出版者・発行元：Cold Spring Harbor Laboratory  

The neural mechanisms responsible for the reduction of consciousness during sleep remain elusive. Previous studies investigating macro/mesoscale neural data have suggested that functional networks are segregated into spatially localized modules, and that these modules are more segregated during sleep than during wakefulness. However, large-scale single-cell resolution functional networks remain largely unexplored. Here, we simultaneously recorded the activities of up to 10,000 cortical neurons from multiple brain regions in mice during wakefulness and sleep using a fast, single-cell resolution, and wide-field-of-view two-photon calcium imaging technique. We examined how networks were integrated or segregated between brain states in terms of modularity and spatial distribution in the cortex. We found that modularity during non-rapid eye movement sleep was higher than that during wakefulness, indicating a more segregated network. However, these modules were not spatially localized but rather intermixed across regions in both states. Our results provide novel insights into differences in the cellular-scale organization of functional networks during altered states of consciousness.

DOI： 10.1101/2023.09.14.557838

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Society for Neuroscience  

The brain is a system that performs numerous functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be controlled based on the minimization of the control cost, and which brain regions are most important to the optimal control of transitions. Despite its great potential, the current control paradigm in neuroscience uses a deterministic framework and is therefore unable to consider stochasticity, severely limiting its application to neural data. Here, to resolve this limitation, we propose a novel framework for the evaluation of control costs based on a linear stochastic model. Following our previous work, we quantified the optimal control cost as the minimal Kullback-Leibler divergence between the uncontrolled and controlled processes. In the linear model, we established an analytical expression for minimal cost and showed that we can decompose it into the cost for controlling the mean and covariance of brain activity. To evaluate the utility of our novel framework, we examined the significant brain regions in the optimal control of transitions from the resting state to seven cognitive task states in human whole-brain imaging data of either sex. We found that, in realizing the different transitions, the lower visual areas commonly played a significant role in controlling the means, while the posterior cingulate cortex commonly played a significant role in controlling the covariances.

SIGNIFICANCE STATEMENTThe brain performs many cognitive functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be optimally controlled in terms of the cost, and which brain regions are most important to the optimal control of transitions. Here, we built a novel framework to quantify control cost that takes account of stochasticity of neural activity, which is ignored in previous studies. We established the analytical expression of the stochastic control cost, which enables us to compute the cost in high-dimensional neural data. We identified the significant brain regions for the optimal control in cognitive tasks in human whole-brain imaging data.

DOI： 10.1523/jneurosci.1053-22.2022

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Where in the brain consciousness resides remains unclear. It has been suggested that the subnetworks supporting consciousness should be bidirectionally (recurrently) connected because both feed-forward and feedback processing are necessary for conscious experience. Accordingly, evaluating which subnetworks are bidirectionally connected and the strength of these connections would likely aid the identification of regions essential to consciousness. Here, we propose a method for hierarchically decomposing a network into cores with different strengths of bidirectional connection, as a means of revealing the structure of the complex brain network. We applied the method to a whole-brain mouse connectome. We found that cores with strong bidirectional connections consisted of regions presumably essential to consciousness (e.g. the isocortical and thalamic regions, and claustrum) and did not include regions presumably irrelevant to consciousness (e.g. cerebellum). Contrarily, we could not find such correspondence between cores and consciousness when we applied other simple methods that ignored bidirectionality. These findings suggest that our method provides a novel insight into the relation between bidirectional brain network structures and consciousness.

DOI： 10.1093/cercor/bhac143

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Quantifying brain state transition cost is a fundamental problem in systems neuroscience. Previous studies utilized network control theory to measure the cost by considering a neural system as a deterministic dynamical system. However, this approach does not capture the stochasticity of neural systems, which is important for accurately quantifying brain state transition cost. Here, we propose a novel framework based on optimal control in stochastic systems. In our framework, we quantify the transition cost as the Kullback-Leibler divergence from an uncontrolled transition path to the optimally controlled path, which is known as Schrödinger Bridge. To test its utility, we applied this framework to functional magnetic resonance imaging data from the Human Connectome Project and computed the brain state transition cost in cognitive tasks. We demonstrate correspondence between brain state transition cost and the difficulty of tasks. The results suggest that our framework provides a general theoretical tool for investigating cognitive functions from the viewpoint of transition cost.

