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Visual commonsense reasoning with directional visual connections Research Articles

Yahong Han, Aming Wu, Linchao Zhu, Yi Yang,yahong@tju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.2000722

Abstract: To boost research into cognition-level visual understanding, i.e., making an accurate inference based on a thorough understanding of visual details, (VCR) has been proposed. Compared with traditional visual question answering which requires models to select correct answers, VCR requires models to select not only the correct answers, but also the correct rationales. Recent research into human cognition has indicated that brain function or cognition can be considered as a global and dynamic integration of local neuron connectivity, which is helpful in solving specific cognition tasks. Inspired by this idea, we propose a to achieve VCR by dynamically reorganizing the that is contextualized using the meaning of questions and answers and leveraging the directional information to enhance the reasoning ability. Specifically, we first develop a GraphVLAD module to capture to fully model visual content correlations. Then, a contextualization process is proposed to fuse sentence representations with visual neuron representations. Finally, based on the output of , we propose to infer answers and rationales, which includes a ReasonVLAD module. Experimental results on the VCR dataset and visualization analysis demonstrate the effectiveness of our method.

Keywords： 视觉常识推理；有向连接网络；视觉神经元连接；情景化连接；有向连接    

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A new photosensitive neuron model and its dynamics Research Articles

Yong Liu, Wan-jiang Xu, Jun Ma, Faris Alzahrani, Aatef Hobiny,hyperchaos@163.com,hyperchaos@lut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 9,   Pages 1387-1396 doi: 10.1631/FITEE.1900606

Abstract: Biological neurons can receive inputs and capture a variety of external stimuli, which can be encoded and transmitted as different electric signals. Thus, the membrane potential is adjusted to activate the appropriate firing modes. Indeed, reliable s should take intrinsic biophysical effects and functional encoding into consideration. One fascinating and important question is the physical mechanism for the transcription of external signals. External signals can be transmitted as a transmembrane current or a signal voltage for generating action potentials. We present a model to estimate the nonlinear encoding and responses of neurons driven by external optical signals. In the model, a (phototube) is used to activate a simple FitzHugh-Nagumo (FHN) neuron, and then external optical signals (illumination) are imposed to excite the for generating a time-varying current/voltage source. The -coupled FHN neuron can therefore capture and encode external optical signals, similar to artificial eyes. We also present detailed analysis for estimating the mode transition and firing pattern selection of neuronal electrical activities. The sampled time series can reproduce the main characteristics of biological neurons (quiescent, spiking, , and even chaotic behaviors) by activating the in the neural circuit. These results could be helpful in giving possible guidance for studying neurodynamics and applying neural circuits to detect optical signals.

Keywords： Photosensitive neuron     Neuron model     Bifurcation     Bursting     Photocell    

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Some Theoretical Issues on Procedure Neural Networks

He Xingui,Liang Jiuzhen

Strategic Study of CAE 2000, Volume 2, Issue 12,   Pages 40-44

Abstract:

In this paper, a novel artificial neuron model-procedure neuron model is proposed, in which the inputs are functions or procedures associated with ‘ time´. Based on these neurons, a model named procedure neural network, which is also a feedforward network with only one hidden layer, is constructed. The authors call this neural network as Procedure Neural Network (PNN) expanded on certain base functions. The related continuity, function approximation ability and computational capability theorems are proved.

Keywords： procedure neural networks     function approximation ability     computational capability     continuity    

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Phase synchronization and energy balance between neurons Research Article

Ying XIE, Zhao YAO, Jun MA

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9,   Pages 1407-1420 doi: 10.1631/FITEE.2100563

