label: url

en: A computational model for tinnitus generation and its management by sound therapy

en: International Journal of Biology and Biomedical Engineering


en: Homeostatic plasticity and spike-time-dependent plasticity in computational modeling of tinnitus generation and its management by sound therapy

ja: International Journal of Biology and Biomedical Engineering


en: Spike-time-dependent plasticity of excitation and inhibition in a neuronal network model for tinnitus relief with sound therapy


en: Inhibition of oscillation in a neural oscillator model for sound therapy of tinnitus

en: International Journal of Modelling and Simulation


en: A plastic neuronal network model with STDP for tinnitus management by sound therapy

en: International Journal of Mathematical Models and Methods in Applied Sciences


en: A Neural Oscillator Model for Tinnitus and Its Management by Sound Therapy

en: International Journal of Modern Engineering


en: A neuronal network model for tinnitus and its management by sound therapy


en: Optimization of MLP/BP for character recognition using a modified alopex algorithm

en: It is difficult to determine the recognition mechanism in the neurons of a neural network trained for pattern recognition due to the non-linear nature of neural networks. This paper describes a recognition mechanism of a four-layer back propagation neural network using Alopex algorithm. We have developed a small-scale, four-layered neural network model for simple character recognition, which can recognize the patterns transformed by affined conversion. Alopex algorithm is an iterative and stochastic processing method, which was proposed for optimization of a given cost function. In this case the receptive fields of the neurons in the output layers are obtained using the Alopex algorithm.

ja: Alopex法を用いた4層ニューラルネットワーク認識機構の解明について検討している．位置ずれ，傾きのある文字画像認識のための小規模な4層ニューラルネットワークを構築し，Alopex法を用いて各出力層ユニットの受容野の解析を行った．

en: International Journal of Knowlrge-based and Intelligent Engineering Systems


en: GMDH-type Neural Network Algorithm with a Feedback Loop for Structural Identification of RBF Neural Network

ja: International Journal of Knowlrge-based and Intelligent Engineering Systems


en: A Plastic Neural Network Model of Sound Therapy of Tinnitus

en: IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


en: Tracking the states of a nonlinear and nonstationary system in the weight-space of artificial neural networks

en: The new proposed method uses the connection-weight-space (CWS) of the neural network(NN) to extract information about the signal generator mechanism. We define a novel feature to measure the separation of two arbitrary states in the CWS and use it to track the state changes of the generating system. The performance of the method is examined via synthetic signals and clinical EEG. Synthetic data indicates that the feature can track the system down to a SNR of 3.5 dB. Clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled by the proposed method, and the blood perfusion state of the brain could be monitored via feature, with small NNs.

ja: 信号を生成するシステムをモデルしたニューラルネットワーク(NN)の結合荷重空間において，2つの状態を分離する方法を提案する．提案法はSNR=3.5dBの環境下でさえ，システムの状態を捉える事が示唆された．提案法を用いて，動脈内膜切除中のEEG信号をモデルすることにより，血液かん流の状態をモニタリングできることが分かった．

en: Medical and Biological Engineering and Computing


en: Brain source localization for two dipoles using a combined method from EEG topography

en: Estimating the correct location of electric current source with the brain from electroencephalographic (EEG) recordings is a challenging analytic and computational problem. Specifically, there is no unique solution and solutions do not depend continuously on the data. This is an inverse problem from EEG to dipole source. In this paper we consider a method combining backpropagation neural network (BPNN) with nonlinear least square (NLS) method for source localization. For inverse problem, the BP neural network and the NLS method has its own advantage and disadvantage, so we use the BPNN to supply the initial value to the NLS method and then get the final result, here we select the Powell algorithm to do the NLS calculating. All these work are for the fast and accurate dipole source localization. The main purpose of using this combined method is to localize two dipole sources when they are locating at the same region of the brain. The following investigations are presented to show that this combined method used in this paper is an advanced approach for two dipole sources localization with high accuracy and fast calculating.

ja: BPNNとNLS法を組み合わせた信号源推定法について述べている．BPNNはNLS法への適切な初期値を設定するために利用している．シミュレーションの結果，有用であることが確認された

en: GESTS International Transactions on Computer Science and Engineering


en: Accuracy of single dipole source localization by BP neural networks from 18-channel EEGs

en: A problem of estimating biopotential sources in the brain based on EEG signals observed on the scalp is known as an important inverse problem of electrophysiology. Usually there is no closed-form solution for this problem and it requires iterative techniques such as the Levenberg-Marquardt algorithm. Considering the nonlinear properties of inverse problem, and signal to noise ratio inherent in EEG signals, a back propagation neural network has been recently proposed as a solution. In this paper, we investigated the properties of neural networks and its localization accuracy for single dipole source localization. Based on the results of extensive studies, we concluded the neural networks are highly feasible in single-source localization with a small number of electrodes (18 electrodes), also examined the usefulness of this method for clinical application with a case of epilepsy.

