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Feedback, Adaptation, Learning or Evolution: How Does the Brain Coordinate and Time Movements?. Amir Karniel Department of Biomedical Engineering Ben Gurion University of the Negev. The studies presented were done in collaboration with:

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Feedback adaptation learning or evolution how does the brain coordinate and time movements

Feedback, Adaptation, Learning or Evolution: How Does the Brain Coordinate and Time Movements?

Amir Karniel

Department of Biomedical Engineering

Ben Gurion University of the Negev

The studies presented were done in collaboration with:

Gideon Inbar, Ronny Meir, and Eldad Klaiman - Technion

Sandro Mussa-Ivaldi - Northwestern University

The first workshop of THE CENTER FOR MOTOR RESEARCH December 18-21, 2003

Jerusalem in Motion


Outline Brain Coordinate and Time Movements?

  • The Hierarchy of Wide Sense AdaptationEquilibrium trajectories and internal models Reaching movements muscle models and adaptation

  • Adaptation to Force Perturbations Time representation Sequence learning and switching

  • Bimanual Coordination Symmetry at the perceptual level as an invariant feature Tapping experiments and first indications for internal models

  • Summary and Future Research

Jerusalem in Motion


Two important concepts in the theory of motor control

y Brain Coordinate and Time Movements? d

x

y

F(x)

F-1(yd)

Two Important Concepts in the Theory of Motor Control

Equilibrium

Inverse Model

Feldman

Bizzi et al.

+Minimum Jerk, Flash and Hogan

+Force fields, primitives, Mussa-Ivaldi

Albus (cerebellum)

Inbar and Yafe (signal adaptation)

+feedback error, Kawato

+distal teacher, Jordan

Adaptation

Change of ImpedanceChange of the inverse

Jerusalem in Motion


Reaching movements

MJT Brain Coordinate and Time Movements?

Reaching movements

  • Feed-Forward Control

  • Invariant Features: Roughly straight line, bell shaped speed profile (Flash & Hogan 1985)

  • Key Questions

  • What is the origin of the invariance ?

  • How do we handle external perturbations ?

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A hill type mechanical muscle model the viscose element b is not a constant
A Hill-type mechanical muscle model Brain Coordinate and Time Movements? The viscose element B is not a constant !

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Linear vs nonlinear muscle model
Linear Vs. Nonlinear Muscle Model Brain Coordinate and Time Movements?

Linear model

The nonlinear Hill-type model

The physiologically plausible nonlinear model can produce the typical speed profile with a simple control signals

Karniel and Inbar (1997) Biol. Cybern. 77:173-183

Jerusalem in Motion


Other typical features of rapid movements are also facilitated by the nonlinear muscle properties
Other typical features of rapid movements are also facilitated by the nonlinear muscle properties

In this set of simulations the one-fifth power law model was used.

Karniel and Inbar (1999) J. Motor Behav. 31:203-206

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Adaptation to force perturbations

Force Field facilitated by the nonlinear muscle properties

After Learning

No Force

After-Effects

Force Field

Initial Exposure

Adaptation to force perturbations

  • Force field exposure  recovery of unperturbed pattern

  • Removal of field  “after-effects”

  • (Shadmehr & Mussa-Ivaldi 1994)

Modified with permission from Patton and Mussa-Ivaldi

Jerusalem in Motion


Hierarchical system with feedback adaptation and learning
Hierarchical system facilitated by the nonlinear muscle propertieswith feedback adaptation and learning

Internal models for control

Desired Target

Adaptation

Learning

Dynamics determine the control signal

(e.g., EPH, CPG, …)

Feedback

Musculoskeletal system

Actual Performance

Jerusalem in Motion


The hierarchy of wide sense adaptation facilitated by the nonlinear muscle propertiesKarniel and Inbar (2001), Karniel (In preparation)

Change Scale

Structural Change

Evolution

Learning

Functional Change

Parameters Change

Adaptation

Time Scale

Feedback

No Change

mSec Minutes Years Myears

Jerusalem in Motion


Outline facilitated by the nonlinear muscle properties

  • The Hierarchy of Wide Sense AdaptationEquilibrium trajectories and internal models Reaching movements muscle models and adaptation

  • Adaptation to Force PerturbationsTime representation Sequence learning and switching

  • Bimanual Coordination Symmetry at the perceptual level as an invariant feature Tapping experiments and first indications for internal models

  • Summary and Future Research

Jerusalem in Motion


What are the limitations of adaptation

Example: facilitated by the nonlinear muscle properties

Plant & Environment

Controller

Internal Representation of the field

Force Field

What is the structure of the modifier ?

