Pub Date : 2002-11-18DOI: 10.1109/ICONIP.2002.1199030
A. Galushkin, V. S. Zlobin, S. V. Korobkova, E.I. Rjabtsev, N. Tomashevich, E.P. Tumoian
The development of the system of recognition of the different contagions, including tuberculosis contageons, is conducted in the Scientific Center of Neurocomputers. In this work some algorithms of tuberculosis contageons (Koch's bacillus) identification are presented. They were designed in the Scientific Center of Neurocomputers RACS in the context of solving this task. The description of Express algorithm and the algorithms of neural network processing of the images and the results are adduced.
{"title":"Neurocomputer processing of the images in the task of tuberculosis contageons identification","authors":"A. Galushkin, V. S. Zlobin, S. V. Korobkova, E.I. Rjabtsev, N. Tomashevich, E.P. Tumoian","doi":"10.1109/ICONIP.2002.1199030","DOIUrl":"https://doi.org/10.1109/ICONIP.2002.1199030","url":null,"abstract":"The development of the system of recognition of the different contagions, including tuberculosis contageons, is conducted in the Scientific Center of Neurocomputers. In this work some algorithms of tuberculosis contageons (Koch's bacillus) identification are presented. They were designed in the Scientific Center of Neurocomputers RACS in the context of solving this task. The description of Express algorithm and the algorithms of neural network processing of the images and the results are adduced.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133992516","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}
Pub Date : 2002-11-18DOI: 10.1109/ICONIP.2002.1198194
Z.H. Zhang, C. Ong, S. Keerthi, E.G. Gilbert
The paper presents a new approach to determine the stability region for constrained dynamical systems. Our approach employs support vector machines (SVMs), a promising new tool for pattern recognition, to this field. By this application, the determination of stability region becomes a typical two-class hard margin pattern recognition problem, rather than the characterizations of the boundaries of such stability regions. In the underlying analysis, a program has been developed to generate critical points in the state space and train them by SVMs. Some examples are given to show the obtained estimates are close approximations of the exact stability region.
{"title":"Using support vector machines for stability region determination","authors":"Z.H. Zhang, C. Ong, S. Keerthi, E.G. Gilbert","doi":"10.1109/ICONIP.2002.1198194","DOIUrl":"https://doi.org/10.1109/ICONIP.2002.1198194","url":null,"abstract":"The paper presents a new approach to determine the stability region for constrained dynamical systems. Our approach employs support vector machines (SVMs), a promising new tool for pattern recognition, to this field. By this application, the determination of stability region becomes a typical two-class hard margin pattern recognition problem, rather than the characterizations of the boundaries of such stability regions. In the underlying analysis, a program has been developed to generate critical points in the state space and train them by SVMs. Some examples are given to show the obtained estimates are close approximations of the exact stability region.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131768933","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}
Pub Date : 2002-11-18DOI: 10.1109/ICONIP.2002.1198181
Jiaxiang Bi
We describe and discuss the coding method and self-organizing process of the information in the brain, advance a kind of encoding system: the "natural encoding system" which is particular to nature itself. In the problem of machine imitation, we further discuss the topological property equivalence problem of artificial and natural intelligence system on the basis of hierarchy structures of the virtual machine. More specifically we present the three large hierarchy structures of the intelligence system: the physical hierarchy, the physiological hierarchy and the psychological hierarchy. We also describe the relations between the three large hierarchies and some sub-stratums, the encoding method of information, the holographic frame structure of the information, the functions of the virtual machine system on different levels, etc. in more detail.