DOI： 10.1162/netn_a_00213

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

An important step in understanding the nature of the brain is to identify "cores" in the brain network, where brain areas strongly interact with each other. Cores can be considered as essential sub-networks for brain functions. In the last few decades, an information-theoretic approach to identifying cores has been developed. In this approach, interactions between parts are measured by an information loss function, which quantifies how much information would be lost if interactions between parts were removed. Then, a core called a "complex" is defined as a subsystem wherein the amount of information loss is locally maximal. Although identifying complexes can be a novel and useful approach, its application is practically impossible because computation time grows exponentially with system size. Here we propose a fast and exact algorithm for finding complexes, called Hierarchical Partitioning for Complex search (HPC). HPC hierarchically partitions systems to narrow down candidates for complexes. The computation time of HPC is polynomial, enabling us to find complexes in large systems (up to several hundred) in a practical amount of time. We prove that HPC is exact when an information loss function satisfies a mathematical property, monotonicity. We show that mutual information is one such information loss function. We also show that a broad class of submodular functions can be considered as such information loss functions, indicating the expandability of our framework to the class. We applied HPC to electrocorticogram recordings from a monkey and demonstrated that HPC revealed temporally stable and characteristic complexes.

DOI： 10.1016/j.neunet.2020.08.020

PubMed

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掲載種別：研究論文（学術雑誌）  

DOI： 10.3390/e20030173

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掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ACM  

DOI： 10.1145/3055635.3056586

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

Recently, a new ICT approach to agriculture called "Smart Agriculture" has been received great attention to support farmers' decision-making for good final yield on various kinds of field conditions. For this purpose, this paper presents two image sensing methods that enable an automatic observation to capture flowers and seedpods of soybeans in real fields. The developed image sensing methods are considered as sensors in an agricultural cyber-physical system in which big data on the growth status of agricultural plants and environmental information (e.g., weather, temperature, humidity, solar radiation, soil condition, etc.) are analyzed to mine useful rules for appropriate cultivation. The proposed image sensing methods are constructed by combining several image processing and machine learning techniques. The flower detection is realized based on a coarse-to-fine approach where candidate areas of flowers are first detected by SLIC and hue information, and the acceptance of flowers is decided by CNN. In the seedpod detection, candidates of seedpod regions are first detected by the Viola-Jones object detection method, and we also use CNN to make a final decision on the acceptance of detected seedpods. The performance of the proposed image sensing methods is evaluated for a data set of soybean images that were taken from a crowd of soybeans in real agricultural fields in Hokkaido, Japan.

DOI： 10.1109/IJCNN.2017.7966067

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その他リンク： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2017.html#YahataOYOKOYMT17

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

This paper introduces a new topological clustering approach to cluster high dimensional datasets based on t-SNE (Stochastic Neighbor Embedding) dimensionality reduction method and spectral clustering. Spectral clustering method needs to construct an adjacency matrix and calculate the eigen-decomposition of the corresponding Laplacian matrix [1] which are computational expensive and is not easy to apply on large-scale data sets. One of the issue of this problem is to reduce the dimensionality befor to cluster the dataset. The t-SNE method which performs good results for visulaization allows a projection of the dataset in low dimensional spaces that make it easy to use for very large datasets. Using t-SNE during the learning process will allow to reduce the dimensionality and to preserve the topology of the dataset by increasing the clustering accuracy. We illustrate the power of this method with several real datasets. The results show a good quality of clustering results and a higher speed.

DOI： 10.1109/IJCNN.2017.7966046

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その他リンク： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2017.html#RogovschiKGOO17

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

DOI： 10.1109/ijcnn.2016.7727629

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

In recent years, with the popularization of SNS, the incidents called flaming, in which a large number of negative comments are retweeted and spread to many followers on SNS, are increasing. Since a flaming event sometimes causes severe criticism by public people, it is becoming a great thread to companies and therefore it is important for companies to protect their reputation from such flaming events. In order to protect companies from serious damages in reputation, we propose a machine learning approach to the detection of flaming events by monitoring the sentiment polarity of SNS comments. From the nature of SNS comments such as the spread of a large number of retweets with the same content for a short time, the word distributions are often strongly biased and it leads to poor performance in sentiment polarity prediction. To alleviate this problem, we introduce transfer learning into the conventional Naive Bayes classifier. More concretely, in the Naive Bayes classifier, the occurrence probabilities of words on a target domain are recalculated using those on other domains, where a domain corresponds to a company to be protected. The experimental results demonstrate that the proposed transfer learning contribute to the improvement in the sentiment polarity prediction for SNS comments. In addition, we show that the proposed system can detect flaming events correctly by monitoring the number of negative comments.