Could it be a function of position, velocity, time, … ?

What are the limitations of adaptation?

Key Questions:

Jerusalem in Motion


Time representation
Time Representation facilitated by the nonlinear muscle properties

These systems are indistinguishable therefore

  • The existence of time variable isn’t sufficient to define time representation.

  • It is sufficient to consider the following form:

Jerusalem in Motion


Time representation definition
Time Representation - Definition facilitated by the nonlinear muscle properties

The system is said to be capable of time representation if there exists a deterministic function h(x) such that for any u(t).

The system is said to be capable of time representation of up to T seconds with ε accuracyif there exists a deterministic function h(x) such that for t<T and for any u(t).

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The experiment
The experiment facilitated by the nonlinear muscle properties

Number of movements ~100 ~500 ~100

Null Learning Generalization

No external field External Force field

time/state/sequence dependent

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Time varying force field
Time Varying Force Field facilitated by the nonlinear muscle properties

The force field is not correlated with the movement initiation, therefore there is no way to use state information.

Only time representation would allow adaptation and after-effects for this field.

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Result no adaptation to this tv force field
Result: No adaptation to this TV force field facilitated by the nonlinear muscle properties

The maximum distance from a straight line during “learning”

A control experiment with the viscous curl field

Karniel and Mussa-Ivaldi (2003) Biol. Cybern.

Jerusalem in Motion


Viscous curl force field

B facilitated by the nonlinear muscle properties-

B+

1

1

Vy

0

Vy

0

-1

-1

-1

0

1

-1

0

1

Vx

Vx

Viscous Curl Force Field

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Result there is significant adaptation with this sequence of force fields
Result: There is Significant Adaptation with This Sequence of Force Fields

The maximum distance from a straight line during “learning”

A control experiment with the viscous curl field

Jerusalem in Motion


Direction error calculation
Direction Error Calculation of Force Fields

1. Find the Euclidean distance from a straight line at the point of maximum velocity(The feed-forward part of the movement)

2. If the deviation is to the right multiply by –1

“B+”

3. If the curl field in the sequence is B- multiply by –1

Therefore:

Positive DE: Yielding to the field

Negative DE: Over resisting the field

DE is Positive

Jerusalem in Motion


Catch trials after effects
Catch trials – After Effects of Force Fields

A few trials without force field were introduced unexpectedly.

The left bar is the mean of the error (DE) during these trials in the first part of the learning.

The right bar is in the last part.

Significant expectation to the correct field after learning

i.e., learning of an internal model of the force field

Jerusalem in Motion


Mid summary
Mid – Summary of Force Fields

  • No adaptation in the case of the time dependent force field

  • Adaptation in the case of the simplest sequence of curl viscous fields with four targets.

What is learned in the second case?

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Odd and even movement

During the learning it is possible to assign a unique force field to each movement instead of learning the sequence of force fields.

The generalization phase would violate this representation.

Odd and Even Movement

B+B-

Force Field:

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Refuting the sequence learning assumption

1. field to each movement instead of learning the sequence of force fields.Analysis of errors in the last part where diagonal movements are introduced

Refuting the Sequence Learning Assumption

The same sequence is applied in this part; sequence learning predicts similar errors

Force Field:

B+B-

Jerusalem in Motion


Distance error analysis of movements in part 1 and part 5
Distance Error Analysis field to each movement instead of learning the sequence of force fields.of movements in part 1 and part 5

Left bar: Catch trials in part 1.

Middle bar: Movements in part 5 that are inconsistent with the learning phase.

Right bar: Movements in part 5 that are consistent with the learning phase.

All movements are consistent with the sequence of force field.

The sequence learning assumption predicts similar errors in the right two bars that is smaller than the first, left bar

However, ANOVA of the data shows similar error in the first two bars and significantly smaller error in the right bar!

Jerusalem in Motion


Refuting the sequence learning assumption1
Refuting the Sequence Learning Assumption field to each movement instead of learning the sequence of force fields.

We found that when the perturbation can be modeled both as a function of sequence and as a function of the state, the brain generates a state dependent model.

Can we design an experiment where only sequence representation would allow adaptation?

Would the brain adapt to this perturbation?

We tried to train subject with the same sequence but with three targets.

In this case one needs to follow the temporal sequence in order to adapt

Jerusalem in Motion


Result no adaptation to the sequence of force fields
Result: No Adaptation to the field to each movement instead of learning the sequence of force fields.Sequence of Force Fields!

The maximum distance from a straight line during “learning”

A control experiment with the viscous curl field

Karniel and Mussa-Ivaldi (2003) Biol. Cybern. 89:10-21

Jerusalem in Motion


Catch trials no after effects
Catch trials – No After Effects field to each movement instead of learning the sequence of force fields.

