{"title":"Testing models of attention with MEG","authors":"A. A. Ioannides, J. G. Taylor","doi":"10.1109/IJCNN.2003.1223359","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223359","url":null,"abstract":"Neuroimaging methods have recently added new opportunities to test models of attention to deficit studies and approaches relying on invasive single and few unit electrophysiology. These techniques have very different time resolution so, a bridging technology is needed to link results from the slow hemodynamic methods and invasive electrophysiology. Electroencephalography (EEG) and magnetoencephalography (MEG) rely on non-invasive measures of mass electrical activity and are good candidates for this task. Some new insights have already been obtained using EEG and/or MEG in conjunction with fMRI. In these studies, the average EEG or MEG signal was used, often with constraints from fMRI, to study changes in the timecourse of regional activations under different attentional load. We postulate an engineering control framework that is broadly consistent with recent findings and generates predictions about when specific modules will become active under different attention paradigms. Specifically, the presence of relatively early temporal signals is predicted to occur in the parietal and frontal lobes as part of the attention-based CODAM model of consciousness. Tomographic analysis of single trial MEG data from a GO/NOGO task then identifies well-circumscribed regional increases and decreases of activity that confirm these general predictions. The results show how detailed information can be extracted from single subject data when the richness of information in the single trial MEG data is carefully analyzed and point the way for more elaborate analysis in the future.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115938413","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}
{"title":"A study on on-line learning of NNTrees","authors":"T. Takaharu, Q. Zhao, Y. Liu","doi":"10.1109/IJCNN.2003.1223965","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223965","url":null,"abstract":"A neural network tree (NNTree) is a hybrid learning model with the overall structure being a decision tree (DT), and each nonterminal node containing a neural network (NN). Using NNTrees, it is possible to learn new knowledge online by adjusting the NNs in the nonterminal nodes. It is also possible to understand the learned knowledge online because the NNs in the nonterminal nodes are usually very small, and can be interpreted easily. Currently, we have studied retraining of the NNTrees; by adjusting the NNs in the nonterminal nodes. The structure of the trees is fixed during retraining. We found that this kind of retraining is good for size reduction in offline learning, if the training set is highly redundant. However, updating the NNs alone is not enough for online learning. In this paper, we introduce two methods for online learning of NNTrees. The first one is SGU (simple growing up), and the second one is GUWL (growing up with learning). The effectiveness of these methods are compared with each other through experiments with several public databases.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114574063","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}
{"title":"Identification of chaotic process systems with least squares support vector machines","authors":"G. T. Jemwa, C. Aldrich","doi":"10.1109/IJCNN.2003.1223726","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223726","url":null,"abstract":"We investigate the nonlinear identification of chaotic process systems with least squares support vector machines, based on a case study of a parallel cubic autocatalytic reaction. State space reconstruction techniques are used to obtain a low-dimensional representation of the system in a different, but equivalent coordinate system. The performance of the support vector machine models are compared to corresponding models obtained when using multilayer perceptron neural networks, which are known to model chaotic dynamical systems well.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114772369","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}
{"title":"An adaptive sparse distributed memory","authors":"J. Aguilar","doi":"10.1109/IJCNN.2003.1223749","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223749","url":null,"abstract":"Sparse distributed memory is a content addressable, associative memory technique which relies on close memory items tending to be clustered together, with some abstraction and blurring of details. This paper discusses the limitations of the original model. Then, we propose a method which improve Sparse Distributed Memory efficiency through an adaptive threshold. The results obtained are good and promising.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114634409","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}
{"title":"A novel approach to fault classification using sparse sets of exemplars","authors":"E. M. Laxdal, N.J. Dimopoulos","doi":"10.1109/IJCNN.2003.1223989","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223989","url":null,"abstract":"An algorithm is proposed for determining if a pattern classifier/recognizer can be developed based upon a sparse set of exemplars. Specifically, we address fault classifications issues associated with cable television distribution networks and use signatures of observed faults to train our neural networks. Our focus is to derive a training set of exemplars which will ensure that the training of a neural network classifier will result in a system capable of generalization.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117271086","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}
{"title":"Predicting protein cellular localization sites with a hardware analog neural network","authors":"S. Hohmann, J. Schemmel, F. Schurmann, K. Meier","doi":"10.1109/IJCNN.2003.1223376","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223376","url":null,"abstract":"This paper presents experimental results obtained during the training of an analog hardware neural network for the prediction of cellular localization sites of proteins in yeast and E.coli. The synaptic weights of the network are optimized by an evolutionary chip-in-the-loop algorithm. The results provide a first demonstration of the applicability of the presented neural network architecture to real-world problems that require the ability to generalize and to handle quasi-continuous multi-bit inputs.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116003403","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}
{"title":"A design of model driven cascade PID controllers using a neural network","authors":"K. Takao, T. Yamamoto, T. Hinamoto","doi":"10.1109/IJCNN.2003.1223928","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223928","url":null,"abstract":"Since most process systems have nonlinearities, it is necessary to consider the design of schemes to deal with such systems. In this paper, a new design scheme of PID controllers is proposed. This scheme is designed based on the IMC, which is a kind of the model driven controllers. The internal model consists of the design-oriented model and the full model. The full model is designed by using the neural network. The inner PID control system is first constructed for the augmented system, which is composed of the controlled subject and the internal model, and this control system is designed by the pole-assignment method. Furthermore, the outer PID controller is designed in order to remove the steady state error. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on a simulation example.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122012467","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}
{"title":"Improving data based nonlinear process modelling through Bayesian combination of multiple neural networks","authors":"Z. Ahmad, Jie Zhang","doi":"10.1109/IJCNN.2003.1223952","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223952","url":null,"abstract":"A single neural network model developed from a limited amount of data usually lacks robustness. Thus combining multiple neural networks can enhance the neural network model performance. In this paper, a Bayesian combination method is developed for nonlinear dynamic process modelling and compared with simple averaging. Instead of using fixed combination weights, the estimated probability of a particular network being the true model is used as the combination weight for combining that network. A nearest neighbour method is used in estimating the network error for a given input data point, which is then used in calculating the combination weights for individual networks. The prior probability is estimated using the SSE of individual networks on a sliding window covering the most recent sampling times. It is shown that Bayesian combination generally outperforms simple averaging.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122033683","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}
{"title":"Neural interpolator for image recognition in the process of microdevice assembly","authors":"O. Makeyev","doi":"10.1109/IJCNN.2003.1223754","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223754","url":null,"abstract":"The neural interpolator for the image recognition system is proposed. The system is developed and used for the microassembly process. The system permits us to increase the assembly process precision. A pin-to-hole insertion task was used to test the developed system.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122044720","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}
{"title":"A computational model of visual attention","authors":"T. Kohonen","doi":"10.1109/IJCNN.2003.1224092","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1224092","url":null,"abstract":"In this paper, an explanation, based on simple data analysis of the visual environment, is given to the automatic capture and focusing of visual attention. A very simple automatic selection mechanism, supposed to be at work already in the ascending visual signal paths, may underlie several familiar phenomena that are usually attributed to visual attention.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122080826","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}