DOI： 10.1109/SSCI.2016.7849868

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その他リンク： https://dblp.uni-trier.de/db/conf/ssci/ssci2016.html#OzawaYKSH16

記述言語：日本語   掲載種別：研究論文（学術雑誌）   出版者・発行元：一般社団法人 電気学会  

In recent years, along with the popularization of SNS, the incidents, which are called <i>flaming</i>, that the number of negative comments surges are on the increase. This becomes a problem for companies because flamings hurt companies' reputation. In order to minimalize the damage of reputation, we propose the method that detects flamings by estimating the sentiment polarities of SNS comments. Because of the unique SNS characteristics such as repetition of same comments, the polarities of words are sometimes wrongly estimated. To alleviate this problem, transfer learning is introduced. In this research, the sentiment polarities of words are trained in every domain. This will enable to extract the words that are domain-specific and dictate the polarity of comments. These words are occurred in retweets. Transfer learning is implemented to non-extracted words by averaging the occurrence probabilities in other domains. These processes keep the polarities of important words that dictate the polarity of comments and modify the wrongly estimated polarities of words. The experimental results show that the proposed method improves the performance of estimating the sentiment polarity of comments. Moreover, flamings can be detected without missing by monitoring time course of the number of negative comments.

DOI： 10.1541/ieejeiss.136.340

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：SPRINGER INTERNATIONAL PUBLISHING AG  

One of the dimension reduction (DR) methods for data-visualization, t-distributed stochastic neighbor embedding (t-SNE), has drawn increasing attention. t-SNE gives us better visualization than conventional DR methods, by relieving so-called crowding problem. The crowding problem is one of the curses of dimensionality, which is caused by discrepancy between high and low dimensional spaces. However, in t-SNE, it is assumed that the strength of the discrepancy is the same for all samples in all datasets regardless of ununiformity of distributions or the difference in dimensions, and this assumption sometimes ruins visualization. Here we propose a new DR method inhomogeneous t-SNE, in which the strength is estimated for each point and dataset. Experimental results show that such pointwise estimation is important for reasonable visualization and that the proposed method achieves better visualization than the original t-SNE.

DOI： 10.1007/978-3-319-46675-0_14

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その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2016-3.html#KitazonoGROO16

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：SPRINGER INTERNATIONAL PUBLISHING AG  

This paper presents an adaptive large-scale monitoring system to detect Distributed Denial of Service (DDoS) attacks whose backscatter packets are observed on the darknet (i.e., unused IP space). To classify DDoS backscatter, 17 features of darknet traffic are defined from IPs/ports information for source and destination hosts. To adapt to the change of DDoS attacks, we newly implement an online learning function in the proposed monitoring system, where an SVM classifier is continuously trained with darknet features transformed from packets during a certain period. In the performance evaluation, we use the MWS Dataset 2014 that consists of darknet packets collected from 1st January 2014 to 28th February 2014 (8 weeks). We demonstrate that the proposed system keeps good test performance in the detection of DDoS backscatter (0.98 in F-measure).

DOI： 10.1007/978-3-319-26561-2_45

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その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2015-4.html#FurutaniKOBNS15

記述言語：英語   出版者・発行元：一般社団法人 情報処理学会  

Feature selection problem has been widely used for various fields. In particular, the sparse estimation has the advantage that its computational cost is the polynomial order of the number of features. However, it has the problem that the obtained solution varies as the dataset has changed a little. The goal of this paper is to exhaustively search the solutions which minimize the generalization error for feature selection problem to investigate the problem of sparse estimation. We calculate the generalization errors for all combinations of features in order to get the histogram of generalization error by using the cross validation method. By using this histogram, we propose a method to verify whether the given data include information for binary classification by comparing the histogram of predictive error for random guessing. Moreover, we propose a statistical mechanical method in order to efficiently calculate the histogram of generalization error by the exchange Monte Carlo (EMC) method and the multiple histogram method. We apply our proposed method to the feature selection problem for selecting the relevant neurons for face identification.

