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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Juanjuan He Zhengke Miao Zheng Zhang Xiaolong Shi Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China Department of Mathematics XuZhou Normal University of China Xuzhou China

Tissue P systems with cell division are biologically inspired theoretical models of distributed and parallel computing, which provide the possibility to solve computationally hard problems in polynomial time. In this paper, we present a semi-uniform solution to the multidimensional 0-1 knapsack problem by such kind of devices, which is linear in terms of the number of variables, the number of constraint inequalities, and all coefficients related with variables.

关键词： Multidimensional systems Biomembranes Biology computing Polynomials Biological tissues Cells (biology) Biological system modeling Helium image processing intelligent control

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Effects of Epidural Spinal Cord Stimulation and Treadmill Training on Locomotion Function and Ultrastructure of Spinal Cord Anterior Horn after Moderate Spinal Cord Injury in Rats

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中国康复医学杂志 2009年第6期24卷 485-488页

作者： WANG Yizhao HUANG Xiaolin XU Jiang XU Tao FANG Zhengyu XU Qi TU Xikai YANG Peipei Department of Rehabilitation Medicine Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan 430030 China Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Technology Huazhong University of Science and Technology Wuhan 430000 China

Objective:To investigate the effects of epidural spinal cord stimulation(ESCS) and treadmill training on the locomotion function and ultrastructure of spinal cord anterior horn after moderate spinal cord injury in ***:Nine adult female Sprague-Dawley rats were randomly distributed into three groups:①spinal cord injury group(SI,n=3).②spinal cord injury plus ESCS group(SE,n=3).③spinal cord injury plus treadmill training group(TT,n=3).All rats received a moderate spinal cord injury *** weeks after surgery,rats in SE group received an electrode implantation procedure,with the electrode field covering spinal cord segments *** weeks after electrode implantation,rats received subthreshold ESCS for 30 min/*** in TT group received 4cm/s treadmill training for 30min/*** in SI group received no intervention,as a control *** procedures in these three groups lasted four *** open field Basso,Beattie and Bresnahan(BBB) scale was used before and after intervention to evaluate rats' hindlimb motor ***:After four weeks intervention,rats in TT group improved their open field locomotion scores to *** contrast,no significant improvement was observed in groups SI and *** morphology of synapses and neurons were similar regardless of whether rats had undergone ESCS,treadmill training or ***:ESCS alone was not sufficient to improve the walking ability of spinal cord injured *** or treadmill training alone might not contribute to the changes of ultrastructure in anterior horn of spinal cord that underlie the recovery of walking *** research is needed to understand the contributions of combination of ESCS and treadmill training to the rehabilitation of spinal cord injured rats.

关键词：脊髓损伤神经元治疗方法跑步训练

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Deterministic algorithm for the reordering problem using tile assembly

Deterministic algorithm for the reordering problem using til...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Yufang Huang Jin Xu Zhen Cheng Zhihua Chen Xuncai Zhang Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China School of Electronics Engineering and Computer Science Peking University Beijing China

Algorithmic self-assembly, a generalization of crystal growth, has been proposed as a mechanism for the bottom-up fabrication of autonomous DNA computation. In theory, growth can be programmed by designing a set of molecular tiles with binding interactions that enforce assembly rules. There are many interesting applications of the reordering problem in Mathematics, as well as Computer Sciences. It is a useful primitive operation for computation and construction in the field of science and engineering. It is worth exploring more efficient approach to solve this problem. In this paper, we provide a description of a novel method for the reordering problem by use of the programmable tile assembly model. We propose the thought of block assembly model which takes full advantage of the huge parallelism inherent in the tile assembly. On the basis of the structural property, we delicately devise a rule to produce a long reporter strand which can connect all the inputs of the given problem with the final results. It is proved that this method can be extended to any reordering problem with arbitrary size in theory. The great advantage of our schema is that it only needs a constant number of tile types and in time linear with the size of the input problem to solve any reordering problem.

关键词： Tiles Assembly Self-assembly DNA computing Molecular computing Fabrication Logic Biology computing laboratories intelligent control

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Improve consensus via decentralized predictive mechanisms

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EPL 2009年第4期86卷 40011 (6pp-40011 (6pp页

作者： Zhang, H. -T. Chen, M. Z. Q. Zhou, T. Huazhong Univ Sci & Technol Dept Control Sci & Engn Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Nanjing Univ Sci & Technol Dept Automat Nanjing 210094 Peoples R China Univ Leicester Dept Engn Leicester LE1 7RH Leics England Univ Sci & Technol China Dept Modern Phys Hefei 230026 Peoples R China Univ Fribourg Dept Phys CH-1700 Fribourg Switzerland

