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Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.最新文献

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Motivational modulation of endogenous inputs to the superior colliculus 上丘内源性输入的动机调节
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1555840
J. Satel, T. Trappenberg, R. Klein
{"title":"Motivational modulation of endogenous inputs to the superior colliculus","authors":"J. Satel, T. Trappenberg, R. Klein","doi":"10.1109/IJCNN.2005.1555840","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1555840","url":null,"abstract":"Proper initiation of saccadic eye movements depends on an intricate balance between exogenous and endogenous control mechanisms. The superior colliculus (SC) is a major site of signal integration that has been shown to drive the initiation of saccades in the brainstem. Previous work has shown that a winner-take-all mechanism implemented with a continuous attractor neural network (CANN) can explain and reproduce a multitude of behavioural findings, including the gap effect and the production of express saccades by K. Kopecz (1995) and by T.P. Trappenberg (2001). This investigation advances the CANN model of saccade initiation in several important ways in order to account for trial by trial adaptation of saccadic reaction times in a biologically plausible manner. A key hypothesis is that endogenous inputs to the intermediate layer of the SC can be adapted through motivationally-based feedback from other areas of the brain such as the basal ganglia or higher cortical areas.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123742372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Empirical approximation for Lyapunov functions with artificial neural nets 李雅普诺夫函数的人工神经网络经验逼近
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1555943
G. Serpen
{"title":"Empirical approximation for Lyapunov functions with artificial neural nets","authors":"G. Serpen","doi":"10.1109/IJCNN.2005.1555943","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1555943","url":null,"abstract":"An artificial neural network is proposed as a function approximator for empirical modeling of a Lyapunov function for a nonlinear dynamic system that projects stable behavior as potentially observable in its state space. The theoretical framework for the methodology of designing the so-called Lyapunov neural network, which empirically models a Lyapunov function, is described. Algorithms for training the Lyapunov neural network for a neurodynamics system are presented.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116101702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 22
Cluster ensemble for gene expression microarray data 基因表达微阵列数据的聚类集成
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1555879
M. D. Souto, Shirlly C. M. Silva, V. G. Bittencourt, D. Araújo
{"title":"Cluster ensemble for gene expression microarray data","authors":"M. D. Souto, Shirlly C. M. Silva, V. G. Bittencourt, D. Araújo","doi":"10.1109/IJCNN.2005.1555879","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1555879","url":null,"abstract":"Ensemble techniques have been successfully applied in the context of supervised learning to increase the accuracy and stability of classification. Recently, similar techniques have been proposed for clustering algorithms. In this context, we analyze the potential of applying cluster ensemble techniques to gene expression microarray data. Our experimental results show that there is often a significant improvement in the results obtained with the use of ensemble when compared to those based on the clustering techniques used individually.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122654920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Comparative genomic study of Parkinson's disease candidate genes 帕金森病候选基因的比较基因组研究
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1555868
G.W.L. Pang, Jagath Rajapakse
{"title":"Comparative genomic study of Parkinson's disease candidate genes","authors":"G.W.L. Pang, Jagath Rajapakse","doi":"10.1109/IJCNN.2005.1555868","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1555868","url":null,"abstract":"Several candidate genes affect Parkinson's disease in a variety of ways. A comparative analysis is performed using orthologues of these genes in other vertebrate species, such as chimp, mouse, rat, chicken, fugu, and tetraodon. The analysis reveals the presence of transmembrane regions and signal peptides in several sequences of some species, which provides a better understanding of the variability of structural and functional aspects of these genes in different species.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122462505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bi-criteria torque optimization of redundant manipulators based on a simplified dual neural network 基于简化对偶神经网络的冗余机械臂双准则转矩优化
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556368
Shubao Liu, Jun Wang
{"title":"Bi-criteria torque optimization of redundant manipulators based on a simplified dual neural network","authors":"Shubao Liu, Jun Wang","doi":"10.1109/IJCNN.2005.1556368","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556368","url":null,"abstract":"The bi-criteria joint torque optimization of kinematically redundant manipulators balances between the energy consumption and the torque distribution among the joints. In this paper, a simplified dual neural network is proposed to solve this problem. Joint torque limits are incorporated simultaneously into the proposed optimization scheme. The simplified dual network has less numbers of neurons compared with other recurrent neural networks and is proved to be globally convergent to optimal solutions. The control scheme based on the recurrent neural network is simulated with the PUMA 560 robot manipulator to demonstrate effectiveness.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122789474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Simulated control of a tracking mobile robot by four aVLSI integrate-and-fire neurons paired into maps 4个配对成地图的aVLSI集火神经元对跟踪移动机器人的模拟控制
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1555936
J. Dungen, Jean-Jules Brault