DOI： 10.2197/ipsjtrans.8.25

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ELSEVIER SCIENCE BV  

This paper presents a machine learning approach to large-scale monitoring for malicious activities on Internet. In the proposed system, network packets sent from a subnet to a darknet (i.e., a set of unused IPs) are collected, and they are transformed into 27-dimensional TAP (Traffic Analysis Profile) feature vectors. Then, a hierarchical clustering is performed to obtain clusters for typical malicious behaviors. In the monitoring phase, the malicious activities in a subnet are estimated from the closest TAP feature cluster. Then, such TAP feature clusters for all subnets are visualized on the proposed monitoring system in real time. In the experiment, we use a big data set of 303,733,994 darknet packs collected from February 1st to February 28th, 2014 (28 days) for monitoring. As a result, we can successfully detect an indication of the pandemic of a new malware, which attacked to the vulnerability of Synology NAS (port 5,000/TCP).

DOI： 10.1016/j.procs.2015.07.292

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その他リンク： https://dblp.uni-trier.de/db/conf/inns-wc/innsbd2015.html#NishikazeOKBNS15

記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：FRONTIERS MEDIA SA  

Near-infrared spectroscopy (NIRS) in psychiatric studies has widely demonstrated that cerebral hemodynamics differs among psychiatric patients. Recently we found that children with attention-deficit/hyperactivity disorder (ADHD) and children with autism spectrum disorders (ASD) showed different hemodynamic responses to their own mother's face. Based on this finding, we may be able to classify the hemodynamic data into two those groups and predict to which diagnostic group an unknown participant belongs. In the present study, we proposed a novel statistical method for classifying the hemodynamic data of these two groups. By applying a support vector machine (SVM), we searched the combination of measurement channels at which the hemodynamic response differed between the ADHD and the ASD children. The SVM found the optimal subset of channels in each data set and successfully classified the ADHD data from the ASD data. For the 24-dimensional hemodynamic data, two optimal subsets classified the hemodynamic data with 84% classification accuracy, while the subset contained all 24 channels classified with 62% classification accuracy. These results indicate the potential application of our novel method for classifying the hemodynamic data into two groups and revealing the combinations of channels that efficiently differentiate the two groups.

DOI： 10.3389/fnhum.2014.00480

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

SNS is one of the most effective communication tools and it has brought about drastic changes in our lives. Recently, however, a phenomenon called flaming or backlash becomes an imminent problem to private companies. A flaming incident is usually triggered by thoughtless comments/actions on SNS, and it sometimes ends up damaging to the company's reputation seriously. In this paper, in order to prevent such unexpected damage to the company's reputation, we propose a new approach to sentiment analysis using a Naive Bayes classifier, in which the features of tweets/comments are selected based on entropy-based criteria and an empirical rule to capture negative expressions. In addition, we propose a semi-supervised learning approach to relabeling noisy training data, which come from various SNS media such as Twitter, Facebook, blogs and a Japanese textboard called '2-channel'. In the experiments, we use four data sets of users' comments, which were posted to different SNS media of private companies. The experimental results show that the proposed Naive Bayes classifier model has good performance for different SNS media, and a semi-supervised learning effectively works for the data consisting of long comments. In addition, the proposed method is applied to detect flaming incidents, and we show that it is successfully detected.

DOI： 10.1109/CIBD.2014.7011523

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その他リンク： https://dblp.uni-trier.de/db/conf/cibd/cibd2014.html#YoshidaKOSHN14

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

In this work, we propose a method to discriminate backscatter caused by DDoS attacks from normal traffic. Since DDoS attacks are imminent threats which could give serious economic damages to private companies and public organizations, it is quite important to detect DDoS backscatter as early as possible. To do this, 11 features of port/IP information are defined for network packets which are sent within a short time, and these features of packet traffic are classified by Suppurt Vector Machine (SVM). In the experiments, we use TCP packets for the evaluation because they include control flags (e.g. SYN-ACK, RST-ACK, RST, ACK) which can give label information (i.e. backscatter or non-backscatter). We confirm that the proposed method can discriminate DDoS backscatter correctly from unknown darknet TCP packets with more than 90% accuracy.