Abstract: A functional neuron has been developed from a simple by incorporating a phototube and a thermistor in different branch circuits. The physical field energy is controlled by the photocurrent across the phototube and the channel current across the thermistor. The firing mode of this neuron is controlled synchronously by external temperature and illumination. There is energy diversity when two functional neurons are exposed to different illumination and temperature conditions. As a result, synapse connections can be created and activated in an adaptive way when field energy is exchanged between neurons. We propose two kinds of criteria to discuss the enhancement of synapse connections to neurons. The energy diversity between neurons determines the increase of the coupling intensity and synaptic current for neurons, and the realization of synchronization is helpful in maintaining energy balance between neurons. The first criterion is similar to the saturation gain scheme in that the coupling intensity is increased with a constant step within a certain period until it reaches energy balance or complete synchronization. The second criterion is that the coupling intensity increases exponentially before reaching energy balance. When two neurons become non-identical, phase synchronization can be controlled during the activation of synapse connections to neurons. For two identical neurons, the second criterion for taming synaptic intensity is effective for reaching complete synchronization and energy balance, even in the presence of noise. This indicates that a synapse connection may prefer to enhance its coupling intensity exponentially. These results are helpful in discovering why synapses are awaken and synaptic current becomes time-varying when any neurons are excited by external stimuli. The potential biophysical mechanism is that energy balance is broken and then synapse connections are activated to maintain an adaptive energy balance between the neurons. These results provide guidance for designing and training intelligent neural networks by taming the coupling channels with gradient energy distribution.

Keywords： Hamilton energy     Coupling synchronization     Synapse enhancement     Neural circuit    

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Anovel spiking neural network of receptive field encoding with groups of neurons decision Article

Yong-qiang MA, Zi-ru WANG, Si-yu YU, Ba-dong CHEN, Nan-ning ZHENG, Peng-ju REN

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 139-150 doi: 10.1631/FITEE.1700714

Abstract: Human information processing depends mainly on billions of neurons which constitute a complex neural network, and the information is transmitted in the form of neural spikes. In this paper, we propose a spiking neural network (SNN), named MD-SNN, with three key features: (1) using receptive field to encode spike trains from images; (2) randomly selecting partial spikes as inputs for each neuron to approach the absolute refractory period of the neuron; (3) using groups of neurons to make decisions. We test MD-SNN on the MNIST data set of handwritten digits, and results demonstrate that: (1) Different sizes of receptive fields influence classification results significantly. (2) Considering the neuronal refractory period in the SNN model, increasing the number of neurons in the learning layer could greatly reduce the training time, effectively reduce the probability of over-fitting, and improve the accuracy by 8.77%. (3) Compared with other SNN methods, MD-SNN achieves a better classification; compared with the convolution neural network, MD-SNN maintains flip and rotation invariance (the accuracy can remain at 90 44% on the test set), and it is more suitable for small sample learning (the accuracy can reach 80 15% for 1000 training samples, which is 7.8 times that of CNN).

Keywords： Tempotron     Receptive field     Difference of Gaussian (DoG)     Flip invariance     Rotation invariance    

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Synchronization transition of a modular neural network containing subnetworks of different scales Research Article

Weifang HUANG, Lijian YANG, Xuan ZHAN, Ziying FU, Ya JIA,jiay@ccnu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1458-1470 doi: 10.1631/FITEE.2300008

Abstract: Time delay and coupling strength are important factors that affect the of neural networks. In this study, a containing s of different scales was constructed using the ;Huxley (HH) neural model; i.‍e., a small-scale random network was unidirectionally connected to a large-scale small-world network through chemical synapses. Time delays were found to induce multiple transitions in the network. An increase in coupling strength also promoted of the network when the time delay was an integer multiple of the firing period of a single neuron. Considering that time delays at different locations in a modular network may have different effects, we explored the influence of time delays within each and between two s on the of modular networks. We found that when the s were well synchronized internally, an increase in the time delay within both s induced multiple transitions of their own. In addition, the state of the small-scale network affected the of the large-scale network. It was surprising to find that an increase in the time delay between the two s caused the factor of the modular network to vary periodically, but it had essentially no effect on the within the receiving . By analyzing the phase difference between the two s, we found that the mechanism of the periodic variation of the factor of the modular network was the periodic variation of the phase difference. Finally, the generality of the results was demonstrated by investigating modular networks at different scales.

