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 organization of behavior: A neuropsychological theory

Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.

Memristive devices for computing

Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 10(14) synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks based on circuits combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one or several crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits, including first demonstrations of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks. Very recently, such experiments have been extended to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors, whose nonlinear current-voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.

pdf/training-and-operation-of-an-integrated-neuromorphic-network-2fddhn6pof.pdf

Training and operation of an integrated neuromorphic network based on metal-oxide memristors

In this paper, recent progress of binary metal-oxide resistive switching random access memory (RRAM) is reviewed. The physical mechanism, material properties, and electrical characteristics of a variety of binary metal-oxide RRAM are discussed, with a focus on the use of RRAM for nonvolatile memory application. A review of recent development of large-scale RRAM arrays is given. Issues such as uniformity, endurance, retention, multibit operation, and scaling trends are discussed.

Metal–Oxide RRAM

A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal−oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.

/pdf/nanoscale-memristor-device-as-synapse-in-neuromorphic-1sl2njhdyu.pdf

Nanoscale Memristor Device as Synapse in Neuromorphic Systems

Nanoscale resistive switching devices, sometimes termed memristors, have recently generated significant interest for memory, logic and neuromorphic applications. Resistive switching effects in dielectric-based devices are normally assumed to be caused by conducting filament formation across the electrodes, but the nature of the filaments and their growth dynamics remain controversial. Here we report direct transmission electron microscopy imaging, and structural and compositional analysis of the nanoscale conducting filaments. Through systematic ex-situ and in-situ transmission electron microscopy studies on devices under different programming conditions, we found that the filament growth can be dominated by cation transport in the dielectric film. Unexpectedly, two different growth modes were observed for the first time in materials with different microstructures. Regardless of the growth direction, the narrowest region of the filament was found to be near the dielectric/inert-electrode interface in these devices, suggesting that this region deserves particular attention for continued device optimization.

/pdf/observation-of-conducting-filament-growth-in-nanoscale-sobo0w0qmm.pdf

Observation of conducting filament growth in nanoscale resistive memories

“Memory” is an essential building block in learning and decision-making in biological systems. Unlike modern semiconductor memory devices, needless to say, human memory is by no means eternal. Yet, forgetfulness is not always a disadvantage since it releases memory storage for more important or more frequently accessed pieces of information and is thought to be necessary for individuals to adapt to new environments. Eventually, only memories that are of significance are transformed from short-term memory into long-term memory through repeated stimulation. In this study, we show experimentally that the retention loss in a nanoscale memristor device bears striking resemblance to memory loss in biological systems. By stimulating the memristor with repeated voltage pulses, we observe an effect analogous to memory transition in biological systems with much improved retention time accompanied by additional structural changes in the memristor. We verify that not only the shape or the total number of stimuli is i...

Short-term memory to long-term memory transition in a nanoscale memristor.

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

We report studies on nanoscale Si-based memristive devices for memory and neuromorphic applications. The devices are based on ion motion inside an insulating a-Si matrix. Digital devices show excellent performance metrics including scalability, speed, ON/OFF ratio, endurance and retention. High density non-volatile memory arrays based on a crossbar structure have been fabricated and tested. Devices inside a 1kb array can be individually addressed with excellent reproducibility and reliability. By adjusting the device and material structures, nanoscale analog memristor devices have also been demonstrated. The analog memristor devices exhibit incremental conductance changes that are controlled by the charge flown through the device. The performances of the digital and analog devices are thought to be determined by the formation of a dominant conducting filament and the continuous motion of a uniform conduction front, respectively.

Si Memristive devices applied to memory and neuromorphic circuits

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