DOI： 10.1109/AsiaJCIS.2014.23

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その他リンク： https://dblp.uni-trier.de/db/conf/asiajcis/asiajcis2014.html#FurutaniBNSKO14

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

There are two dominant models for the functional organization of brain regions underlying object recognition. One model postulates category-specific modules while the other proposes a distributed representation of objects with generic visual features. Functional imaging techniques relying on metabolic signals, such as fMRI and optical intrinsic signal imaging (OISI), have been used to support both models, but due to the indirect nature of the measurements in these techniques, the existing data for one model cannot be used to support the other model. Here, we used large-scale multielectrode recordings over a large surface of anterior inferior temporal (IT) cortex, and densely mapped stimulus-evoked neuronal responses. We found that IT cortex is subdivided into distinct domains characterized by similar patterns of responses to the objects in our stimulus set. Each domain spanned several millimeters on the cortex. Some of these domains represented faces ("face" domains) or monkey bodies ("monkey-body" domains). We also identified domains with low responsiveness to faces ("anti-face" domains). Meanwhile, the recording sites within domains that displayed category selectivity showed heterogeneous tuning profiles to different exemplars within each category. This local heterogeneity was consistent with the stimulus-evoked feature columns revealed by OISI. Taken together, our study revealed that regions with common functional properties (domains) consist of a finer functional structure (columns) in anterior IT cortex. The "domains" and previously proposed "patches" are rather like "mosaics" where a whole mosaic is characterized by overall similarity in stimulus responses and pieces of the mosaic correspond to feature columns. © 2013 the authors.

DOI： 10.1523/JNEUROSCI.5557-12.2013

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記述言語：英語   出版者・発行元：一般社団法人 情報処理学会  

With developments in optical imaging over the past decade, statistical methods for estimating dendritic membrane resistance from observed noisy signals have been proposed. In most of previous studies, membrane resistance over a dendritic tree was assumed to be constant, or membrane resistance at a point rather than that over a dendrite was investigated. Membrane resistance, however, is actually not constant over a dendrite. In a previous study, a method was proposed in which membrane resistance value is expressed as a non-constant function of position on dendrite, and parameters of the function are estimated. Although this method is effective, it is applicable only when the appropriate function is known. We propose a statistical method, which does not express membrane resistance as a function of position on dendrite, for estimating membrane resistance over a dendrite from observed membrane potentials. We use the Markov random field (MRF) as a prior distribution of the membrane resistance. In the MRF, membrane resistance is not expressed as a function of position on dendrite, but is assumed to be smoothly varying along a dendrite. We apply our method to synthetic data to evaluate its efficacy, and show that even when we do not know the appropriate function, our method can accurately estimate the membrane resistance.

DOI： 10.2197/ipsjtrans.5.186

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Physical Society of Japan  

An associative memory model and a neural network model with a Mexican-hat type interaction are two major attractor neural network models. The associative memory model has discretely distributed fixed-point attractors, and achieves a discrete information representation. On the other hand, a neural network model with a Mexican-hat type interaction uses a ring attractor to achieves a continuous information representation, which can be seen in the working memory in the prefrontal cortex and columnar activity in the visual cortex. In the present study, we propose a neural network model that achieves discrete and continuous information representation. We use a statistical–mechanical analysis to find that a localized retrieval phase exists in the proposed model, where the memory pattern is retrieved in the localized subpopulation of the network. In the localized retrieval phase, the discrete and continuous information representation is achieved by using the orthogonality of the memory patterns and the neutral stability of fixed points along the positions of the localized retrieval. The obtained phase diagram suggests that the antiferromagnetic interaction and the external field are important for generating the localized retrieval phase.

