HowTheBrainWorks Wolpert Surrey

8/10/2019 HowTheBrainWorks Wolpert Surrey

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movement is the only way we have of interacting with the world

communication: speech, gestures, writing are motor acts sensory, memory, cognitive ! future movements

Q. Why do we and other animals have brains?

Sea Squirt

A. To produce adaptable and complex movements

Daniel Wolpert, Computational and Biological Learning Lab., Dept. of Engineering, University of Cambridge

The Master Puppeteer:How the Brain Controls the Body


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vs.What to move where

vs.Moving

2


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3DLR Institute of Robotics and Mechatronics Cup stacking world record


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www.wolpertlab.com

Sensory

Feedback MotorCommand

+Noise+Noise

Task

AmbiguousVariable

4

Noise: Unwanted disturbance corrupting a signals


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I now send you an essay which I have found amongthe papers of our deceased friend Mr Bayes, andwhich, in my opinion, has great merit....

Essay towards solving a problem in the doctrine

of chances (1764) Phil. Trans. Roy. Soc.

Rev. Thomas Bayes1702-1761

5

"


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Probabilities used to represent degrees of belief Strength of a belief is given a value between 0 and 1 P(A) = belief in proposition A

P(dice shows six)=1/6 P(all ravens are black)=0.95 P(Fermat would understand the proof of Fermats last theorem)

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Conditional probability P(A|B)= Probability of A being true given that B is true

P (dice shows six| dice is even)=1/3


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A = Disease B = Positive blood test

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n=10

P(A | B) = P( B | A) P(A)P(A , B) } A and B A given B

} = P(B)


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Prior

Belief in state BEFORE sensory input

LikelihoodPosterior

Belief in state AFTER sensory input

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Neuroscience A = State of the world B = Sensory input

A = Disease B = Positive blood test

P(A | B) = P( B | A) P(A)

P(B)

P(state | sensory input) = P(sensory input | state) P(state)P(sensory input)


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-1 0 1Ball position (cm)

The brain would need to represent the statistics of the task (prior) the noise in its own sensors (likelihood)

0.5

Inferred Prior

-1 0 1Ball position (cm)

(Krding & Wolpert, Nature, 2004)


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State 1 (Empty)

State 2 (Full)

Fundamental for

1. Control with delays2. Mental simulation3. Likelihood estimation

Wolpert & Kawato, Neural Networks 1998Haruno, Wolpert, Kawato, Neural Computation 2001

4. Sensory ltering


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Our sensors report afferent information combining

Ex-afferent information: changes in outside world Re-afferent information: changes we cause

+ =

Internalsource

Externalsource

EfferenceCopy

SensoryFeedback

+-


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Self-administered tactile stimuli rated as less ticklish than externallyadministered tactile stimuli (Weiskrantz, et al, 1971)
http://store.apple.com/Apple/WebObjects/ukstore.woa/90201/wo/TF42SklZF3Uu2mwwQue1mXqCe6k/0.SLID?nclm=micekeyboard&mco=62895Ahttp://store.apple.com/Apple/WebObjects/ukstore.woa/90201/wo/TF42SklZF3Uu2mwwQue1mXqCe6k/0.SLID?nclm=micekeyboard&mco=62895A


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0

1

2

3

Self-produced Robot-produced

T i c k l e r a t i n g Gain control or precise

spatio-temporal prediction?

***


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0

1

2

3

4

0ms 100ms 200ms 300ms Robot

T i c k l e r a t

i n g

Delay

0

1

2

3

4

0 30 60 90 Robot

T i c k l e r a t i n g

Angle

****** **

**

(Blakemore, Frith & Wolpert. J. Cog. Neurosci. 1999)


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Force escalates under rules designedto achieve parity: Increase by ~40% per turn

(Shergill, Bays, Frith & Wolpert, Science, 2003)Turn

F o r c e

( N )

0.25 N


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70% overestimate in force Presented force (N)

M a t c h e

d f o r c e

( N )


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Presented force (N)

M a t c h e

d f o r c e

( N )


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Failure to make correct sensory predictions (Frith 1987 Psychol. Med.)

EfferenceCopy

SensoryFeedback

P r e d t o

r

+-


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20(Shergill, Samson, Bays, Frith & Wolpert, Am J. Psychiatry, 2006)

ControlsPatients

Presented force (N)

M a t c

h e

d f o r c e

( N )


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Duration Hand trajectory

Arm conguration Muscles

Tasks are usually specied at a symbolic level Movements are specied at a detailed level: 600 muscle activations


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Eye-saccades Arm- movements

Time (ms)Time (ms)

V e

l o c i t y

S p e e

d ( d e g / s )

(Collewijn et al., 1988) (Uno et al., 1989)


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Movements have evolved to maximize tness improve through evolution/learning

every possible movement which can achieve a task has a cost we select movement with the lowest cost


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motor commands probability distribution of movement

M o t o r c o m m a n d

Noise

Desired command Actual command

TimeTime

(Harris & Wolpert, Nature, 98)

DesiredOptimal!


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Eye movements

Obstacle avoidance

(Hamilton & Wolpert JNP 2002)

Saccades main sequence

(Harris & Wolpert Biol Cyb.2006)

Sources of SDN noise

(Jones, Hamilton & Wolpert, JNP, 2002)Hamilton, Jones & Wolpert, EBR, 2004)

Other noise sources

(Van Beers, Haggard & Wolpert, JNP, 2004)

Arm movements


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l tl b

PaulBays

Chris Frith

AntoniaHamilton

ChrisHarris

SarahBlakemore

JamesIngram

KonradKrding

SukhiShergill

IanHoward
http://www.wolpertlab.com/http://www.wolpertlab.com/

HowTheBrainWorks Wolpert Surrey

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