For biogroups and groups of self-driven agents, making decisions often depends on interactions among group members. In this paper, we seek to understand the fundamental predictive mechanisms used by group members in order to perform such coordinated behaviors. In particular, we show that the future dynamics of each node in the network can be predicted solely using local information provided by its neighbors. Using this predicted future dynamics information, we propose a decentralized predictive consensus protocol, which yields drastic improvements in terms of both consensus speed and internal communication cost. In natural science, this study provides an evidence for the idea that some decentralized predictive mechanisms may exist in widely-spread biological swarms/flocks. From the industrial point of view, incorporation of a decentralized predictive mechanism allows for not only a significant increase in the speed of convergence towards consensus but also a reduction in the communication energy required to achieve a predefined consensus performance. Copyright (C) EPLA, 2009

关键词： Self-organized systems Cell locomotion, chemotaxis Complex systems

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Variational Bayesian framework for estimating parameters of integrated E/MEG and fMRI model

Variational Bayesian framework for estimating parameters of ...

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Babajani-Feremi, Abbas Bowyer, Susan Moran, John Elisevich, Kost Soltanian-Zadeh, Hamid Image Analysis Lab. Radiology Department Henry Ford Hospital Detroit MI 48202 United States Neuromagnetism Lab. Neurology Department Henry Ford Hospital Detroit MI 48202 United States Department of Neurology Henry Ford Hospital Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran 14395-515 Iran

ISBN: (纸本)9780819475138

The integrated analysis of the Electroencephalography (EEG), Magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) are instrumental for functional neuroimaging of the brain. A bottom-up integrated E/MEG and fMRI model based on physiology as well as a method for estimating its parameters are keys to the integrated analysis. We propose the variational Bayesian expectation maximization (VBEM) method to estimate parameters of our proposed integrated model. VBEM method iteratively optimizes a lower bound on the marginal likelihood. An iteration of the VBEM consists of two steps: a variational Bayesian expectation step implemented using the extended Kalman smoother (EKS) and the posterior probability of the parameters in the previous step, and a variational Bayesian maximization step to estimate the posterior distributions of the parameters. For a given external stimulus, a variety of multi-area models can be considered in which the number of areas and the configuration and strength of connections between the areas are different. The proposed VBEM method can be used to select an optimal model as well as estimate its parameters. The efficiency of the proposed VBEM method is illustrated using simulation and real datasets. The proposed VBEM method can be used to estimate parameters of other non- linear dynamical systems. This study proposes an effective method to integrate E/MEG and fMRI and plans to use these techniques in functional neuroimaging. © 2009 SPIE.

关键词： Electroencephalography

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Multi-area integrated E/MEG and fMRI modeling

Multi-area integrated E/MEG and fMRI modeling

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Babajani-Feremi, Abbas Bowyer, Susan Moran, John Elisevich, Kost Podell, Kenneth Soltanian-Zadeh, Hamid Image Analysis Lab. Radiology Department Henry Ford Hospital Detroit MI 48202 United States Neuromagnetism Lab. Neurology Department Henry Ford Hospital Detroit MI 48202 United States Department of Neurology Henry Ford Hospital Detroit MI 48202 United States Division of Neuropsychology and Sports Concussion Safety Program Henry Ford Hospital Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran 14395-515 Iran

ISBN: (纸本)9780819475138

Functional magnetic resonance imaging (fMRI) has complementary spatiotemporal resolution compared to Electroencephalography (EEG) as well as Magnetoencephalography (MEG). Thus, their integrated analysis should improve the overall resolution. To integrate analysis of E/MEG and fMRI, we extend our previously proposed integrated E/MEG and fMRI neural mass model to a multi-area model by defining two types of connections: the Short-Range Connections (SRCs) between minicolumns within the areas and Long-Range Connections (LRCs) between inter-areas minicolumns. The nonlinear input/output relationship in the proposed model is derived from the state space representation of the multi-area model. The E/MEG signals are originated from the overall synaptic activities of the pyramidal cells of all minicolumns and can be calculated using the lead field matrix (i.e., forward electromagnetic model). The fMRI signal is extracted from the proposed integrated model by calculating the overall neural activities in the areas and using it as the input of the extended balloon model (EBM). Using the simulation results, the capabilities of the proposed model to generate E/MEG and fMRI signals is shown. In addition, changes in the dynamics of the model to variations of its parameters were evaluated and lead us to the appropriate ranges for the parameters. In conclusion, this work proposes an effective method to integrate E/MEG and fMRI for the more effective use of these techniques in functional neuroimaging. © 2009 SPIE.

