Time delay and coupling strength are important factors that affect the synchronization of neural networks.In this study,a modular neural network containing subnetworks of different scales was constructed using the Hod...Time delay and coupling strength are important factors that affect the synchronization of neural networks.In this study,a modular neural network containing subnetworks of different scales was constructed using the Hodgkin–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 synchronization transitions in the network.An increase in coupling strength also promoted synchronization 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 subnetwork and between two subnetworks on the synchronization of modular networks.We found that when the subnetworks were well synchronized internally,an increase in the time delay within both subnetworks induced multiple synchronization transitions of their own.In addition,the synchronization state of the small-scale network affected the synchronization of the large-scale network.It was surprising to find that an increase in the time delay between the two subnetworks caused the synchronization factor of the modular network to vary periodically,but it had essentially no effect on the synchronization within the receiving subnetwork.By analyzing the phase difference between the two subnetworks,we found that the mechanism of the periodic variation of the synchronization 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.展开更多
The active-subnetwork-extraction theorem and the passive-subnetwork-extraction theorem are derived from one of the author’s previous work. Using them to find the fully symbolic network functions, the multilevel-teari...The active-subnetwork-extraction theorem and the passive-subnetwork-extraction theorem are derived from one of the author’s previous work. Using them to find the fully symbolic network functions, the multilevel-tearing topological analysis for active networks can be substantially simplified so that it can be conducted conveniently on a computer. Using them to find partially symbolic network functions, one finds it possible to extend not only the electrical network that can be analysed on a computer to the order that can be processed by an ordinary numerical analysis program, but also the symbolic subnetwork to the order that can be processed by an ordinary topological analysis program. So far the latter cannot be achieved with the current conventional methods, i. e. the parameter-extraction method and interpolative approach.展开更多
The problem for the solvability of pseudo-tearing subnetwork is one of the essentialinvestigations of network theory.The results presented would be not only mathematical conditionsbut also topological conditions for s...The problem for the solvability of pseudo-tearing subnetwork is one of the essentialinvestigations of network theory.The results presented would be not only mathematical conditionsbut also topological conditions for subnetwork solvability.These conditions are necessary andalmost sufficient.It should guide one intuitively to the design of accessible nodes.展开更多
A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network（interactome） indicates that, at ...A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network（interactome） indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy（IDCM） data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.展开更多
Human motion recognition plays a crucial role in the video analysis framework.However,a given video may contain a variety of noises,such as an unstable background and redundant actions,that are completely different fr...Human motion recognition plays a crucial role in the video analysis framework.However,a given video may contain a variety of noises,such as an unstable background and redundant actions,that are completely different from the key actions.These noises pose a great challenge to human motion recognition.To solve this problem,we propose a new method based on the 3-Dimensional(3D)Bag of Visual Words(BoVW)framework.Our method includes two parts:The first part is the video action feature extractor,which can identify key actions by analyzing action features.In the video action encoder,by analyzing the action characteristics of a given video,we use the deep 3D CNN pre-trained model to obtain expressive coding information.A classifier with subnetwork nodes is used for the final classification.The extensive experiments demonstrate that our method leads to an impressive effect on complex video analysis.Our approach achieves state-of-the-art performance on the datasets of UCF101(85.3%)and HMDB51(54.5%).展开更多
基金supported by the National Natural Science Foundation of China(No.12175080)the Fundamental Research Funds for the Central Universities,China(No.CCNU22JC009)。
文摘Time delay and coupling strength are important factors that affect the synchronization of neural networks.In this study,a modular neural network containing subnetworks of different scales was constructed using the Hodgkin–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 synchronization transitions in the network.An increase in coupling strength also promoted synchronization 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 subnetwork and between two subnetworks on the synchronization of modular networks.We found that when the subnetworks were well synchronized internally,an increase in the time delay within both subnetworks induced multiple synchronization transitions of their own.In addition,the synchronization state of the small-scale network affected the synchronization of the large-scale network.It was surprising to find that an increase in the time delay between the two subnetworks caused the synchronization factor of the modular network to vary periodically,but it had essentially no effect on the synchronization within the receiving subnetwork.By analyzing the phase difference between the two subnetworks,we found that the mechanism of the periodic variation of the synchronization 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.
文摘The active-subnetwork-extraction theorem and the passive-subnetwork-extraction theorem are derived from one of the author’s previous work. Using them to find the fully symbolic network functions, the multilevel-tearing topological analysis for active networks can be substantially simplified so that it can be conducted conveniently on a computer. Using them to find partially symbolic network functions, one finds it possible to extend not only the electrical network that can be analysed on a computer to the order that can be processed by an ordinary numerical analysis program, but also the symbolic subnetwork to the order that can be processed by an ordinary topological analysis program. So far the latter cannot be achieved with the current conventional methods, i. e. the parameter-extraction method and interpolative approach.
基金This project supported by National Natural Science Foundation of China
文摘The problem for the solvability of pseudo-tearing subnetwork is one of the essentialinvestigations of network theory.The results presented would be not only mathematical conditionsbut also topological conditions for subnetwork solvability.These conditions are necessary andalmost sufficient.It should guide one intuitively to the design of accessible nodes.
基金funded by the National Plan for Science,Technology and Innovation program (NSTIP/KACST, No.11-BIO2072-20 to D.C.)
文摘A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network（interactome） indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy（IDCM） data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.
文摘Human motion recognition plays a crucial role in the video analysis framework.However,a given video may contain a variety of noises,such as an unstable background and redundant actions,that are completely different from the key actions.These noises pose a great challenge to human motion recognition.To solve this problem,we propose a new method based on the 3-Dimensional(3D)Bag of Visual Words(BoVW)framework.Our method includes two parts:The first part is the video action feature extractor,which can identify key actions by analyzing action features.In the video action encoder,by analyzing the action characteristics of a given video,we use the deep 3D CNN pre-trained model to obtain expressive coding information.A classifier with subnetwork nodes is used for the final classification.The extensive experiments demonstrate that our method leads to an impressive effect on complex video analysis.Our approach achieves state-of-the-art performance on the datasets of UCF101(85.3%)and HMDB51(54.5%).