Keywords： Hodgkin–     Huxley neuron     Modular neural network     Subnetwork     Synchronization     Transmission delay    

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Dynamics of a neuron exposed to integer- and fractional-order discontinuous externalmagnetic flux Regular Papers

Karthikeyan RAJAGOPAL, Fahimeh NAZARIMEHR, Anitha KARTHIKEYAN, Ahmed ALSAEDI, Tasawar HAYAT, Viet-Thanh PHAM

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4,   Pages 584-590 doi: 10.1631/FITEE.1800389

Abstract:

We propose a modified Fitzhugh-Nagumo neuron (MFNN) model. Based on this model, an integerorder MFNN system (case A) and a fractional-order MFNN system (case B) were investigated. In the presence of electromagnetic induction and radiation, memductance and induction can show a variety of distributions. Fractionalorder magnetic flux can then be considered. Indeed, a fractional-order setting can be acceptable for non-uniform diffusion. In the case of an MFNN system with integer-order discontinuous magnetic flux, the system has chaotic and non-chaotic attractors. Dynamical analysis of the system shows the birth and death of period doubling, which is a sign of antimonotonicity. Such a behavior has not been studied previously in the dynamics of neurons. In an MFNN system with fractional-order discontinuous magnetic flux, different attractors such as chaotic and periodic attractors can be observed. However, there is no sign of antimonotonicity.

Keywords： Fitzhugh-Nagumo     Chaos     Fractional order     Magnetic flux    

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Visual Prostheses: Technological and Socioeconomic Challenges Perspective

John B. Troy

Engineering 2015, Volume 1, Issue 3,   Pages 288-291 doi: 10.15302/J-ENG-2015080

Abstract:

Visual prostheses are now entering the clinical marketplace. Such prostheses were originally targeted for patients suffering from blindness through retinitis pigmentosa (RP). However, in late July of this year, for the first time a patient was given a retinal implant in order to treat dry age-related macular degeneration. Retinal implants are suitable solutions for diseases that attack photoreceptors but spare most of the remaining retinal neurons. For eye diseases that result in loss of retinal output, implants that interface with more central structures in the visual system are needed. The standard site for central visual prostheses under development is the visual cortex. This perspective discusses the technical and socioeconomic challenges faced by visual prostheses.

Keywords： neuroprostheses     vision     eye disease     restoration of function     rehabilitation    

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Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes Review

Zhaofei Yu, Jian K. Liu, Shanshan Jia, Yichen Zhang, Yajing Zheng, Yonghong Tian, Tiejun Huang

Engineering 2020, Volume 6, Issue 4,   Pages 449-461 doi: 10.1016/j.eng.2020.02.004

Abstract:

A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion, while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body. Incoming visual information can be processed by the brain in millisecond intervals. The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation. Thus, the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike. Closed-loop computation in a neuroprosthesis includes two stages: encoding a stimulus as a neuronal signal, and decoding it back into a stimulus. In this paper, we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos. We hypothesize that in order to obtain a better understanding of the computational principles in the retina, a hypercircuit view of the retina is necessary, in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina. The different building blocks of the retina, which include a diversity of cell types and synaptic connections—both chemical synapses and electrical synapses (gap junctions)—make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes. An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.

Keywords： Visual coding     Retina     Neuroprosthesis     Brain–machine interface     Artificial intelligence     Deep learning     Spiking neural network     Probabilistic graphical model    

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Simulation Algorithm of Flightdeck Airflow Based on Neural Network

Xun Wensheng,Lin Ming

Strategic Study of CAE 2003, Volume 5, Issue 5,   Pages 76-79

Abstract:

The airflow on the flightdeck is an important factor which influences helicopter flight safety. The airflow velocity distribution characteristics directly influences simulation accuracy of helicopter flight dynamics. Based on the Navier-Stokes equations, the method to determine the airflow velocity in real-time is discussed using BP neural network. This method can be used for flightdeck airflow real-time simulation, and it can improve helicopter flight simulation accuracy.

