An improved memristor model for brain-inspired computing

Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.

New challenge for bionics--brain-inspired computing

By definition, bionics is the application of biological mechanisms found in nature to artificial systems in order to achieve specific functional goals. Successful examples range from Velcro, the touch fastener inspired by the hooks of burrs, to self-cleaning material, inspired by the surface of the lotus leaf. Recently, a new trend in bionics i Brain-Inspired Computing （BIC） - has captured increasing attention. Instead of learning from burrs and leaves, BIC aims to understand the brain and then utilize its operating principles to achieve powerful and efficient information processing.

Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data

Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations.In this study,we aimed to develop an integrated method for predicting effluent COD and NH3 levels.We employed a 200 L pilot-scale sequencing batch reactor(SBR)to gather multimodal data from urban sewage over 40 d.Then we collected data on critical parameters like COD,DO,pH,NH_(3),EC,ORP,SS,and water temperature,alongside wastewater surface images,resulting in a data set of approximately 40246 points.Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network(BITF-CL)using this data.This innovative model synergized sewage imagery with water quality data,enhancing prediction accuracy.As a result,the BITF-CL model reduced prediction error by over 23%compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data.Consequently,this research presents a cost-effective and precise prediction system for sewage treatment,demonstrating the potential of brain-inspired models.

Research on General-Purpose Brain-Inspired Computing Systems

Brain-inspired computing is a new technology that draws on the principles of brain science and is oriented to the efficient development of artificial general intelligence(AGI),and a brain-inspired computing system is a hierarchical system composed of neuromorphic chips,basic software and hardware,and algorithms/applications that embody this tech-nology.While the system is developing rapidly,it faces various challenges and opportunities brought by interdisciplinary research,including the issue of software and hardware fragmentation.This paper analyzes the status quo of brain-inspired computing systems.Enlightened by some design principle and methodology of general-purpose computers,it is proposed to construct"general-purpose"brain-inspired computing systems.A general-purpose brain-inspired computing system refers to a brain-inspired computing hierarchy constructed based on the design philosophy of decoupling software and hardware,which can flexibly support various brain-inspired computing applications and neuromorphic chips with different architec-tures.Further,this paper introduces our recent work in these aspects,including the ANN(artificial neural network)/SNN(spiking neural network)development tools,the hardware agnostic compilation infrastructure,and the chip micro-archi-tecture with high flexibility of programming and high performance;these studies show that the"general-purpose"system can remarkably improve the efficiency of application development and enhance the productivity of basic software,thereby being conductive to accelerating the advancement of various brain-inspired algorithms and applications.We believe that this is the key to the collaborative research and development,and the evolution of applications,basic software and chips in this field,and conducive to building a favorable software/hardware ecosystem of brain-inspired computing.

Brain-inspired Intelligent Robotics:Theoretical Analysis and Systematic Application

Traditional joint-link robots have been widely used in production lines because of their high precision for single tasks.With the development of the manufacturing and service industries,the requirement for the comprehensive performance of robotics is growing.Numerous types of bio-inspired robotics have been investigated to realize human-like motion control and manipulation.A study route from inner mechanisms to external structures is proposed to imitate humans and animals better.With this idea,a brain-inspired intelligent robotic system is constructed that contains visual cognition,decision-making,motion control,and musculoskeletal structures.This paper reviews cutting-edge research in brain-inspired visual cognition,decision-making,motion control,and musculoskeletal systems.Two software systems and a corresponding hardware system are established,aiming at the verification and applications of next-generationbrain-inspired musculoskeletal robots.

Traffic Clustering Algorithm of Urban Data Brain Based on a Hybrid-Augmented Architecture of Quantum Annealing and Brain-Inspired Cognitive Computing