{"title":"Simulated control of a tracking mobile robot by four aVLSI integrate-and-fire neurons paired into maps","authors":"J. Dungen, Jean-Jules Brault","doi":"10.1109/IJCNN.2005.1555936","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1555936","url":null,"abstract":"A simulated four-wheeled robot is controlled exclusively by four aVLSI integrate-and-fire neurons paired into winner-takes-all maps. The neural network takes analog sensor data as input and outputs to stepper motors controlling steering and throttle. The robot follows a randomly moving target in a closed environment 67% better than by chance, based on average distance to target. Simulation results suggest that silicon neural networks based on biological computing principles are effective, efficient, and compact embedded controllers. Test results should be confirmed on a physical implementation of the robot, and research should continue in network and circuit optimisation, as well as in the creation of robot societies.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116872101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An analysis of associative chaotic neurodynamics by using surrogate neurons 用替代神经元分析联想混沌神经动力学
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1555947
M. Adachi
{"title":"An analysis of associative chaotic neurodynamics by using surrogate neurons","authors":"M. Adachi","doi":"10.1109/IJCNN.2005.1555947","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1555947","url":null,"abstract":"In the present paper, associative chaotic neurodynamics is analyzed by using a method for nonlinear time series analysis. The aim of the analysis is to finding out which statistic of the deterministic chaos of the constituent neurons is important for the chaotic associative neurodynamics. A method comparing features of the original time series with that of artificially made time series preserving some statistics of the original one is applied for the analysis as follows. Some of the constituent neurons in the chaotic neural network are replaced by their surrogate data. The retrieval frequencies of the original network and the network with three surrogate methods, that preserve the dynamic range of the original data, are compared. The results show that not only the auto-correlation in neuronal output of a neuron but also the cross-spectra among the neurons in the network play certain role for maintaining the associative chaotic neurodynamics.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128474529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Computational models of emotion 情绪的计算模型
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556117
J. Armony
{"title":"Computational models of emotion","authors":"J. Armony","doi":"10.1109/IJCNN.2005.1556117","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556117","url":null,"abstract":"Emotion is clearly an important aspect of the mind, yet it has been largely ignored by the brain and mind (cognitive) sciences in modern times. However, there are signs that this is beginning to change. Here, we survey some issues about the nature of emotion, describe what is known about the neural basis of emotion, and consider some efforts that have been made to develop computational models of different aspects of emotion.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129356503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
A vigilance-free ART network with general geometry internal categories 具有一般几何内部类别的无警戒ART网络
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1555875
D. Gomes, M. Fernández-Delgado, S. Barro
{"title":"A vigilance-free ART network with general geometry internal categories","authors":"D. Gomes, M. Fernández-Delgado, S. Barro","doi":"10.1109/IJCNN.2005.1555875","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1555875","url":null,"abstract":"ART neural networks are important tools for online supervised pattern recognition. They use internal categories with pre-defined geometry, given by the category choice function. Pre-defined geometry limits the ability of the categories to fit complex borders among output predictions for a given data set, and may contribute to the category proliferation problem. This work proposes Polytope ARTMAP (PTAM), whose category representation regions have general geometry-polytopes in R/sup n/ whose vertices are selected training patterns. The category borders compose a piece-wise linear approximation to the borders among predictions. Overlapping among categories is avoided in PTAM because they do not need to overlap in order to keep their geometry during learning. The choice function does not depend on the category size. Category growing is only limited by the other categories, and the vigilance parameter can be removed, so that PTAM learns a training data set without any parameter tuning.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130307570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
A hierarchical Bayesian model of invariant pattern recognition in the visual cortex 视觉皮层中不变模式识别的层次贝叶斯模型
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556155
D. George, J. Hawkins
{"title":"A hierarchical Bayesian model of invariant pattern recognition in the visual cortex","authors":"D. George, J. Hawkins","doi":"10.1109/IJCNN.2005.1556155","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556155","url":null,"abstract":"We describe a hierarchical model of invariant visual pattern recognition in the visual cortex. In this model, the knowledge of how patterns change when objects move is learned and encapsulated in terms of high probability sequences at each level of the hierarchy. Configuration of object parts is captured by the patterns of coincident high probability sequences. This knowledge is then encoded in a highly efficient Bayesian network structure. The learning algorithm uses a temporal stability criterion to discover object concepts and movement patterns. We show that the architecture and algorithms are biologically plausible. The large scale architecture of the system matches the large scale organization of the cortex and the micro-circuits derived from the local computations match the anatomical data on cortical circuits. The system exhibits invariance across a wide variety of transformations and is robust in the presence of noise. Moreover, the model also offers alternative explanations for various known cortical phenomena.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130531170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 246
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