抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The accumulation and extrusion of Ca2+ in the pre- and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. To emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca2+ dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization, closely resembling synaptic influx and extrusion of Ca2+, respectively. The diffusive memristor and its dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses, representing an advance in hardware implementation of neuromorphic functionalities.

/pdf/memristors-with-diffusive-dynamics-as-synaptic-emulators-for-4j06bmhu3f.pdf

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

A memristor is a resistive device with an inherent memory. The theoretical concept of a memristor was connected to physically measured devices in 2008 and since then there has been rapid progress in the development of such devices, leading to a series of recent demonstrations of memristor-based neuromorphic hardware systems. Here, we evaluate the state of the art in memristor-based electronics and explore where the future of the field lies. We highlight three areas of potential technological impact: on-chip memory and storage, biologically inspired computing and general-purpose in-memory computing. We analyse the challenges, and possible solutions, associated with scaling the systems up for practical applications, and consider the benefits of scaling the devices down in terms of geometry and also in terms of obtaining fundamental control of the atomic-level dynamics. Finally, we discuss the ways we believe biology will continue to provide guiding principles for device innovation and system optimization in the field. This Perspective evaluates the state of the art in memristor-based electronics and explores the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing.

The future of electronics based on memristive systems

Modern computers are based on the von Neumann architecture in which computation and storage are physically separated: data are fetched from the memory unit, shuttled to the processing unit (where computation takes place) and then shuttled back to the memory unit to be stored. The rate at which data can be transferred between the processing unit and the memory unit represents a fundamental limitation of modern computers, known as the memory wall. In-memory computing is an approach that attempts to address this issue by designing systems that compute within the memory, thus eliminating the energy-intensive and time-consuming data movement that plagues current designs. Here we review the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, their resistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation. We examine the different digital, analogue, and stochastic computing schemes that have been proposed, and explore the microscopic physical mechanisms involved. Finally, we discuss the challenges in-memory computing faces, including the required scaling characteristics, in delivering next-generation computing. This Review Article examines the development of in-memory computing using resistive switching devices.

/pdf/in-memory-computing-with-resistive-switching-devices-1mv368csjy.pdf

In-memory computing with resistive switching devices

This review article attempts to provide a comprehensive review of the recent progress in the so-called resistive random access memories (RRAMs) First, a brief introduction is presented to describe the construction and development of RRAMs, their potential for broad applications in the fields of nonvolatile memory, unconventional computing and logic devices, and the focus of research concerning RRAMs over the past decade Second, both inorganic and organic materials used in RRAMs are summarized, and their respective advantages and shortcomings are discussed Third, the important switching mechanisms are discussed in depth and are classified into ion migration, charge trapping/de-trapping, thermochemical reaction, exclusive mechanisms in inorganics, and exclusive mechanisms in organics Fourth, attention is given to the application of RRAMs for data storage, including their current performance, methods for performance enhancement, sneak-path issue and possible solutions, and demonstrations of 2-D and 3-D crossbar arrays Fifth, prospective applications of RRAMs in unconventional computing, as well as logic devices and multi-functionalization of RRAMs, are comprehensively summarized and thoroughly discussed The present review article ends with a short discussion concerning the challenges and future prospects of the RRAMs

Recent progress in resistive random access memories: Materials, switching mechanisms, and performance

Sparse representation of information performs powerful feature extraction on high-dimensional data and is of interest for applications in signal processing, machine vision, object recognition, and neurobiology Sparse coding is a mechanism by which biological neural systems can efficiently process complex sensory data while consuming very little power Sparse coding algorithms in a bio-inspired approach can be implemented in a crossbar array of memristors (resistive memory devices) This network enables efficient implementation of pattern matching and lateral neuron inhibition, allowing input data to be sparsely encoded using neuron activities and stored dictionary elements The reconstructed input can be obtained by performing a backward pass through the same crossbar matrix using the neuron activity vector as input Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals Using the sparse coding algorithm, natural image processing is performed based on a learned dictionary

Sparse coding with Memristor networks

Memristors have been extensively studied for data storage and low-power computation applications. In this study, we show that memristors offer more than simple resistance change. Specifically, the dynamic evolutions of internal state variables allow an oxide-based memristor to exhibit Ca2+-like dynamics that natively encode timing information and regulate synaptic weights. Such a device can be modeled as a second-order memristor and allow the implementation of critical synaptic functions realistically using simple spike forms based solely on spike activity.

Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity.

Nanoscale memristive devices using tungsten oxide as the switching layer have been fabricated and characterized. The devices show the characteristics of a flux-controlled memristor such that the conductance change is governed by the history of the applied voltage signals, leading to synaptic behaviors including long-term potentiation and depression. The memristive behavior is attributed to the migration of oxygen vacancies upon bias which modulates the interplay between Schottky barrier emission and tunneling at the WOX/electrode interface. A physical model incorporating ion drift and diffusion effects using an internal state variable representing the area of the conductive region has been proposed to explain the observed memristive behaviors. A SPICE model has been further developed that can be directly incorporated into existing circuit simulators. This type of device can be fabricated with low-temperature processes and has potential applications in synaptic computations and as analog circuit components.

Synaptic behaviors and modeling of a metal oxide memristive device

Memristors have attracted broad interest as a promising candidate for future memory and computing applications. Particularly, it is believed that memristors can effectively implement synaptic functions and enable efficient neuromorphic systems. Most previous studies, however, focus on implementing specific synaptic learning rules by carefully engineering external programming parameters instead of focusing on emulating the internal cause that leads to the apparent learning rules. Here, it is shown that by taking advantage of the different time scales of internal oxygen vacancy (VO) dynamics in an oxide-based memristor, diverse synaptic functions at different time scales can be implemented naturally. Mathematically, the device can be effectively modeled as a second-order memristor with a simple set of equations including multiple state variables. Not only is this approach more biorealistic and easier to implement, by focusing on the fundamental driving mechanisms it allows the development of complete theoretical and experimental frameworks for biologically inspired computing systems.

Biorealistic Implementation of Synaptic Functions with Oxide Memristors through Internal Ionic Dynamics

Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems. However a major challenge is to address the potentially large variations in space and time in these nanoscale devices. Here we show that in metal-filament based memristive devices the switching can be fully stochastic. While individual switching events are random, the distribution and probability of switching can be well predicted and controlled. Rather than trying to force high switching probabilities using excess voltage or time, the inherent stochastic nature of resistive switching allows these binary devices to be used as building blocks for novel error-tolerant computing schemes such as stochastic computing and provides the needed “analog” feature for neuromorphic applications. To verify such potential, we demonstrated memristor-based stochastic bitstreams in both time and space domains, and show that an array of binary memristors can act as a multi-level “analog” device for neuromorphic applications.

Patrick Sheridan

Papers

Sparse coding with Memristor networks

Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity.

Synaptic behaviors and modeling of a metal oxide memristive device

Biorealistic Implementation of Synaptic Functions with Oxide Memristors through Internal Ionic Dynamics

Stochastic memristive devices for computing and neuromorphic applications