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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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Authorship：Lead author,　Corresponding author   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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Authorship：Lead author,　Corresponding author   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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Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/e20030173

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Authorship：Corresponding author   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)  

DOI： 10.1109/IJCNN.2017.7966046

Web of Science

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2017.html#RogovschiKGOO17

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/IJCNN.2017.7966067

Web of Science

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Other Link： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2017.html#YahataOYOKOYMT17

Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ijcnn.2016.7727629

Web of Science

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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

CiNii Books

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/SSCI.2016.7849868

Web of Science

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Other Link： https://dblp.uni-trier.de/db/conf/ssci/ssci2016.html#OzawaYKSH16

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-319-46675-0_14

Web of Science

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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)  

DOI： 10.1007/978-3-319-26561-2_45

Web of Science

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Other Link： https://dblp.uni-trier.de/db/conf/iconip/iconip2015-4.html#FurutaniKOBNS15

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1016/j.procs.2015.07.292

Web of Science

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Other Link： https://dblp.uni-trier.de/db/conf/inns-wc/innsbd2015.html#NishikazeOKBNS15

Language：English   Publisher：Information Processing Society of Japan  

DOI： 10.2197/ipsjtrans.8.25

Scopus

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

DOI： 10.3389/fnhum.2014.00480

Web of Science

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/AsiaJCIS.2014.23

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Other Link： https://dblp.uni-trier.de/db/conf/asiajcis/asiajcis2014.html#FurutaniBNSKO14

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/CIBD.2014.7011523

Web of Science

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Other Link： https://dblp.uni-trier.de/db/conf/cibd/cibd2014.html#YoshidaKOSHN14

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

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

DOI： 10.1523/JNEUROSCI.5557-12.2013

Scopus

PubMed

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Authorship：Lead author   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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Authorship：Lead author   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

CiNii Books

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Language：Japanese  

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Language：Japanese  

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Language：Japanese  

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J-GLOBAL

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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.

CiNii Books

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

CiNii Books

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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.

CiNii Books

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Language：Japanese   Publisher：Forum on Information Technology  

CiNii Books

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

CiNii Books

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Language：English  

CiNii Books

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Language：English  

CiNii Books

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

Language：English  

CiNii Books

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

CiNii Books

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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.

CiNii Books

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Language：Japanese   Publisher：The Physical Society of Japan  

CiNii Books

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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.

CiNii Books

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

CiNii Books

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Language：English   Publishing type：Research paper, summary (international conference)  

DOI： 10.1016/j.neures.2010.07.1688

Web of Science

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

CiNii Books

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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.

CiNii Books

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