{"title":"Research about holographic relation and topological structure for thinking process on brain and artificial intelligence system","authors":"Jiaxiang Bi","doi":"10.1109/ICONIP.2002.1198181","DOIUrl":"https://doi.org/10.1109/ICONIP.2002.1198181","url":null,"abstract":"We describe and discuss the coding method and self-organizing process of the information in the brain, advance a kind of encoding system: the \"natural encoding system\" which is particular to nature itself. In the problem of machine imitation, we further discuss the topological property equivalence problem of artificial and natural intelligence system on the basis of hierarchy structures of the virtual machine. More specifically we present the three large hierarchy structures of the intelligence system: the physical hierarchy, the physiological hierarchy and the psychological hierarchy. We also describe the relations between the three large hierarchies and some sub-stratums, the encoding method of information, the holographic frame structure of the information, the functions of the virtual machine system on different levels, etc. in more detail.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134224878","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}
Pub Date : 2002-11-18DOI: 10.1109/ICONIP.2002.1198156
O. Hoshino, K. Mitsunaga, M. Miyamoto, K. Kuroiwa
By simulating a neural network model we investigated roles of background spectral components of vowel sounds in the neuronal representation of vowel sounds. The model consists of two networks, by which vowel sounds are processed in a hierarchical manner. The first network, which is tonotopically organized, detects spectral peaks called first and second formant frequencies (F1 and F2). The second network has a tonotopic two-dimensional structure and receives input from the first network in a convergent manner. The second network detects the combinatory information of the first (F1) and second (F2) formant frequencies of vowel sounds. We trained the model with five Japanese vowels spoken by different people and modified synaptic connection strengths of the second network according to the Hebbian learning rule, by which relevant dynamic cell assemblies expressing categories of vowels were organized. We show that for creating the dynamic cell assemblies background components around two-formant peaks (F1, F2) are not necessary but advantageous for the creation of the cell assemblies.
{"title":"Dynamic cell assemblies and vowel sound categorization","authors":"O. Hoshino, K. Mitsunaga, M. Miyamoto, K. Kuroiwa","doi":"10.1109/ICONIP.2002.1198156","DOIUrl":"https://doi.org/10.1109/ICONIP.2002.1198156","url":null,"abstract":"By simulating a neural network model we investigated roles of background spectral components of vowel sounds in the neuronal representation of vowel sounds. The model consists of two networks, by which vowel sounds are processed in a hierarchical manner. The first network, which is tonotopically organized, detects spectral peaks called first and second formant frequencies (F1 and F2). The second network has a tonotopic two-dimensional structure and receives input from the first network in a convergent manner. The second network detects the combinatory information of the first (F1) and second (F2) formant frequencies of vowel sounds. We trained the model with five Japanese vowels spoken by different people and modified synaptic connection strengths of the second network according to the Hebbian learning rule, by which relevant dynamic cell assemblies expressing categories of vowels were organized. We show that for creating the dynamic cell assemblies background components around two-formant peaks (F1, F2) are not necessary but advantageous for the creation of the cell assemblies.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130332703","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}
Pub Date : 2002-11-18DOI: 10.1109/ICONIP.2002.1198952
K. Kitano, T. Fukai
The intrinsic properties of 'synfire chain', the feedforward network propagating synchronous spike packets, has been studied so far. Possible functional roles of the synfire chain, however, has been poorly understood. Considering that coordinated activities of multiple synfire chains can serve as a reference time, we study whether a network model based on the multiple synfire chains contributes to generation of predictive synchrony to occurrence times of external events, observed in the primary motor cortex. In our model, neurons that code occurrence times of external events are partly innervated by the multiple synfire chains. The event times are embedded into the synaptic projections between layers that coincide with the events and event coding neurons through spike-timing-dependent synaptic learning. From our simulation results, it is found that our model can generate the predictive synchrony when the ratio of the projections is within a suitable range.
