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