Affiliation: Human Information Science Laboratories
- Modular organization of internal models of tools in the human cerebellumHiroshi Imamizu
Advanced Telecommunications Research Institute, Human Information Science Laboratories, 2 2 2 Hikaridai, Seika, Soraku, Kyoto 619 0288, Japan
Proc Natl Acad Sci U S A 100:5461-6. 2003....
- Functional magnetic resonance imaging examination of two modular architectures for switching multiple internal modelsHiroshi Imamizu
Advanced Telecommunications Research Institute Computational Neuroscience Laboratories, Keihanna Science City, Kyoto 619 0288, Japan
J Neurosci 24:1173-81. 2004..These results suggest that switching mechanisms in the frontal cortex can be explained by the mixture-of-experts architecture, whereas those in the cerebellum and the parietal cortex are explained by the MOSAIC model...
- Internal forward models in the cerebellum: fMRI study on grip force and load force couplingMitsuo Kawato
ATR Human Information Science Laboratories, 2 2 2, Hikaridai, Seika cho, Soraku gun, Kyoto 619 0288, Japan
Prog Brain Res 142:171-88. 2003..Then, we used functional magnetic resonance imaging (fMRI) to measure the brain activity related to this coupling and demonstrate that the cerebellum is the most likely site for forward models to be stored...
- Brain mechanisms for predictive control by switching internal models: implications for higher-order cognitive functionsHiroshi Imamizu
Biological Information and Communications Technology Group, National Institute of Information and Communications Technology, 2 2 2, Hikaridai, Keihanna Science City, Kyoto, 619 0288, Japan
Psychol Res 73:527-44. 2009..These studies suggest that a concept of internal models can consistently explain the neural mechanisms and computational principles needed for fundamental sensorimotor functions as well as higher-order cognitive functions...
- Explicit contextual information selectively contributes to predictive switching of internal modelsHiroshi Imamizu
Department of Cognitive Neuroscience, ATR Computational Neuroscience Laboratories, 2 2 2, Hikaridai, Keihanna Science City, Kyoto 6190288, Japan
Exp Brain Res 181:395-408. 2007..Explicit contextual information prevents destruction and assists memory retention by improving the changes in output signals. Thus, the asymptotic levels of performance improved...
- Physical delay but not subjective delay determines learning rate in prism adaptationHirokazu Tanaka
National Institute of Information and Communications Technology NICT, Hikaridai 2 2 2, Keihanna Science City, Kyoto 619 0288, Japan
Exp Brain Res 208:257-68. 2011..These results indicate that prism adaptation occurs independently of awareness of subjective timing and may be processed in primary motor areas that are thought to have fidelity with temporal relations...
- A neural correlate of reward-based behavioral learning in caudate nucleus: a functional magnetic resonance imaging study of a stochastic decision taskMasahiko Haruno
Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute, Kyoto 619 0288, Japan
J Neurosci 24:1660-5. 2004..These findings suggest that the caudate nucleus is one of the main loci for reward-based behavioral learning...
- Neural correlates of predictive and postdictive switching mechanisms for internal modelsHiroshi Imamizu
Advanced Telecommunications Research Institute International, Computational Neuroscience Laboratories, and National Institute of Information and Communications Technology, Kyoto 619 0288, Japan
J Neurosci 28:10751-65. 2008..These results are consistent with separate mechanisms for predictive and postdictive switches and suggest that the LOTC and SMA receive output signals from appropriate internal models...
- Human sensorimotor cortex represents conflicting visuomotor mappingsKenji Ogawa
Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International, Keihanna Science City, Kyoto 619 0288, Japan
J Neurosci 33:6412-22. 2013..Our results reveal that the sensorimotor cortex represents different visuomotor mappings, which permits joint learning and switching between conflicting sensorimotor skills...
- Reconstruction of two-dimensional movement trajectories from selected magnetoencephalography cortical currents by combined sparse Bayesian methodsAkihiro Toda
Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International, Keihanna Science City, Kyoto, Japan
Neuroimage 54:892-905. 2011..These results suggest that the combined sparse Bayesian methods provide effective means to predict movement trajectory from non-invasive brain activity directly related to sensorimotor control...
- Single-trial prediction of reaction time variability from MEG brain activityRyu Ohata
Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International ATR, Keihanna Science City, Kyoto 619 0288, Japan
Sci Rep 6:27416. 2016..Thus, inter-trial variability in RTs is predictable information. Our study provides a proof-of-concept of the future development of non-invasive BMIs to prevent delayed movements. ..
- Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patternsMasahiro Yamashita
1 Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International ATR, Kyoto 619 0288, Japan 2 Graduate School of Information Science, Nara Institute of Science and Technology NAIST, Nara 630 0192, Japan
Sci Rep 5:7622. 2015..Our findings suggest that learning performance is potentially constrained by system-level interactions within task-activated networks as well as those between task-activated and less-activated networks. ..
- Central control of grasp: manipulation of objects with complex and simple dynamicsTheodore E Milner
School of Kinesiology, Simon Fraser University, Burnaby, Canada
Neuroimage 36:388-95. 2007..Nat. Neurosci. 1, 635-640, Blakemore, S.-J., Frith, C.D., Wolpert, D.M., 2001. The cerebellum is involved in predicting the sensory consequences of action. NeuroReport 12, 1879-1884)...
- Neural correlates of internal-model loadingLulu L C D Bursztyn
Department of Psychology and Centre for Neuroscience Studies, Queen s University, Kingston, Ontario K7L 3N6, Canada
Curr Biol 16:2440-5. 2006..The analysis revealed significant activity in the ipsilateral cerebellum and the contralateral and supplementary motor areas. We propose that these regions are involved in internal-model recruitment in preparation for movement execution...
- Central representation of dynamics when manipulating handheld objectsTheodore E Milner
Computational Neuroscience Laboratories, ATR, Kyoto, Japan
J Neurophysiol 95:893-901. 2006..We suggest that this is related to imagining the location and motion of an object with complex manipulation dynamics...
- Reorganization of brain activity for multiple internal models after short but intensive trainingHiroshi Imamizu
ATR Computational Neuroscience Laboratories, Kyoto, Japan
Cortex 43:338-49. 2007....
- Cerebellar activity evoked by common tool-use execution and imagery tasks: an fMRI studySatomi Higuchi
Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan
Cortex 43:350-8. 2007..Moreover, for half of the subjects the spatial distribution pattern for each tool was similar, suggesting that neural mechanisms contributing to skillful tool-use are modularly organized in the cerebellum...
- Activation of the human superior temporal gyrus during observation of goal attribution by intentional objectsJohannes Schultz
Wellcome Department of Imaging Neuroscience, London, UK
J Cogn Neurosci 16:1695-705. 2004..These data implicate the superior temporal gyrus in the identification of objects displaying complex goal-directed motion...