{"title":"A multiple synfire-chain model for the predictive synchrony in the motor-related cortical areas","authors":"K. Kitano, T. Fukai","doi":"10.1109/ICONIP.2002.1198952","DOIUrl":"https://doi.org/10.1109/ICONIP.2002.1198952","url":null,"abstract":"The intrinsic properties of 'synfire chain', the feedforward network propagating synchronous spike packets, has been studied so far. Possible functional roles of the synfire chain, however, has been poorly understood. Considering that coordinated activities of multiple synfire chains can serve as a reference time, we study whether a network model based on the multiple synfire chains contributes to generation of predictive synchrony to occurrence times of external events, observed in the primary motor cortex. In our model, neurons that code occurrence times of external events are partly innervated by the multiple synfire chains. The event times are embedded into the synaptic projections between layers that coincide with the events and event coding neurons through spike-timing-dependent synaptic learning. From our simulation results, it is found that our model can generate the predictive synchrony when the ratio of the projections is within a suitable range.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130382198","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}
Pub Date : 2002-11-18DOI: 10.1109/ICONIP.2002.1198169
F. Ishida, Y. Kuramoto, Y. Sawada
We report the results of analysis of a phase model for explaining the transition observed at 0.6 Hz between the position matching and rhythm matching modes in human hand-tracking. The present model is derived from a delayed feedforward model which we recently proposed to understand proactive human percepto-motor control. The present model not only reproduced the systematic phase-lead of the hand motion with respect to the target motion in a finite frequency range but also the transition in the tracking modes from position-matching to rhythm-matching at a critical frequency of the target motion experimentally observed in human hand-tracking tasks.
{"title":"Phase equation model for the transition between position matching to rhythm matching in hand tracking task","authors":"F. Ishida, Y. Kuramoto, Y. Sawada","doi":"10.1109/ICONIP.2002.1198169","DOIUrl":"https://doi.org/10.1109/ICONIP.2002.1198169","url":null,"abstract":"We report the results of analysis of a phase model for explaining the transition observed at 0.6 Hz between the position matching and rhythm matching modes in human hand-tracking. The present model is derived from a delayed feedforward model which we recently proposed to understand proactive human percepto-motor control. The present model not only reproduced the systematic phase-lead of the hand motion with respect to the target motion in a finite frequency range but also the transition in the tracking modes from position-matching to rhythm-matching at a critical frequency of the target motion experimentally observed in human hand-tracking tasks.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115336198","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}
Pub Date : 2002-11-18DOI: 10.1109/ICONIP.2002.1202797
J. Torreão
Binocular disparities arise from the positional differences of scene features projected in the two retinae. Disparity-selective neurons are known to exist in several areas of the visual cortex of cats and monkeys, and have been associated with mechanisms of gaze stabilization and stereoscopic depth perception. Such neurons appear with different response profiles, leading to their classification as tuned excitatory, tuned inhibitory, tuned near, tuned far, and reciprocal (near and far) neurons. Here we propose an analytical model for the shape of these disparity selectivity curves, showing that they can be approximated as either the Green's function or the homogeneous solution to a second-order differential equation derived from a signal matching constraint. This means that the mathematical solution to the matching problem involves functions which are similar in shape to the selectivity profiles of the binocular neurons.
{"title":"An analytical model for the disparity selectivity profiles of binocular neurons","authors":"J. Torreão","doi":"10.1109/ICONIP.2002.1202797","DOIUrl":"https://doi.org/10.1109/ICONIP.2002.1202797","url":null,"abstract":"Binocular disparities arise from the positional differences of scene features projected in the two retinae. Disparity-selective neurons are known to exist in several areas of the visual cortex of cats and monkeys, and have been associated with mechanisms of gaze stabilization and stereoscopic depth perception. Such neurons appear with different response profiles, leading to their classification as tuned excitatory, tuned inhibitory, tuned near, tuned far, and reciprocal (near and far) neurons. Here we propose an analytical model for the shape of these disparity selectivity curves, showing that they can be approximated as either the Green's function or the homogeneous solution to a second-order differential equation derived from a signal matching constraint. This means that the mathematical solution to the matching problem involves functions which are similar in shape to the selectivity profiles of the binocular neurons.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114381856","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}
Pub Date : 2002-11-18DOI: 10.1109/ICONIP.2002.1202120
Jagath Rajapakse, Kutllhiko Fukushima, Soo-Young Lee, Xin Yao Editors
NeuroLab 2003 is an expert simulation system that helps the laboratory experimenter design better experiments while accounting for multiple circuit levels in the brain. The levels make network connections (i.e., any level connected to any other level) as well as hierarchical relations between adjacent levels. The experimenter is required to become thoroughly prepared through multiple hypotheses discussions and brainstorming sessions with senior advisors and collaborators. This preparation is necessary for the proper utilization of NeuroLab's extensive expert system variations. NeuroLab 2003 provides a systematic means to conduct multi-tiered, alternative hypotheses testing and to clarify relations between neural levels.