en: IEICE Transactions on Information and Systems


en: Oscillatory modes in a neuronal network model with transmission latency

en: Neurocomputing

ja: Neurocomputing


en: Measurement system of jaw movements by using BP neural networks method and a nonlinear least-squares method

en: The jaw movements can be measured by estimating the position and orientation of two small permanent magnets attached on the upper and lower jaws. It is a difficult problem to estimate the positions and orientations of the magnets from magnetic field because it is a typical inverse problem. The back propagation neural networks (BPNN) are applicable to solve this problem in short processing time. But its precision is not enough to apply to practical measurement. In the other hand, precise estimation is possible by using the nonlinear least-square (NLS) method. However, it takes long processing time for iterative calculation, and the solutions may be trapped in the local minima. In this paper, we propose a precise and fast measurement system which makes use of the estimation algorithm combining BPNN with NLS method. In this method, the BPNN performs an approximate estimation of magnet parameters in short processing time, and its result is used as the initial value of iterative calculation of NLS method. The cost function is solved by Gauss-Newton iteration algorithm. Precision, processing time and noise immunity were examined by computer simulations. These results shows the proposed system has satisfactory ability to be applied to practical measurement.

ja: 2個の小型磁石を上下顎に取り付け，周辺の磁界分布から顎運動を推定するのは困難である．本研究では非線形最小二乗法とニューラルネットワークを組み合わせることで，高精度，高速な測定が可能なシステムを提案し，計算機シミュレーションにより推定制度について検討している．

ja: IEICE Transactions on Information and Systems


en: Phase-locked oscillations in a neuronal network model


en: Integrated EEG monitoring system of brain functions during operations

en: In some cases, it is necessary to occlude the blood flow in a part of the brain during the surgical procedure. In such cases, it is very important to monitor brain functions, and to do appropriate treatment in order to minimize the postoperative complication. At present, several kinds of equipment specialized to monitor single modality are being used simultaneously. The aim of this study was to develop an EEG monitoring system which integrates various analyzing methods. It can indicate not only single modality information but also multi modality. In addition, the system has great advantages. It can provide complex diagnostic information by synthesizing the results of the multi modality analysis. The system consists of data acquisition, waveform memory management, and data analysis parts. To add a new analyzing method, a corresponding module was added in this part. This system can be used without difficulty because it is composed of simple modules. The first experimental system with the above architecture was made.

ja: 本研究では様々解析手法を統合することができるEEGモニタリングシステムの開発を目的としている．様々なモダリティを表示することができ，その診断結果を提示することが可能となる．本システムはデータ取得モジュール，波形メモリモジュール，データ解析モジュールから成り立っており，モジュール構造のため機能の追加が容易である．このようなアーキテクチャを持つ試作機を製作した．

en: International Congress Series

ja: International Congress Series


en: BP neural networks approach for identifying biological signal source in circadian data fluctuations

en: Almost all land animals coordinate their behavior with circadian rhythms, matching their functions to the daily cycles of lightness and darkness that result from the rotation of the earth corresponding to 24 hours. Through external stimuli, such as dairy life activities or other sources from our environment may influence the internal rhythmicity of sleep and waking properties. However, the rhythms are regulated to keep their activity constant by homeostasis while fluctuating by incessant influences of external forces. A modeling study has been developed to identify homeostatic dynamics properties underlying a circadian rhythm activity of Sleep and Wake data measured from normal subjects, using an MA (Moving Average) model associated with Backpropagation (BP) algorithm. As results, we found that the neural network can capture the regularity and irregularity components included in the data. The order of MA neural network model depends on subjects behavior, the first two orders are usually dominant in the case of no strong external forces. The adaptive dynamic changes are evaluated by the change of weight vectors, a kind of internal representation of the trained network. The dynamic is kept in a steady state for more than 20 days at most. Identified properties reflect the subject's behavior, and hence may be useful for medical diagnoses of disorders related to circadian rhythms.

ja: 移動平均型BPニューラルネットワーク(MA-BPNN)を用いて生体の恒常性の動態を明らかにすることを試みている．健常者の覚醒-睡眠リズムを記録したところ，外的要因によって撹乱を受けても恒常性により安定化する性質があることがわかった．MABPNNは信号に含まれる規則性を獲得することができる．MABPNNの次数は被験者によって変わる．適応的に変化している恒常性の動態を，MABPNNの結合荷重を変化により評価した．動態はほとんどの場合10から20日間定常状態が保たれていた．


en: Magnetic measurement system for mandibular movement

ja: 磁界を用いた下顎運動の計測システム

en: Magnetic measurement system using a method combining BPNN (back propagation neural networks) with NLS (nonlinear least-squares) is described in this paper. The mandibular movements can be measured by estimating the position and orientation of two small permanent magnets attached on the maxilla and mandible separately. Although a BPNN method can solve this typical inverse problem fast enough for real time measurement, the accuracy may be not local minima of an error function or be not converged. The authors propose a method combining BPNN with NLS. The new methods shows how to estimate an approximate solution of the inverse problem by the BPNN method, and how to select the initial value of the NLS method due to the results of BPNN to obtain the optimum solution, where the problem is solved by Gauss-Newton iteration algorithm. This system is proven to be accurate and fast enough for the application to diagnosis by a simulation for the practical mandibular movements.