Keywords： flow     finite element     neural network    

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Neural Mechanisms of Mental Fatigue Revisited: New Insights from the Brain Connectome Review

Peng Qi, Hua Ru, Lingyun Gao, Xiaobing Zhang, Tianshu Zhou, Yu Tian, Nitish Thakor, Anastasios Bezerianos, Jinsong Li, Yu Sun

Engineering 2019, Volume 5, Issue 2,   Pages 276-286 doi: 10.1016/j.eng.2018.11.025

Abstract:

Maintaining sustained attention during a prolonged cognitive task often comes at a cost: high levels of mental fatigue. Heuristically, mental fatigue refers to a feeling of tiredness or exhaustion, and a disengagement from the task at hand; it manifests as impaired cognitive and behavioral performance. In order to effectively reduce the undesirable yet preventable consequences of mental fatigue in many real-world workspaces, a better understanding of the underlying neural mechanisms is needed, and continuous efforts have been devoted to this topic. In comparison with conventional univariate approaches, which are widely utilized in fatigue studies, convergent evidence has shown that multivariate functional connectivity analysis may lead to richer information about mental fatigue. In fact, mental fatigue is increasingly thought to be related to the deviated reorganization of functional connectivity among brain regions in recent studies. In addition, graph theoretical analysis has shed new light on quantitatively assessing the reorganization of the brain functional networks that are modulated by mental fatigue. This review article begins with a brief introduction to neuroimaging studies on mental fatigue and the brain connectome, followed by a thorough overview of connectome studies on mental fatigue. Although only a limited number of studies have been published
thus far, it is believed that the brain connectome can be a useful approach not only for the elucidation of underlying neural mechanisms in the nascent field of neuroergonomics, but also for the automatic detection and classification of mental fatigue in order to address the prevention of fatigue-related human error in the near future.

Keywords： Mental fatigue     Functional connectivity     Graph theoretical analysis     Brain network    

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Miniaturized five fundamental issues about visual knowledge Perspectives

Yun-he Pan,panyh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.2040000

Abstract: 认知心理学早已指出，人类知识记忆中的重要部分是视觉知识，被用来进行形象思维。因此，基于视觉的人工智能（AI）是AI绕不开的课题，且具有重要意义。本文继《论视觉知识》一文，讨论与之相关的5个基本问题：（1）视觉知识表达；（2）视觉识别；（3）视觉形象思维模拟；（4）视觉知识的学习；（5）多重知识表达。视觉知识的独特优点是具有形象的综合生成能力，时空演化能力和形象显示能力。这些正是字符知识和深度神经网络所缺乏的。AI与计算机辅助设计/图形学/视觉的技术联合将在创造、预测和人机融合等方面对AI新发展提供重要的基础动力。视觉知识和多重知识表达的研究是发展新的视觉智能的关键，也是促进AI 2.0取得重要突破的关键理论与技术。这是一块荒芜、寒湿而肥沃的“北大荒”，也是一块充满希望值得多学科合作勇探的“无人区”。

Keywords： 视觉知识表达；视觉识别；视觉形象思维模拟；视觉知识学习；多重知识表达    

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Three-dimensional shape space learning for visual concept construction: challenges and research progress Perspective

Xin TONG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9,   Pages 1290-1297 doi: 10.1631/FITEE.2200318

Abstract: Human beings can easily categorize three-dimensional (3D) objects with similar shapes and functions into a set of “visual concepts” and learn “visual knowledge” of the surrounding 3D real world (). Developing efficient methods to learn the computational representation of the visual concept and the visual knowledge is a critical task in artificial intelligence (). A crucial step to this end is to learn the shape space spanned by all 3D objects that belong to one visual concept. In this paper, we present the key technical challenges and recent research progress in 3D shape space learning and discuss the open problems and research opportunities in this area.

Keywords： 视觉概念；视觉知识；三维几何学习；三维形状空间；三维结构    

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Mittag-Leffler stability analysis ofmultiple equilibrium points in impulsive fractional-order quaternion-valued neural networks Research Articles

K. UDHAYAKUMAR, R. RAKKIYAPPAN, Jin-de CAO, Xue-gang TAN

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 2,   Pages 234-246 doi: 10.1631/FITEE.1900409

Abstract: In this study, we investigate the problem of multiple Mittag-Leffler stability analysis for fractional-order quaternion-valued neural networks (QVNNs) with impulses. Using the geometrical properties of activation functions and the Lipschitz condition, the existence of the equilibrium points is analyzed. In addition, the global Mittag-Leffler stability of multiple equilibrium points for the impulsive fractional-order QVNNs is investigated by employing the Lyapunov direct method. Finally, simulation is performed to illustrate the effectiveness and validity of the main results obtained.