In recent years,the urbanization process has brought modernity while also causing key issues,such as traffic congestion and parking conflicts.Therefore,cities need a more intelligent"brain"to form more intelligent and efficient transportation systems.At present,as a type of machine learning,the traditional clustering algorithm still has limitations.K-means algorithm is widely used to solve traffic clustering problems,but it has limitations,such as sensitivity to initial points and poor robustness.Therefore,based on the hybrid architecture of Quantum Annealing(QA)and brain-inspired cognitive computing,this study proposes QA and Brain-Inspired Clustering Algorithm(QABICA)to solve the problem of urban taxi-stand locations.Based on the traffic trajectory data of Xi’an and Chengdu provided by Didi Chuxing,the clustering results of our algorithm and K-means algorithm are compared.We find that the average taxi-stand location bias of the final result based on QABICA is smaller than that based on K-means,and the bias of our algorithm can effectively reduce the tradition K-means bias by approximately 42%,up to approximately 83%,with higher robustness.QA algorithm is able to jump out of the local suboptimal solutions and approach the global optimum,and brain-inspired cognitive computing provides search feedback and direction.Thus,we will further consider applying our algorithm to analyze urban traffic flow,and solve traffic congestion and other key problems in intelligent transportation.

Towards a New Paradigm for Brain-inspired Computer Vision

Brain-inspired computer vision aims to learn from biological systems to develop advanced image processing techniques.However,its progress so far is not impressing.We recognize that a main obstacle comes from that the current paradigm for brain-inspired computer vision has not captured the fundamental nature of biological vision,i.e.,the biological vision is targeted for processing spatio-temporal patterns.Recently,a new paradigm for developing brain-inspired computer vision is emerging,which emphasizes on the spatio-temporal nature of visual signals and the brain-inspired models for processing this type of data.In this paper,we review some recent primary works towards this new paradigm,including the development of spike cameras which acquire spiking signals directly from visual scenes,and the development of computational models learned from neural systems that are specialized to process spatio-temporal patterns,including models for object detection,tracking,and recognition.We also discuss about the future directions to improve the paradigm.

Efficient Visual Recognition:A Survey on Recent Advances and Brain-inspired Methodologies

Visual recognition is currently one of the most important and active research areas in computer vision,pattern recognition,and even the general field of artificial intelligence.It has great fundamental importance and strong industrial needs,particularly the modern deep neural networks(DNNs)and some brain-inspired methodologies,have largely boosted the recognition performance on many concrete tasks,with the help of large amounts of training data and new powerful computation resources.Although recognition accuracy is usually the first concern for new progresses,efficiency is actually rather important and sometimes critical for both academic research and industrial applications.Moreover,insightful views on the opportunities and challenges of efficiency are also highly required for the entire community.While general surveys on the efficiency issue have been done from various perspectives,as far as we are aware,scarcely any of them focused on visual recognition systematically,and thus it is unclear which progresses are applicable to it and what else should be concerned.In this survey,we present the review of recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related and brain-inspired visual recognition approaches,including efficient network compression and dynamic brain-inspired networks.We investigate not only from the model but also from the data point of view(which is not the case in existing surveys)and focus on four typical data types(images,video,points,and events).This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.

Brain-inspired multimodal learning based on neural networks

Modern computational models have leveraged biological advances in human brain research. This study addresses the problem of multimodal learning with the help of brain-inspired models. Specifically, a unified multimodal learning architecture is proposed based on deep neural networks, which are inspired by the biology of the visual cortex of the human brain. This unified framework is validated by two practical multimodal learning tasks: image captioning, involving visual and natural language signals, and visual-haptic fusion, involving haptic and visual signals. Extensive experiments are conducted under the framework, and competitive results are achieved.

Towards“General Purpose”Brain-Inspired Computing System

Brain-inspired computing refers to computational models,methods,and systems,that are mainly inspired by the processing mode or structure of brain.A recent study proposed the concept of"neuromorphic completeness"and the corresponding system hierarchy,which is helpful to determine the capability boundary of brain-inspired computing system and to judge whether hardware and software of brain-inspired computing are compatible with each other.As a position paper,this article analyzes the existing brain-inspired chips design characteristics and the current so-called"general purpose"application development frameworks for brain-inspired computing,as well as introduces the background and the potential of this proposal.Further,some key features of this concept are presented through the comparison with the Turing completeness and approximate computation,and the analyses of the relationship with"general-purpose"brain-inspired computing systems(it means that computing systems can support all computable applications).In the end,a promising technical approach to realize such computing systems is introduced,as well as the on-going research and the work foundation.We believe that this work is conducive to the design of extensible neuromorphic complete hardware-primitives and the corresponding chips.On this basis,it is expected to gradually realize"general purpose"brain-inspired computing system,in order to take into account the functionality completeness and application efficiency.

