Flexible artificial vision computing system based on FeOx optomemristor for speech recognition

With the advancement of artificial intelligence,optic in-sensing reservoir computing based on emerging semiconductor devices is high desirable for real-time analog signal processing.Here,we disclose a flexible optomemristor based on C_(27)H_(30)O_(15)/FeOx heterostructure that presents a highly sensitive to the light stimuli and artificial optic synaptic features such as short-and long-term plasticity(STP and LTP),enabling the developed optomemristor to implement complex analogy signal processing through building a real-physical dynamic-based in-sensing reservoir computing algorithm and yielding an accuracy of 94.88%for speech recognition.The charge trapping and detrapping mediated by the optic active layer of C_(27)H_(30)O_(15) that is extracted from the lotus flower is response for the positive photoconductance memory in the prepared optomemristor.This work provides a feasible organic−inorganic heterostructure as well as an optic in-sensing vision computing for an advanced optic computing system in future complex signal processing.

A spintronic memristive circuit on the optimized RBF-MLP neural network

A radial basis function network(RBF)has excellent generalization ability and approximation accuracy when its parameters are set appropriately.However,when relying only on traditional methods,it is difficult to obtain optimal network parameters and construct a stable model as well.In view of this,a novel radial basis neural network(RBF-MLP)is proposed in this article.By connecting two networks to work cooperatively,the RBF’s parameters can be adjusted adaptively by the structure of the multi-layer perceptron(MLP)to realize the effect of the backpropagation updating error.Furthermore,a genetic algorithm is used to optimize the network’s hidden layer to confirm the optimal neurons(basis function)number automatically.In addition,a memristive circuit model is proposed to realize the neural network’s operation based on the characteristics of spin memristors.It is verified that the network can adaptively construct a network model with outstanding robustness and can stably achieve 98.33%accuracy in the processing of the Modified National Institute of Standards and Technology(MNIST)dataset classification task.The experimental results show that the method has considerable application value.

Memristor-based multi-synaptic spiking neuron circuit for spiking neural network

Spiking neural networks(SNNs) are widely used in many fields because they work closer to biological neurons.However,due to its computational complexity,many SNNs implementations are limited to computer programs.First,this paper proposes a multi-synaptic circuit(MSC) based on memristor,which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse signals.The synapse circuit participates in the calculation of the network while transmitting the pulse signal,and completes the complex calculations on the software with hardware.Secondly,a new spiking neuron circuit based on the leaky integrate-and-fire(LIF) model is designed in this paper.The amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as required.The combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron(MSSN).The MSSN was simulated in PSPICE and the expected result was obtained,which verified the feasibility of the circuit.Finally,a small SNN was designed based on the mathematical model of MSSN.After the SNN is trained and optimized,it obtains a good accuracy in the classification of the IRIS-dataset,which verifies the practicability of the design in the network.

THE EXPONENTIAL PROPERTY OF SOLUTIONS BOUNDED FROM BELOW TO DEGENERATE EQUATIONS IN UNBOUNDED DOMAINS

This paper is focused on studying the structure of solutions bounded from below to degenerate elliptic equations with Neumann and Dirichlet boundary conditions in unbounded domains.After establishing the weak maximum principles,the global boundary Holder estimates and the boundary Harnack inequalities of the equations,we show that all solutions bounded from below are linear combinations of two special solutions(exponential growth at one end and exponential decay at the other)with a bounded solution to the degenerate equations.

基于忆阻器-CMOS的通用逻辑电路及其应用

忆阻器是一种具有阻值开关特性的信息存储器件.由于忆阻器件具有可变电导性,其组合电路可应用与逻辑运算.本文提出了一种新的忆阻器-CMOS逻辑电路,能够在同一电路中同时实现ANDOR-XOR-XNOR 4种基本的逻辑操作.相较于MAD Gates, MRL, IMPLY逻辑电路,忆阻器数量和功耗均有大幅降低,电路性能更优,电路效率大幅提高.在此基础上设计了一种新的全加电路及二值图像加密电路.与现有忆阻器逻辑加法电路相比,本文设计的加法电路在元件数量上同样具有极大的优势.本文设计的二值图像加密电路能够用两种不同的加密方式实现图像加密,密钥与电路相互独立,提高了加密结果的可靠性.

GABAergic Interneurons are Required for Generation of Slow CA1 Oscillation in Rat Hippocampus

Neuronal oscillations are fundamental to hip- pocampal function. It has been shown that GABAergic interneurons make an important contribution to hippocampal oscillations, but the underlying mechanism is not well understood. Here, using whole-cell recording in the complete hippocampal formation isolated from rats at postnatal days 14-18, we showed that GABAA receptormediated activity enhanced the generation of slow CA1 oscillations. In vitro, slow oscillations （0.5-1.5 Hz） were generated in CA1 neurons, and they consisted primarily of excitatory rather than inhibitory membrane-potential changes. These oscillations were greatly reduced by blocking GABAA receptor-mediated activity with bicuculline and were enhanced by increasing such activity with midazolam, suggesting that interneurons are required for oscillation generation. Consistently, CA1 fast-spiking interneurons were found to generate action potentials usually preceding those in CA1 pyramidal cells. These findings indicate a GABAA receptor-based mechanism for the generation of the slow CA1 oscillation in the hippocampus.

