Publisher：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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Publishing type：Research paper (scientific journal)   Publisher：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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Language：English   Publishing type：Research paper (scientific journal)  

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

PubMed

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Language：English   Publishing type：Research paper (scientific journal)  

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

PubMed

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Language：English   Publishing type：Research paper (scientific journal)  

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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Publishing type：Research paper (scientific journal)  

DOI： 10.3390/e20030173

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Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3055635.3056586

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：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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Other Link： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2017.html#YahataOYOKOYMT17

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：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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Other Link： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2017.html#RogovschiKGOO17

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ijcnn.2016.7727629

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：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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Other Link： https://dblp.uni-trier.de/db/conf/ssci/ssci2016.html#OzawaYKSH16

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electrical Engineers of Japan  

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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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：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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Other Link： https://dblp.uni-trier.de/db/conf/iconip/iconip2016-3.html#KitazonoGROO16

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：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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Other Link： https://dblp.uni-trier.de/db/conf/iconip/iconip2015-4.html#FurutaniKOBNS15

Language：English   Publisher：Information Processing Society of Japan  

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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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：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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Other Link： https://dblp.uni-trier.de/db/conf/inns-wc/innsbd2015.html#NishikazeOKBNS15

Language：English   Publishing type：Research paper (scientific journal)   Publisher：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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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：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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Other Link： https://dblp.uni-trier.de/db/conf/cibd/cibd2014.html#YoshidaKOSHN14

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：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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Other Link： https://dblp.uni-trier.de/db/conf/asiajcis/asiajcis2014.html#FurutaniBNSKO14

Language：Japanese   Publishing type：Research paper (scientific journal)  

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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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Language：English   Publisher：Information Processing Society of Japan  

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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Language：English   Publishing type：Research paper (scientific journal)   Publisher：The 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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J-GLOBAL

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Language：Japanese   Publisher：THE INSTITUTE OF ELECTRONICS INFORMATION AND COMMUNICATION ENGINEERS  

In this work, we propose a method to quickly discriminate DDoS backscatter packets from those of other traffic observed by darknet sensors (i.e., backscatter or non-backscatter). We define 12 features on short-time packets which are transmitted between source\/destination ports and IPs, and these traffic features are classified by Suppurt Vector Machine (SVM). As the training d

J-GLOBAL

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Language：English   Publisher：Information Processing Society of Japan (IPSJ)  

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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Language：Japanese   Publisher：The Physical Society of Japan (JPS)  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

In the present study, we propose a neural network model that generates the sparse and local excitation. Our model is based on two types of models. One is the neural network model that has Mexican-hat type interaction, and the other is the associative memory model. Our model generates the local excitation as is the case with the Mexican-hat-type network model, and can store a number of memory patterns on the local excitation as is the case with the associative memory model. The property of the system varies according to the firing rate of memory pattern in the associative memory model. We analyze the nature of our model for the case of various firing rates of the memory patterns. The analytical result shows that the sparse and local excitation in the proposed model can store a number of memory patterns only if the firing rate of the local excitation is equal to or lower than 50%.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

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 propose a new method of an exhaustive search method, instead of those method, by using the exchange Monte Carlo method.

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Language：Japanese   Publisher：Forum on Information Technology  

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Language：Japanese   Publisher：The Physical Society of Japan (JPS)  

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Other Link： http://id.nii.ac.jp/1001/00085820/

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CiNii Books

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Language：Japanese   Publisher：The Physical Society of Japan (JPS)  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

The inferior temporal cortex is known to be crucial for face recognition. Recent physiological studies have reported the existence of sparse and local excitation in the inferior temporal cortex. This sparse and local excitation is likely involved in the face recognition. In the present study, we propose a neural network model which generates the sparse and local excitation. Our model is based on two types of models. One is the neural network model that has Mexican-hat type interaction, and the other is the associative memory model. We analyze the stability of the sparse and local excitation by statistical mechanics. The analytical result shows that the sparse and local excitation in the proposed model is stable in the sparse limit.

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Language：Japanese   Publisher：The Physical Society of Japan  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Statistical methods for estimating dendritic membrane properties, including membrane resistance as a representative example, from observed noisy signals have been proposed in the last decade. Most of these methods assume membrane properties to be uniform over a dendritic tree. However, it is known that these properties are actually non-uniformly distributed, and even change steeply along a dendrite. In this study, we propose a statistical method for estimating membrane resistance distributions from observed membrane potentials, applicable to any distribution form. Dynamics of membrane potential is expressed in the cable equation, and membrane resistance distributions are expressed in the line process model. The line process model assumes a piecewise smooth distribution. Hence, even in the case where there are steep changes, our method can accurately estimate the membrane resistance distribution.

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Language：Japanese   Publisher：一般社団法人 日本物理学会  

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Language：English   Publishing type：Research paper, summary (international conference)   Publisher：ELSEVIER IRELAND LTD  

DOI： 10.1016/j.neures.2010.07.1688

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Language：Japanese   Publisher：The Physical Society of Japan (JPS)  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

An associative memory model and a neural network model with Mexican-hat type interaction are the two most typical attractor networks in the artificial neural network models. The associative memory model has discretely distributed fixed-point attractors, and realizes discrete information representation. On the other hand, the neural network model with Mexican-hat type interaction realizes continuous information representation by a line attractor, seen in working memory in the prefrontal cortex and columnar activity in the visual cortex. In the present study, we propose a neural network model which realizes discrete and continuous information representation. By analysis based on statistical mechanics, we find that the localized retrieval phase exists in the proposed model, where a memory pattern is retrieved in localized subpopulation of the network. In the localized retrieval phase, the discrete and continuous information representation is realized by orthogonality of memory patterns and neutral stability of fixed points along positions of the localized retrieval. The obtained phase diagram suggests that the anti-ferromagnetic interaction and the external field are important to generate the localized retrieval phase.

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