Denoised Internal Models:A Brain-inspired Autoencoder Against Adversarial Attacks

Despite its great success,deep learning severely suffers from robustness;i.e.,deep neural networks are very vulnerable to adversarial attacks,even the simplest ones.Inspired by recent advances in brain science,we propose the denoised internal models(DIM),a novel generative autoencoder-based model to tackle this challenge.Simulating the pipeline in the human brain for visual signal processing,DIM adopts a two-stage approach.In the first stage,DIM uses a denoiser to reduce the noise and the dimensions of inputs,reflecting the information pre-processing in the thalamus.Inspired by the sparse coding of memory-related traces in the primary visual cortex,the second stage produces a set of internal models,one for each category.We evaluate DIM over 42 adversarial attacks,showing that DIM effectively defenses against all the attacks and outperforms the SOTA on the overall robustness on the MNIST(Modified National Institute of Standards and Technology)dataset.

健康和医学领域的人工智能:期许、伦理挑战和治理（英文）

Artificial intelligence (AI) is rapidly being applied to a wide range of fields,including medicine,and has been considered as an approach that may augment or substitute human professionals in primary healthcare.However,AI also raises several challenges and ethical concerns.In this article,the author investigates and discusses three aspects of AI in medicine and healthcare:the application and promises of AI,special ethical concerns pertaining to AI in some frontier fields,and suggestive ethical governance systems.Despite great potentials of frontier AI research and development in the field of medical care,the ethical challenges induced by its applications has put forward new requirements for governance.To ensure “trustworthy” AI applications in healthcare and medicine,the creation of an ethical global governance framework and system as well as special guidelines for frontier AI applications in medicine are suggested.The most important aspects include the roles of governments in ethical auditing and the responsibilities of stakeholders in the ethical governance system.

Fabrication and investigation of ferroelectric memristors with various synaptic plasticities

In the post-Moore era,neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks.Memristors have been proposed as a key part of neuromorphic computing architectures,and can be used to emulate the synaptic plasticities of the human brain.Ferroelectric memristors represent a breakthrough for memristive devices on account of their reliable nonvolatile storage,low write/read latency and tunable conductive states.However,among the reported ferroelectric memristors,the mechanisms of resistive switching are still under debate.In addition,there needs to be more research on emulation of the brain synapses using ferroelectric memristors.Herein,Cu/PbZr_(0.52)Ti_(0.48)O_(3)(PZT)/Pt ferroelectric memristors have been fabricated.The devices are able to realize the transformation from threshold switching behavior to resistive switching behavior.The synaptic plasticities,including excitatory post-synaptic current,paired-pulse facilitation,paired-pulse depression and spike time-dependent plasticity,have been mimicked by the PZT devices.Furthermore,the mechanisms of PZT devices have been investigated by first-principles calculations based on the interface barrier and conductive filament models.This work may contribute to the application of ferroelectric memristors in neuromorphic computing systems.

脑启发的人工智能综述:进展与应用

Recent advances in Artificial Intelligence(AI)have indicated that inspirations from the brain can effectively improve the level of intelligence for AI computational models,even if just local and partial inspirations.Nevertheless,realizing and exceeding intelligence at a human level still needs a deeper investigation and inspirations from the brain.The goal of brain-inspired intelligence is to achieve human intelligence inspired from brain neural mechanism and cognitive behavior mechanism.To this end,in this paper we introduce the relationship between AI and neuroscience,the current status of brain-inspired intelligence,the future work in intelligent control systems,and its profound influence in other fields.