DOI： 10.1143/jpsj.78.114801

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記述言語：日本語   出版者・発行元：電子情報通信学会  

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記述言語：日本語  

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記述言語：日本語   出版者・発行元：システム制御情報学会  

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J-GLOBAL

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記述言語：日本語   出版者・発行元：電子情報通信学会  

DDoS攻撃は企業や団体に深刻な経済損害を与える脅威であり，早急にDDoS攻撃を検知することが重要である．本研究では，ダークネットセンサで観測されたパケットトラフィック情報からDDoS攻撃によるバックスキャッタであるかどうかを高速に判定する手法を提案する．短時間のパケットデータから送信元\/送信先ポートや送信元\/送信先IPなどに関連した12の特徴を抽出し，サポートベクターマシーン(SVM)を用いた分類を試みる．評価実験では，バックスキャッタか否かのラベル情報を与えることのできる80番ポートからのTCPパケットと53番ポートからのUDPパケットを用いて，これらの特徴ベクトルからDDoS攻撃によるバックスキャッタかどうかの判定を行う．

J-GLOBAL

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記述言語：英語   出版者・発行元：一般社団法人情報処理学会  

Feature selection problem has been widely used for various fields. In particular, the sparse estimation has the advantage that its computational cost is the polynomial order of the number of features. However, it has the problem that the obtained solution varies as the dataset has changed a little. The goal of this paper is to exhaustively search the solutions which minimize the generalization error for feature selection problem to investigate the problem of sparse estimation. We calculate the generalization errors for all combinations of features in order to get the histogram of generalization error by using the cross validation method. By using this histogram, we propose a method to verify whether the given data include information for binary classification by comparing the histogram of predictive error for random guessing. Moreover, we propose a statistical mechanical method in order to efficiently calculate the histogram of generalization error by the exchange Monte Carlo (EMC) method and the multiple histogram method. We apply our proposed method to the feature selection problem for selecting the relevant neurons for face identification.Feature selection problem has been widely used for various fields. In particular, the sparse estimation has the advantage that its computational cost is the polynomial order of the number of features. However, it has the problem that the obtained solution varies as the dataset has changed a little. The goal of this paper is to exhaustively search the solutions which minimize the generalization error for feature selection problem to investigate the problem of sparse estimation. We calculate the generalization errors for all combinations of features in order to get the histogram of generalization error by using the cross validation method. By using this histogram, we propose a method to verify whether the given data include information for binary classification by comparing the histogram of predictive error for random guessing. Moreover, we propose a statistical mechanical method in order to efficiently calculate the histogram of generalization error by the exchange Monte Carlo (EMC) method and the multiple histogram method. We apply our proposed method to the feature selection problem for selecting the relevant neurons for face identification.

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記述言語：日本語   出版者・発行元：一般社団法人日本物理学会  

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

メキシカンハット型のモデルと連想記憶モデルは,代表的なアトラクターネットワークである.本研究では,これら二つのアトラクターネットワークの特徴を同時に有するモデルについて,統計力学的な解析と,シミュレーションを行う.このモデルは,メキシカンハット型のモデルと同様に局在興奮を持ち,連想記憶モデルと同様に,その局在興奮中に複数の記憶パターンを記銘することが出来る.連想記憶モデルでは発火率を変えると,系の性質が著しく変化することが知られている.そこで本研究では,このモデルの記憶パターンの発火率を変化させたときの性質を調べる.その結果,発火率が50%以下の場合のみ,複数のパターンを局在興奮に記銘できることを示す.

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

高次元データの特徴を表す少数の変数を抽出する変数選択問題が,近年注目を集めている.従来手法では,最適な変数の組み合わせを,一つだけ抽出する.しかし,そのような変数の組み合わせは一つとは限らず,複数存在する最適な組み合わせを網羅的に抽出する手法が必要不可欠である.本研究では,汎化誤差をエネルギー関数としたMCMC法を実装し,汎化性能の高い変数の組み合わせを網羅的かつ効率的に抽出する手法を提案する.

