Mitsuo Kawato ATR Computational Neuroscience Labs
description
Transcript of Mitsuo Kawato ATR Computational Neuroscience Labs
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
BrainImaging Data
Model 2 X Y Z P Q R U V W
Visual stimuliand reward sequence
Actions takenby subject
Model 1 Model 3
BehavioralData
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
MEG/EEG data
Soft Constraint from fMRI/NIRS data
Estimated Current
Focus on active region
Temporal average data from fMRI/NIRSCurrent Source
Hierarchical Bayesian Filter
Hierarchical Baeysian Estimation of Current Distribution from fMRI/MEG Data
High temporal resolution (ms)High spatial resolution (mm)
time
Cu
rre
nt
time
Cu
rre
nt
Classification of Attend to Motion or Color by Single-trial MEG before Stimulus
Presentation
MEG
MEG
Source localization
1. Classification at Sensor Space with Sparse Logistic Regression
2. Classification at Brain Space via VB-MEG Inversion
Feature extractionClassification
Feature extractionClassification
Test : 70.4% (40 features)CV : 78.5 ± 7.7%
Test : 85.7% (8 features)CV : 90.7 ± 6.9%
Prefrontal decoding
Parietal decoding
CBL decoding
M1 decoding
Decision
Intention
Internal model
Muscle activity
ROBOTHuman
SARCOS, ATR, CMU, NiCT
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Understanding HierarchicalSensory-Motor Control by the Brain
through Robot Control with BNI