Here we report the first example of a volatile, voltage-controlled memcapacitor in which capacitive memory arises from reversible and hysteretic geometrical changes in a lipid bilayer that mimics the composition and structure of biomembranes.

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes.

The ability to recreate synaptic functionalities in synthetic circuit elements is essential for neuromorphic computing systems that seek to emulate the cognitive powers of the brain with comparable efficiency and density. To date, silicon-based three-terminal transistors and two-terminal memristors have been widely used in neuromorphic circuits, in large part due to their ability to co-locate information processing and memory. Yet these devices cannot achieve the interconnectivity and complexity of the brain because they are power-hungry, fail to mimic key synaptic functionalities, and suffer from high noise and high switching voltages.

Adiabatic Quantum Computation Applied to Deep Learning Networks.

Training deep learning networks is a difficult task due to computational complexity, and this is traditionally handled by simplifying network topology to enable parallel computation on graphical processing units (GPUs). However, the emergence of quantum devices allows reconsideration of complex topologies. We illustrate a particular network topology that can be trained to classify MNIST data (an image dataset of handwritten digits) and neutrino detection data using a restricted form of adiabatic quantum computation known as quantum annealing performed by a D-Wave processor.

Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics.

Solid-state neuromorphic systems based on transistors or memristors have yet to achieve the interconnectivity, performance, and energy efficiency of the brain due to excessive noise, undesirable material properties, and nonbiological switching mechanisms. Here we demonstrate that an alamethicin-doped, synthetic biomembrane exhibits memristive behavior, emulates key synaptic functions including paired-pulse facilitation and depression, and enables learning and computing. Unlike state-of-the-art devices, our two-terminal, biomolecular memristor features similar structure (biomembrane), switching mechanism (ion channels), and ionic transport modality as biological synapses while operating at considerably lower power.

Memristor crossbar-based neuromorphic computing system: a case study.

By mimicking the highly parallel biological systems, neuromorphic hardware provides the capability of information processing within a compact and energy-efficient platform. However, traditional Von Neumann architecture and the limited signal connections have severely constrained the scalability and performance of such hardware implementations. Recently, many research efforts have been investigated in utilizing the latest discovered memristors in neuromorphic systems due to the similarity of memristors to biological synapses.

Practice question.

The term 'personalisation' has become increasingly common in the context of a movement that recognises the importance of people's individuality and their right to exercise choice in their daily lives. In the fields of health and social care, personalisation focuses on placing the individual at the centre of their care and an understanding that they know best what their needs are and how to meet them ( Carr 2008 ). Personalisation can be as significant as a direct payment or individual budget, or as small as supporting a resident in a care home to decide for themselves when they should go to bed.