1. 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

2. Direct Use of Computational Models in Neuroimaging
Brain
Imaging Data X Y Z P Q R U V W
Model 1
Model 2
Model 3
Visual stimuli
and reward sequence
Actions taken
by subject
Behavioral
Data

3. 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

4. Hierarchical Bayesian Filter Soft Constraint from fMRI/NIRS data
Hierarchical Baeysian Estimation of Current Distribution from fMRI/MEG Data
High temporal resolution (ms)
High spatial resolution (mm)
time
Current
Hierarchical Bayesian Filter
Estimated Current
MEG/EEG data
Focus on active region
Soft Constraint from fMRI/NIRS data
time
Current
Current Source
Temporal average data from fMRI/NIRS

5. 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

6. Understanding Hierarchical Sensory-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