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Towards Manipulative Neuroscience based on Brain Network Interface ブレインネットワークインタフェースに 基づく操作脳科学を目指して. Mitsuo Kawato ATR Computational Neuroscience Labs. Discovery Channel. Direct Use of Computational Models in Neuroimaging. Brain Imaging Data. Y. Z. Q. R. V. W. X. P. U. Model 1.
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Towards Manipulative Neuroscience based on Brain Network Interfaceブレインネットワークインタフェースに基づく操作脳科学を目指して Mitsuo Kawato ATR Computational Neuroscience Labs Discovery Channel
Direct Use of Computational Models in Neuroimaging Brain Imaging Data Y Z Q R V W X P U Model 1 Model 2 Model 3 Visual stimuli and reward sequence Actions taken by subject Behavioral Data
Framework of Control (Manipulative) System Neuroscience • Necessity to link theory and experiments, beyond mere temporal correlation of hypothetical theoretical variables with neural firings or brain activation • Decoding of neural information by BNI and its feedback to brain • Theory-guided manipulation of BNI feedbacks and their predicted effects
High temporal resolution (ms) High spatial resolution (mm) Current Current time time Hierarchical Baeysian Estimation of Current Distribution from fMRI/MEG Data Hierarchical Bayesian Filter Estimated Current MEG/EEG data Focus on active region Soft Constraint from fMRI/NIRS data Current Source Temporal average data from fMRI/NIRS
Classification of Attend to Motion or Color by Single-trial MEG before Stimulus Presentation 1. Classification at Sensor Space with Sparse Logistic Regression MEG Feature extraction Classification Test : 70.4% (40 features) CV : 78.5 ± 7.7% 2. Classification at Brain Space via VB-MEG Inversion MEG Feature extraction Classification Test : 85.7% (8 features) CV : 90.7 ± 6.9% Source localization
Understanding HierarchicalSensory-Motor Control by the Brain through Robot Control with BNI Human ROBOT Decision Prefrontal decoding Intention Parietal decoding Internal model CBL decoding Muscle activity M1 decoding SARCOS, ATR, CMU, NiCT