Flexible 3D memristor array for binary storage and multi-states neuromorphic computing applications

The demand of flexible neuromorphic computing electronics is increasing with the rapid development of wearable artificial intelligent devices.The flexible resistive random-access memory(RRAM)is one excellent candidate of highdensity storage devices.However,due to the limitations of fabrication process,materials system and device structure,it is difficult to prepare flexible 3D highdensity network for neuromorphic computing.In this paper,a 3D flexible memristors network is developed via low-temperature atomic layer deposition(ALD)at 130C,with potential of extending to various flexible electronics.The typical bipolar switching characteristics are verified in RRAM units of 3D network,including first,second and third layers.Besides binary storage,the multibit storage in single unit is demonstrated and the storage density is further increased.As a connection link between binary storage and brain-inspired neuromorphic computing,the multibit storage capability paves the way for the tunable synaptic plasticity,for example,long-term potentiation/depression(LTP/LTD).The 3D memristors network successfully mimicked the typical neuromorphic functionality and realized ultra-multi conductance states modulation under 600 spikes.The robust mechanical flexibility is further demonstrated via LTP/LTD emulation under bending states(radius=10 mm).The 3D flexible memristors network shows significant potential of applications in high-performance,high-density and reliable wearable neuromorphic computing system.

Neural Decoding of Visual Information Across Different Neural Recording Modalities and Approaches

Vision plays a peculiar role in intelligence.Visual information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents.Recent advances have led to the development of brain-inspired algorithms and models for machine vision.One of the key components of these methods is the utilization of the computational principles underlying biological neurons.Additionally,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information.Thus,there is a high demand for mapping out functional models for reading out visual information from neural signals.Here,we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals,from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography(EEG)and functional magnetic resonance imaging recordings of brain signals.

Biologically inspired visual computing:the state of the art

Visual information is highly advantageous for the evolutionary success of almost all animals.This information is likewise critical for many computing tasks,and visual computing has achieved tremendous successes in numerous applications over the last 60 years or so.In that time,the development of visual computing has moved forwards with inspiration from biological mechanisms many times.In particular,deep neural networks were inspired by the hierarchical processing mechanisms that exist in the visual cortex of primate brains(including ours),and have achieved huge breakthroughs in many domainspecific visual tasks.In order to better understand biologically inspired visual computing,we will present a survey of the current work,and hope to offer some new avenues for rethinking visual computing and designing novel neural network architectures.

New Advanced Computing Architecture for Cryptography Design and Analysis by D-Wave Quantum Annealer

Universal quantum computers are far from achieving practical applications.The D-Wave quantum computer is initially designed for combinatorial optimizations.Therefore,exploring the potential applications of the D-Wave device in the field of cryptography is of great importance.First,although we optimize the general quantum Hamiltonian on the basis of the structure of the multiplication table(factor up to 1005973),this study attempts to explore the simplification of Hamiltonian derived from the binary structure of the integers to be factored.A simple factorization on 143 with four qubits is provided to verify the potential of further advancing the integer-factoring ability of the D-Wave device.Second,by using the quantum computing cryptography based on the D-Wave 2000 Q system,this research further constructs a simple version of quantum-classical computing architecture and a Quantum-Inspired Simulated Annealing(QISA)framework.Good functions and a high-performance platform are introduced,and additional balanced Boolean functions with high nonlinearity and optimal algebraic immunity can be found.Further comparison between QISA and Quantum Annealing(QA)on six-variable bent functions not only shows the potential speedup of QA,but also suggests the potential of architecture to be a scalable way of D-Wave annealer toward a practical cryptography design.

A Spatial Cognitive Model that Integrates the Effects of Endogenous and Exogenous Information on the Hippocampus and Striatum

Reproducing the spatial cognition of animals using computational models that make agents navigate autonomously has attracted much attention. Many biologically inspired models for spatial cognition focus mainly on the simulation of the hippocampus and only consider the effect of external environmental information(i.e., exogenous information) on the hippocampal coding. However, neurophysiological studies have shown that the striatum, which is closely related to the hippocampus, also plays an important role in spatial cognition and that information inside animals(i.e., endogenous information) also affects the encoding of the hippocampus. Inspired by the progress made in neurophysiological studies, we propose a new spatial cognitive model that consists of analogies between the hippocampus and striatum. This model takes into consideration how both exogenous and endogenous information affects coding by the environment. We carried out a series of navigation experiments that simulated a water maze and compared our model with other models. Our model is self-adaptable and robust and has better performance in navigation path length. We also discuss the possible reasons for the results and how our findings may help us understand real mechanisms in the spatial cognition of animals.