{"title":"Neurolab 2003: a simulator that produces biologically-based experimental alternatives that aid in designing detailed experiments","authors":"Jagath Rajapakse, Kutllhiko Fukushima, Soo-Young Lee, Xin Yao Editors","doi":"10.1109/ICONIP.2002.1202120","DOIUrl":"https://doi.org/10.1109/ICONIP.2002.1202120","url":null,"abstract":"NeuroLab 2003 is an expert simulation system that helps the laboratory experimenter design better experiments while accounting for multiple circuit levels in the brain. The levels make network connections (i.e., any level connected to any other level) as well as hierarchical relations between adjacent levels. The experimenter is required to become thoroughly prepared through multiple hypotheses discussions and brainstorming sessions with senior advisors and collaborators. This preparation is necessary for the proper utilization of NeuroLab's extensive expert system variations. NeuroLab 2003 provides a systematic means to conduct multi-tiered, alternative hypotheses testing and to clarify relations between neural levels.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117073565","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}
Pub Date : 2002-11-18DOI: 10.1109/ICONIP.2002.1198148
Tang Zhe, S. H. Lian, S. Zeng-qi
Omni-directional catadioptric vision system has been used increasingly in RoboCup(Robot World Cup) medium-sized league for localization. Omni-directional vision system can greatly increase the view range of robot. The main disadvantage of this vision system is the image distortion. This paper proposed an efficient method, using an Artificial Neural Network, to localize the robot in the field based on the distorted video image captured by mobile robot.
{"title":"A neural network based localization from distorted image in omnidirectional catadioptric vision system","authors":"Tang Zhe, S. H. Lian, S. Zeng-qi","doi":"10.1109/ICONIP.2002.1198148","DOIUrl":"https://doi.org/10.1109/ICONIP.2002.1198148","url":null,"abstract":"Omni-directional catadioptric vision system has been used increasingly in RoboCup(Robot World Cup) medium-sized league for localization. Omni-directional vision system can greatly increase the view range of robot. The main disadvantage of this vision system is the image distortion. This paper proposed an efficient method, using an Artificial Neural Network, to localize the robot in the field based on the distorted video image captured by mobile robot.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123326623","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}
Pub Date : 2002-11-18DOI: 10.1109/ICONIP.2002.1198970
A. Kharlamov, V. Raevsky
In the report a model of associative processor with memory (APM) is considered. APM is a structural unit of the network consisting of neuron-like elements (NE) with temporal summation of input signals. APM realizes associative mapping of input information into signal space. This representation preserves the topology of perceptual space. On the basis of APM it is possible to build hierarchical structure for structural processing of the information.
{"title":"Associative processor with memory based on neuron-like elements capable of temporal summation of input signals: processor for structural processing of information","authors":"A. Kharlamov, V. Raevsky","doi":"10.1109/ICONIP.2002.1198970","DOIUrl":"https://doi.org/10.1109/ICONIP.2002.1198970","url":null,"abstract":"In the report a model of associative processor with memory (APM) is considered. APM is a structural unit of the network consisting of neuron-like elements (NE) with temporal summation of input signals. APM realizes associative mapping of input information into signal space. This representation preserves the topology of perceptual space. On the basis of APM it is possible to build hierarchical structure for structural processing of the information.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123597843","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}
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