Keywords： Mittag-Leffler stability     Fractional-order     Quaternion-valued neural networks     Impulse    

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A quantitative attribute-based benchmark methodology for single-target visual tracking Article

Wen-jing KANG, Chang LIU, Gong-liang LIU

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 405-421 doi: 10.1631/FITEE.1900245

Abstract: In the past several years, various visual object tracking benchmarks have been proposed, and some of them have been used widely in numerous recently proposed trackers. However, most of the discussions focus on the overall performance, and cannot describe the strengths and weaknesses of the trackers in detail. Meanwhile, several benchmark measures that are often used in tests lack convincing interpretation. In this paper, 12 frame-wise visual attributes that reflect different aspects of the characteristics of image sequences are collated, and a normalized quantitative formulaic definition has been given to each of them for the first time. Based on these definitions, we propose two novel test methodologies, a correlation-based test and a weight-based test, which can provide a more intuitive and easier demonstration of the trackers’ performance for each aspect. Then these methods have been applied to the raw results from one of the most famous tracking challenges, the Video Object Tracking (VOT) Challenge 2017. From the tests, most trackers did not perform well when the size of the target changed rapidly or intensely, and even the advanced deep learning based trackers did not perfectly solve the problem. The scale of the targets was not considered in the calculation of the center location error; however, in a practical test, the center location error is still sensitive to the targets’ changes in size.

Keywords： Visual tracking     Performance evaluation     Visual attributes     Computer vision    

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Title Author Date Type Operation

Visual commonsense reasoning with directional visual connections

Yahong Han, Aming Wu, Linchao Zhu, Yi Yang,yahong@tju.edu.cn

Journal Article

A new photosensitive neuron model and its dynamics

Yong Liu, Wan-jiang Xu, Jun Ma, Faris Alzahrani, Aatef Hobiny,hyperchaos@163.com,hyperchaos@lut.edu.cn

Journal Article

Some Theoretical Issues on Procedure Neural Networks

He Xingui,Liang Jiuzhen

Journal Article

Phase synchronization and energy balance between neurons

Ying XIE, Zhao YAO, Jun MA

Journal Article

Anovel spiking neural network of receptive field encoding with groups of neurons decision

Yong-qiang MA, Zi-ru WANG, Si-yu YU, Ba-dong CHEN, Nan-ning ZHENG, Peng-ju REN

Journal Article

Synchronization transition of a modular neural network containing subnetworks of different scales

Weifang HUANG, Lijian YANG, Xuan ZHAN, Ziying FU, Ya JIA,jiay@ccnu.edu.cn

Journal Article

Dynamics of a neuron exposed to integer- and fractional-order discontinuous externalmagnetic flux

Karthikeyan RAJAGOPAL, Fahimeh NAZARIMEHR, Anitha KARTHIKEYAN, Ahmed ALSAEDI, Tasawar HAYAT, Viet-Thanh PHAM

Journal Article

Visual Prostheses: Technological and Socioeconomic Challenges

John B. Troy

Journal Article

Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

Zhaofei Yu, Jian K. Liu, Shanshan Jia, Yichen Zhang, Yajing Zheng, Yonghong Tian, Tiejun Huang

Journal Article

Simulation Algorithm of Flightdeck Airflow Based on Neural Network

Xun Wensheng,Lin Ming

Journal Article

Neural Mechanisms of Mental Fatigue Revisited: New Insights from the Brain Connectome

Peng Qi, Hua Ru, Lingyun Gao, Xiaobing Zhang, Tianshu Zhou, Yu Tian, Nitish Thakor, Anastasios Bezerianos, Jinsong Li, Yu Sun

Journal Article

Miniaturized five fundamental issues about visual knowledge

Yun-he Pan,panyh@zju.edu.cn

Journal Article

Three-dimensional shape space learning for visual concept construction: challenges and research progress

Xin TONG

Journal Article

Mittag-Leffler stability analysis ofmultiple equilibrium points in impulsive fractional-order quaternion-valued neural networks

K. UDHAYAKUMAR, R. RAKKIYAPPAN, Jin-de CAO, Xue-gang TAN

Journal Article

A quantitative attribute-based benchmark methodology for single-target visual tracking

Wen-jing KANG, Chang LIU, Gong-liang LIU

Journal Article