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記述言語：日本語   出版者・発行元：FIT(電子情報通信学会・情報処理学会)運営委員会  

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記述言語：日本語   出版者・発行元：一般社団法人日本物理学会  

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記述言語：英語  

Feature selection in machine learning is an important process for improving the generalization capability and interpretability of learned models through the selection of a relevant feature subset. In the last two decades, a number of feature selection methods, such as L1 regularization and automatic relevance determination have been intensively developed and used in a wide range of areas. We can select a relevant subset of features, by using these feature selection methods. In this study, we apply an exhaustive search, instead of these methods, to the neural data recorded in the area of brain involved in face recognition. We evaluate how accurately every subset of recorded neurons can discriminate faces, by using SVM classifiers and cross validation. We show that there are a number of highly accurate neuron subsets. All of these results demonstrate that we should not select only one feature subset but exhaustively evaluate every feature subset.Feature selection in machine learning is an important process for improving the generalization capability and interpretability of learned models through the selection of a relevant feature subset. In the last two decades, a number of feature selection methods, such as L1 regularization and automatic relevance determination have been intensively developed and used in a wide range of areas. We can select a relevant subset of features, by using these feature selection methods. In this study, we apply an exhaustive search, instead of these methods, to the neural data recorded in the area of brain involved in face recognition. We evaluate how accurately every subset of recorded neurons can discriminate faces, by using SVM classifiers and cross validation. We show that there are a number of highly accurate neuron subsets. All of these results demonstrate that we should not select only one feature subset but exhaustively evaluate every feature subset.

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記述言語：英語   出版者・発行元：情報処理学会  

With developments in optical imaging over the past decade, statistical methods for estimating dendritic membrane resistance from observed noisy signals have been proposed. In most of previous studies, membrane resistance over a dendritic tree was assumed to be constant, or membrane resistance at a point rather than that over a dendrite was investigated. Membrane resistance, however, is actually not constant over a dendrite. In a previous study, a method was proposed in which membrane resistance value is expressed as a non-constant function of position on dendrite, and parameters of the function are estimated. Although this method is effective, it is applicable only when the appropriate function is known. We propose a statistical method, which does not express membrane resistance as a function of position on dendrite, for estimating membrane resistance over a dendrite from observed membrane potentials. We use the Markov random field (MRF) as a prior distribution of the membrane resistance. In the MRF, membrane resistance is not expressed as a function of position on dendrite, but is assumed to be smoothly varying along a dendrite. We apply our method to synthetic data to evaluate its efficacy, and show that even when we do not know the appropriate function, our method can accurately estimate the membrane resistance.With developments in optical imaging over the past decade, statistical methods for estimating dendritic membrane resistance from observed noisy signals have been proposed. In most of previous studies, membrane resistance over a dendritic tree was assumed to be constant, or membrane resistance at a point rather than that over a dendrite was investigated. Membrane resistance, however, is actually not constant over a dendrite. In a previous study, a method was proposed in which membrane resistance value is expressed as a non-constant function of position on dendrite, and parameters of the function are estimated. Although this method is effective, it is applicable only when the appropriate function is known. We propose a statistical method, which does not express membrane resistance as a function of position on dendrite, for estimating membrane resistance over a dendrite from observed membrane potentials. We use the Markov random field (MRF) as a prior distribution of the membrane resistance. In the MRF, membrane resistance is not expressed as a function of position on dendrite, but is assumed to be smoothly varying along a dendrite. We apply our method to synthetic data to evaluate its efficacy, and show that even when we do not know the appropriate function, our method can accurately estimate the membrane resistance.

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その他リンク： http://id.nii.ac.jp/1001/00085820/