ja: 下顎運動計測を目的とし，下顎および上顎にそれぞれ装着された2個の磁石の磁界分布から，その位置と方向を誤差逆伝播ニューラルネットワーク(BPNN)と非線形最小2乗(NLS)法を組み合わせて測定する磁気計測システムを提案した．本計測は逆問題の一種であるが，BPNNの高速性，NLS法の高精度性を利用し実用的な精度を実現することができることを計算機シミュレーションにより示した．

en: The Journal of the Japanese Society of Magnetic Applications in Dentistry

ja: 日本磁気歯科学会雑誌


en: A neural network model for quadruped gait generation and transitions


en: Identification of biological signal sources for circadian and cardiac cycle rhythms using BP neural networks

en: Biological oscillatory activity in neural networks has been intensively studied over the past years. Neuronal oscillations are the basis of many different behavioral patterns and sensory mechanism. Understanding the dynamic properties of these mechanisms is useful for analyses of biological functions and medical diagnoses. The dynamic characteristics of wake sleep circadian rhythms and ECG's cardiac cycle data measured for normal subjects are identified here, using MA BP neural network model. It was found that dynamics of regular components can be captured by the model. The captured dynamics are kept in a steady state for some periods. The order of the MA neural network was suppressively controlled by the first 2∼3 orders. Hence it may be useful for medical diagnoses of circadian rhythms and heart related diseases.

en: Kybernetes

ja: Kybernetes


en: Synchronization with a periodic pulse train in an asymmetrically coupled neuronal network model


en: Identification of homeostatic dynamics for a circadian signal source using BP neural networks

en: System identification of biological signal sources is useful for analyses of biological functions and medical diagnoses. Homeostatic dynamics of circadian rhythms are identified here by using an MA model composed of BP neural networks. Wake-sleep rhythms measured for normal subjects are supplied for the identification. The rhythms are regulated to keep their activity constant by homeostasis, while fluctuating by incessant influences of external forces. The neural networks can acquire the regularity included in the data. The order of the MA neural network model depends on subjects, but the first three orders are dominant, usually in the case of no strong external force. The adaptive change of the homeostatic dynamics can be evaluated by the change of weight vectors, a kind of internal representation of the trained network. The dynamics are kept in a steady state for ten to 20 days at most. Identified properties reflect each subject, and hence may be useful for medical diagnoses of disorders related to circadian rhythms.

ja: 生体信号からのシステム同定は整体システムの解析や診断に有用である．本稿では移動平均型BPニューラルネットワーク(MA-BPNN)を用いて生体の恒常性の動態を明らかにすることを試みている．健常者の覚醒-睡眠リズムを記録したところ，外的要因によって撹乱を受けても恒常性により安定化する性質があることがわかった．MABPNNは信号に含まれる規則性を獲得することができる．MABPNNの次数は被験者によって変わるが，直前3点の入力が支配的である．適応的に変化している恒常性の動態を，MABPNNの結合荷重を変化により評価した．動態はほとんどの場合10から20日間定常状態が保たれていた．今回秋赤にされた特徴はそれぞれの被験者を表すものであり，概日リズムに関連する疾患の医学的診断に有用であると期待される．

en: ITBM-RBM (Innovation and Technology in Biology and Medicine)


en: A coupled neural oscillator model for recruitment and annihilation of the degrees of freedom of oscillatory movements

en: Neurocomputing


en: Entrainment of a reciprocal inhibition neural network model to a periodic pulse train

en: Computational Neuroscience: Trends in Research


en: Accuracy of neural network localization based on 18 electrodes EEG in a 10-20 system

en: Brain Topography Today

ja: Brain Topography Today


en: Entrainment of a spiking neuron model to two periodic pulse trains

en: Computational Neuroscience, Trends in Research


en: Dipole source localization of MEG by BP neural networks

en: The purpose of this study was to examine the usefulness of BP neural network for source localization of MEG. Since the performance of this method does not depend on the complexity of brain parameters and source models, a homogeneous brain model and a single current dipole source are assumed for convenience. Localization accuracy was examined in relation to the configuration and scale of the network. As a result, average error for position and moment estimations was within 2% while the maximum error was about 5%. It was therefore concluded that the neural network method, was useful for MEG source localization, though some improvements are still necessary.

en: Brain Topography

ja: Brain Topography


en: Control of oscillation in a plastic neural oscillator

en: Neural Network World

ja: Neural Network World