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Sasaki H.,University of Electro - Communications | Gutmann M.U.,University of Helsinki | Shouno H.,University of Electro - Communications | Hyvarinen A.,University of Helsinki | Hyvarinen A.,ATR Cognitive Mechanisms Laboratories
Journal of Machine Learning Research | Year: 2014

The statistical dependencies which independent component analysis (ICA) cannot remove often provide rich information beyond the ICA components. It would be very useful to estimate the dependency structure from data. However, most models have concentrated on higher-order correlations such as energy correlations, neglecting linear correlations. Linear correlations might be a strong and informative form of a dependency for some real data sets, but they are usually completely removed by ICA and related methods, and not analyzed at all. In this paper, we propose a probabilistic model of non-Gaussian components which are allowed to have both linear and energy correlations. The dependency structure of the components is explicitly parametrized by a parameter matrix, which defines an undirected graphical model over the latent components. Furthermore, the estimation of the parameter matrix is shown to be particularly simple because using score matching, the objective function is a quadratic form. Using artificial data, we demonstrate that the proposed method is able to estimate non-Gaussian components and their dependency structures, as it is designed to do. When applied to natural images and outputs of simulated complex cells in the primary visual cortex, novel dependencies between the estimated features are discovered.

Manto M.,Unite dEtude du Mouvement UEM | Bower J.M.,University of Texas Health Science Center at San Antonio | Conforto A.B.,University of Sao Paulo | Conforto A.B.,Instituto Israelita Of Ensino E Pesquisa Albert Einstein | And 22 more authors.
Cerebellum | Year: 2012

Considerable progress has been made in developing models of cerebellar function in sensorimotor control, as well as in identifying key problems that are the focus of current investigation. In this consensus paper, we discuss the literature on the role of the cerebellar circuitry in motor control, bringing together a range of different viewpoints. The following topics are covered: oculomotor control, classical conditioning (evidence in animals and in humans), cerebellar control of motor speech, control of grip forces, control of voluntary limb movements, timing, sensorimotor synchronization, control of corticomotor excitability, control of movement-related sensory data acquisition, cerebro-cerebellar interaction in visuokinesthetic perception of hand movement, functional neuroimaging studies, and magnetoencephalographic mapping of cortico-cerebellar dynamics. While the field has yet to reach a consensus on the precise role played by the cerebellum in movement control, the literature has witnessed the emergence of broad proposals that address cerebellar function at multiple levels of analysis. This paper highlights the diversity of current opinion, providing a framework for debate and discussion on the role of this quintessential vertebrate structure. ©Springer Science+Business Media, LLC 2011.

Gurbuz S.,Universal Communication Research Institute | Oztop E.,ATR Cognitive Mechanisms Laboratories | Oztop E.,NICT Advanced ICT Research Institute | Inoue N.,Universal Communication Research Institute
Pattern Recognition | Year: 2012

In this paper we present a stereovision based model free 3D head pose (orientation and position) estimation system suitable for humanmachine interface applications. The system works by obtaining a 'face plane' from the 3D reconstructed face data, which is then used for head pose estimation. The key novelty in this work is the utilization of the face plane together with the eye locations on the reconstructed face data to obtain a robust head pose estimate. This approach leads to a model and initialization free head pose estimation system; therefore it is suitable for natural humanmachine interfaces. In order to quantitatively asses the accuracy of the system for such applications, several evaluation experiments were conducted using a commercial motion capture system. The evaluation results indicate that this system can be used in humancomputer and humanrobot applications. © 2011 Elsevier Ltd. All rights reserved.

Ugur E.,Biological ICT group | Ugur E.,Middle East Technical University | Ugur E.,ATR Cognitive Mechanisms Laboratories | Oztop E.,Biological ICT group | And 2 more authors.
Robotics and Autonomous Systems | Year: 2011

In this paper, we show that through self-interaction and self-observation, an anthropomorphic robot equipped with a range camera can learn object affordances and use this knowledge for planning. In the first step of learning, the robot discovers commonalities in its action-effect experiences by discovering effect categories. Once the effect categories are discovered, in the second step, affordance predictors for each behavior are obtained by learning the mapping from the object features to the effect categories. After learning, the robot can make plans to achieve desired goals, emulate end states of demonstrated actions, monitor the plan execution and take corrective actions using the perceptual structures employed or discovered during learning. We argue that the learning system proposed shares crucial elements with the development of infants of 710 months age, who explore the environment and learn the dynamics of the objects through goal-free exploration. In addition, we discuss goal emulation and planning in relation to older infants with no symbolic inference capability and non-linguistic animals which utilize object affordances to make action plans. © 2011 Elsevier B.V. All rights reserved.

Takeda Y.,ATR Neural Information Analysis Laboratories | Yamanaka K.,Showa Womens University | Yamagishi N.,ATR Cognitive Mechanisms Laboratories | Yamagishi N.,Japan National Institute of Information and Communications Technology | And 2 more authors.
PLoS ONE | Year: 2014

Brain activities related to cognitive functions, such as attention, occur with unknown and variable delays after stimulus onsets. Recently, we proposed a method (Common Waveform Estimation, CWE) that could extract such brain activities from magnetoencephalography (MEG) or electroencephalography (EEG) measurements. CWE estimates spatiotemporal MEG/EEG patterns occurring with unknown and variable delays, referred to here as unlocked waveforms, without hypotheses about their shapes. The purpose of this study is to demonstrate the usefulness of CWE for cognitive neuroscience. For this purpose, we show procedures to estimate unlocked waveforms using CWE and to examine their role. We applied CWE to the MEG epochs during Go trials of a visual Go/NoGo task. This revealed unlocked waveforms with interesting properties, specifically large alpha oscillations around the temporal areas. To examine the role of the unlocked waveform, we attempted to estimate the strength of the brain activity of the unlocked waveform in various conditions. We made a spatial filter to extract the component reflecting the brain activity of the unlocked waveform, applied this spatial filter to MEG data under different conditions (a passive viewing, a simple reaction time, and Go/NoGo tasks), and calculated the powers of the extracted components. Comparing the powers across these conditions suggests that the unlocked waveforms may reflect the inhibition of the task-irrelevant activities in the temporal regions while the subject attends to the visual stimulus. Our results demonstrate that CWE is a potential tool for revealing new findings of cognitive brain functions without any hypothesis in advance. © 2014 Takeda et al.

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