記述言語：英語  

With developments in optical imaging over the past decade, statistical methods for estimating dendritic membrane resistance from observed noisy signals have been proposed. In most of previous studies, membrane resistance over a dendritic tree was assumed to be constant, or membrane resistance at a point rather than that distributed over a dendrite was investigated. Membrane resistance, however, is actually non-uniformly distributed. Although in a previous study a method was proposed in which a specific non-homogeneous distribution form was assumed, it is applicable only when the appropriate distribution form is known. We propose a statistical method, that does not assume a particular distribution form of membrane resistance, for estimating membrane resistance distribution from observed membrane potentials. We use the Markov random field (MRF) as a prior of the membrane-resistance distribution. In the MRF, any specific distribution form of membrane resistance is not assumed, but only spatial smoothness of membrane resistance is assumed. We apply our method to synthetic data to evaluate its efficacy, and show that even when we do not know the appropriate distribution form, our method can accurately estimate the membrane-resistance distribution.With developments in optical imaging over the past decade, statistical methods for estimating dendritic membrane resistance from observed noisy signals have been proposed. In most of previous studies, membrane resistance over a dendritic tree was assumed to be constant, or membrane resistance at a point rather than that distributed over a dendrite was investigated. Membrane resistance, however, is actually non-uniformly distributed. Although in a previous study a method was proposed in which a specific non-homogeneous distribution form was assumed, it is applicable only when the appropriate distribution form is known. We propose a statistical method, that does not assume a particular distribution form of membrane resistance, for estimating membrane resistance distribution from observed membrane potentials. We use the Markov random field (MRF) as a prior of the membrane-resistance distribution. In the MRF, any specific distribution form of membrane resistance is not assumed, but only spatial smoothness of membrane resistance is assumed. We apply our method to synthetic data to evaluate its efficacy, and show that even when we do not know the appropriate distribution form, our method can accurately estimate the membrane-resistance distribution.

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記述言語：日本語   出版者・発行元：一般社団法人日本物理学会  

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

これまでの研究から,側頭葉の下側頭皮質では顔に選択的に反応するニューロンの存在が知られている.下側頭皮質では,ニューロンが空間的に疎らに発火した状態にある,スパースな局在興奮の存在が報告されており,このスパースな局在興奮が下側頭皮質による顔認知に関与していると考えられている.そこで本研究で,我々は,メキシカンハット型相互作用と共分散学習型相互作用に基づいたスパースな局在興奮を実現する神経回路モデルを提案する.本研究では,この神経回路モデルの実現するスパースな局在興奮の安定性を,統計力学に基づき解析を行った.解析の結果から,記憶パターンの発火率0のスパース極限でも局在興奮が安定に存在することがわかった.

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記述言語：日本語   出版者・発行元：一般社団法人 日本物理学会  

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記述言語：日本語   出版者・発行元：一般社団法人電子情報通信学会  

近年,膜電位の観測値を元に,樹状突起上の膜抵抗の空間分布を推定する手法が提案されている.従来の多くの手法では,膜抵抗値が樹状突起上で一定であると仮定して推定している.しかしながら,実際の膜抵抗は,樹状突起上で不均一に分布しており,また,膜抵抗値が樹状突起に沿って急峻に変化する例も報告されている.そこで本研究では,任意の形の膜抵抗分布に適応可能な推定手法を提案する.提案手法では,膜電位のダイナミクスをケーブル方程式で表し,また,膜抵抗分布をラインプロセスを用いて表現する.ラインプロセスによって,膜抵抗分布が区分的に滑らかであるという制約が課される.これにより,膜抵抗分布がなだらかな変化を持つ場合や急峻な変化を持つ場合など,様々な場合において,真の膜抵抗分布を推定することが可能になる.

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記述言語：日本語   出版者・発行元：一般社団法人 日本物理学会  

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）   出版者・発行元：ELSEVIER IRELAND LTD  

DOI： 10.1016/j.neures.2010.07.1688

Web of Science

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記述言語：日本語   出版者・発行元：社団法人日本物理学会  

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記述言語：日本語   出版者・発行元：社団法人電子情報通信学会  

連想記憶モデルとメキシカンハット型相互作用をもつ神経回路モデルは,神経回路モデルにおいて二つの最も代表的なアトラクターネットワークである.連想記憶モデルでは,複数のパターンが個々のポイントアトラクターによって記憶されており,離散的な情報表現が実現される.一方,メキシカンハット型相互作用を持つ神経回路モデルでは,ワーキングメモリーや初期視覚野のコラム構造などで見られる連続的な情報表現がラインアトラクターにより実現される.本研究で,我々は,離散的情報表現と連続的情報表現の両方の性質を備えた神経回路モデルを提案する.統計力学に基づいた解析により,記憶パターンが空間的に局在して想起される局在想起相が存在することが示される.局在想起相では,想起が局在的に生じる場所に関して中立安定であるとともに,異なるパターン同士は直交しているため,提案モデルでは離散的かつ連続的な情報表現が実現されることが示される.さらに,局在想起相の出現に反強磁性相互作用と外部磁場が重要であることを示す.

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