Dependence of Generation of Hippocampal CAI Slow Oscillations on Electrical Synapses

Neuronal oscillations in the hippocampus are critical for many brain functions including learning and memory.The underlying mechanism of oscillation generation has been extensively investigated in terms of chemical synapses and ion channels.Recently,electrical synapses have also been indicated to play important roles,as reported in various brain areas in vivo and in brain slices.However,this issue remains to be further clarified,including in hippocampal networks.Here,using the completely isolated hippocampus,we investigated in vitro the effect of electrical synapses on slow CA1 oscillations(0.5 Hz-1.5 Hz)generated intrinsically by the hippocampus.We found that these oscillations were totally abolished by bath application of a general blocker of gap junctions(carbenoxolone)or a specific blocker of electrical synapses(mefloquine),as determined by whole-cell recordings in both CA1 pyramidal cells and fast-spiking cells.Our findings indicate that electrical synapses are required for the hippocampal generation of slow CA1 oscillations.

SARS-CoV-2 hijacks cellular kinase CDK2 to promote viral RNA synthesis

The coronavirus disease 2019(COVID-19)pandemic has devastated global health.Identifying key host factors essential for SARS-CoV-2 RNA replication is expected to unravel cellular targets for the development of broad-spectrum antiviral drugs which have been quested for the preparedness of future viral outbreaks.Here,we have identified host proteins that associate with nonstructural protein 12(nsp12),the RNA-dependent RNA polymerase(RdRp)of SARS-CoV-2 using a mass spectrometry(MS)-based proteomic approach.Among the candidate factors,CDK2(Cyclin-dependent kinase 2),a member of cyclin-dependent kinases,interacts with nsp12 and causes its phosphorylation at T20,thus facilitating the assembly of the RdRp complex consisting of nsp12,nsp7 and nsp8 and promoting efficient synthesis of viral RNA.The crucial role of CDK2 in viral RdRp function is further supported by our observation that CDK2 inhibitors potently impair viral RNA synthesis and SARS-CoV-2 infection.Taken together,we have discovered CDK2 as a key host factor of SARS-CoV-2 RdRp complex,thus serving a promising target for the development of SARS-CoV-2 RdRp inhibitors.

Excitatory Crossmodal Input to a Widespread Population of Primary Sensory Cortical Neurons

Crossmodal information processing in sensory cortices has been reported in sparsely distributed neurons under normal conditions and can undergo experience-or activity-induced plasticity.Given the potential role in brain function as indicated by previous reports,crossmodal connectivity in the sensory cortex needs to be further explored.Using perforated whole-cell recording in anesthetized adult rats,we found that almost all neurons recorded in the primary somatosensory,auditory,and visual cortices exhibited significant membrane-potential responses to crossmodal stimulation,as recorded when brain activity states were pharmacologically down-regulated in light anesthesia.These crossmodal cortical responses were excitatory and subthreshold,and further seemed to be relayed primarily by the sensory thalamus,but not the sensory cortex,of the stimulated modality.Our experiments indicate a sensory cortical presence of widespread excitatory crossmodal inputs,which might play roles in brain functions involving crossmodal information processing or plasticity.

Wrinkling modes of graphene oxide assembled on curved surfaces

Assembling two-dimensional(2D)sheets into macroscopic three-dimensional(3D)forms has created a promising material family with rich functionalities.Multiscale wrinkles are intrinsic features of 2D sheets in their 3D assembles.Therefore,the precise wrinkling modulation optimizes the transition of outstanding properties of 2D sheets to expected performances of assembled materials and dominates their fabrication process.The wrinkling evolution of 2D sheets assembling onto flat surfaces has been extensively understood,however,the wrinkling behaviors on the more generally curved surface still remain unclear.Here,we investigate the wrinkling behaviors of graphene oxide sheets assembled onto curved surfaces and reveal the selection rule of wrinkling modes that determined by the curvature mismatch between 2D sheets and target surfaces.We uncover that three wrinkling modes including isotropic cracked land,labyrinth,and anisotropic curtain phases,respectively emerge on flat,spherical,and cylindrical surfaces.A favorable description paradigm is offered to quantitatively measure the complex wrinkling patterns and assess the curvature mismatch constraint underlying the wrinkling mode selection.This research provides a general and quantitative description framework of wrinkling modulation of 2D materials such as high performance graphene fibers,and guides the precise fabrication of particles and functional coatings.