关键词： Electroencephalography

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Research advances and prospect of spiking neural P systems

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Jisuanji Xuebao/Chinese Journal of Computers 2008年第12期31卷 2090-2096页

作者： Pan, Lin-Qiang Zhang, Xing-Yi Zeng, Xiang-Xiang Wang, Jun Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

Spiking neural P systems are a new computing model inspired from the biological phenomena that in the brain the neurons cooperate to deal with spikes by axons. Since it has been shown that they have powerful computational capability and potential capability in solving computationally hard problems, more and more people begin to get interested in this field. This paper firstly introduces the formal definition of standard spiking neural P systems and some notions which are often used in this area;then, several extensions of the original spiking neural P systems are summarized, that are: Extented SN P system;SN P system with exhaustive use of rules;Asynchronous SN P system;Sequential SN P system. Also, the results on the topic of spiking neural P systems are briefly recalled in two aspects: computational completeness and computational efficiency. In the end, two more important future research directions on spiking neural P systems are pointed out. Specifically, one interesting topic is to develop a new computing model which is more "realistic";another topic is to consider how to use these models in biological modeling and simulation.

关键词： Computational efficiency

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Smaller Universal Spiking Neural P Systems

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FUNDAMENTA INFORMATICAE 2008年第1期87卷 117-136页

作者： Zhang, Xingyi Zeng, Xiangxiang Pan, Linqiang Huazhong Univ Sci & Technol Dept Control Sci & Engn Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

The problem of finding small universal spiking neural P systems was recently investigated by Andrei Paun and Gheorghe Paun, for spiking neural P systems used as devices computing functions and as devices generating sets of numbers. For the first case, a universal spiking neural P system was produced by using 84 neurons for standard rules and using 49 neurons for extended rules. For spiking neural P systems used as generators of sets of numbers, a universal system with standard rules having 76 neurons, and one with extended rules having 50 neurons were obtained. In this paper, we continue the study of small universal spiking neural P systems and we improve in the number of neurons as follows. The small universal spiking neural P systems use 67 neurons for standard rules and 41 neurons for extended rules in the case of computing functions, and 63 neurons for standard rules and 41 neurons for extended rules in the case of generating sets of numbers.

关键词： membrane computing SN P systems small universal SN P systems

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Emergency response to disaster-struck scale-free network with redundant systems

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2008年第18期387卷 4683-4691页

作者： Ouyang, Min Yu, Ming-Hui Huang, Xiang-Zhao Luan, En-Jie Huazhong Univ Sci & Technol Inst Syst Engn Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Disasters cause tremendous damage every year. In this paper, we have specifically studied emergency response to disaster-struck scale-free networks when some nodes in the network have redundant systems. If one node collapses, its redundant system will substitute it to work for a period of time. In the first part, according to the network structure, several redundant strategies have been formulated, and then our studies focused on their effectiveness by means of simulation. Results show that the strategy based on total degrees is the most effective one. However, many nodes still collapse in the end if redundant systems do not have sufficient capability, so emergency responses are necessary. Several emergent strategies controlling the distribution of external resources have been proposed in the second part. The effectiveness of those emergent strategies are then studied from three aspects, such as the effect of strategies on spreading processes, minimum sufficient quantities of external resources and determination of the most appropriate emergent strategy. In addition, the effects of redundant intensity on these aspects have been discussed as well. (C) 2008 Elsevier B.V. All rights reserved.

关键词： scale-free network redundant system disaster spreading emergency response

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Directed epileptic network from scalp and intracranial EEG of epileptic patients

Directed epileptic network from scalp and intracranial EEG o...

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IEEE Workshop on Machine Learning for Signal processing

作者： L. Amini C. Jutten S. Achard O. David H. Soltanian-Zadeh G.A. Hossein-Zadeh P. Kahane L. Minotti L. Vercueil Grenoble INP GIPSA Laboratory Grenoble France Grenoble INP GIPSA-LAB Grenoble Cedex France Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Tehran Iran CHUG Neurology Department GIN U836 INSERM Grenoble France Neurology Department CHUG Grenoble France Henry Ford Health System Radiology Image Analysis Laboratory Detroit MI USA

We proposed recently the computation of epileptic connectivity graphs based on wavelet correlation coefficients between EEG signals. The suspected epileptiform electrodes are recognized using the clustering of the topological properties of the graph that can be useful for pre-surgical studies. Here, we present a method for comparing epileptic networks estimated from scalp and intracranial EEG (IEEG) in partial epilepsy patients. The results are presented for a patient with left temporal epilepsy. Good spatial correspondence between the IEEG and the scalp EEG epileptic graphs is obtained. These results are consistent with the patient's clinical diagnosis.

关键词： Epilepsy Scalp Electroencephalography Electrodes Surgery Time frequency analysis Indium phosphide Radio control Process control control systems

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