A few trials without force field were introduced unexpectedly.

The left bar is the mean of the error (DE) during these trials in the first part of the learning.

The right bar is in the last part.

No significant expectation to the correct field after learning

i.e., no learning of an internal model to the sequence!

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Mid summary 2
Mid – Summary (2) field to each movement instead of learning the sequence of force fields.

  • No adaptation in the case of time dependent force field

  • Adaptation when the temporal sequence coincide with single state mapping

  • No adaptation in the case of sequence of force fields

Karniel and Mussa-Ivaldi (2003) Biol. Cybern. 89:10-21

Maybe it is too difficult to construct two internal models simultaneously

Multiple Models Conjecture (“soft” version): If each force field is experienced separately and enough time is given for consolidation of each model, then the multiple model would be constructed

Jerusalem in Motion


Day 1 Day 2 Day 3 Day 4 field to each movement instead of learning the sequence of force fields.

Karniel and Mussa-Ivaldi EBR 2002

Early Training

Late Training

Late Training

Catch-Trials

Jerusalem in Motion


Result: field to each movement instead of learning the sequence of force fields.Clear learning of each perturbation, but No evidence for ability to utilize multiple models and context switching

Error [DE, mm] during early and late training

Day 1 Day 2 Day 3 Day 4

Error [DE, mm] during catch trials

(Subject E)

Jerusalem in Motion


Does the brain employs clocks counters or switches
Does the brain employs field to each movement instead of learning the sequence of force fields.clocks counters or switches ?

In contrast to artificial devices that are based on clock counters and switches the brain seems to prefer state dependent maps

Jerusalem in Motion


Outline field to each movement instead of learning the sequence of force fields.

  • The Hierarchy of Wide Sense AdaptationEquilibrium trajectories and internal models Reaching movements muscle models and adaptation

  • Adaptation to Force Perturbations Time representation Sequence learning and switching

  • Bimanual Coordination Symmetry at the perceptual level as an invariant feature Tapping experiments and first indications for internal models

  • Summary and Future Research

Jerusalem in Motion


Bimanual coordination 1
Bimanual Coordination (1) field to each movement instead of learning the sequence of force fields.

  • Preference for in-phase symmetry

  • Stable vs. Unstable

  • Homologous muscles

    Figure from Kelso and Schöner (1988)

Jerusalem in Motion


Bimanual coordination 2
Bimanual Coordination field to each movement instead of learning the sequence of force fields. (2)

  • It was recently shown that the preference for symmetry in bimanual coordination is perceptual

    Figure from Mechsner et al. (2001)

Jerusalem in Motion


Bimanual coordination 3
Bimanual Coordination (3) field to each movement instead of learning the sequence of force fields.

  • Untrained individuals are unable to produce non-harmonic polyrhythms

  • However, with altered feedback (gear) they are able to generate symmetrical movement of the flags and non-symmetrical movements of the hands.

  • Again: The preference for symmetry is perceptual

  • Figure from Mechsner et al. (2001)

Jerusalem in Motion


Bimanual coordination 4
Bimanual Coordination (4) field to each movement instead of learning the sequence of force fields.

  • The preference for symmetry was explained in terms of stable solution of dynamic system without employing internal models.

  • Following the vast literature about reaching movements we propose an alternative Hypothesis:

    The brain contains internal representation of the transformation between the perceptual level and the execution level in order to maintain the symmetry invariance in face of altered feedback or other external perturbations.

  • Predictions: 1. Learning curves, 2. After effects

Jerusalem in Motion


Bimanual index tapping experiment
Bimanual Index Tapping Experiment field to each movement instead of learning the sequence of force fields.

  • The right hand received slower feedback such that when the display shows rotation at equal speeds the subject eventually produces a non-harmonic polyrhythm, with a left/right tapping frequency ratio of 2/3

Jerusalem in Motion


Learning curve regression standardized data
Learning Curve Regression (Standardized Data) field to each movement instead of learning the sequence of force fields.

From: Karniel A, Klaiman E, and Yosef V, Society for Neuroscience 2003

Jerusalem in Motion


After effect indications the last 60 seconds of each half in the experiment
After-Effect Indications field to each movement instead of learning the sequence of force fields.The last 60 seconds of each half in the experiment

Jerusalem in Motion


Bimanual adaptation hypothesis
Bimanual Adaptation Hypothesis field to each movement instead of learning the sequence of force fields.

  • Symmetry Invariance

  • Adaptable transformation from the perception level to the execution level

  • After effects

  • The structure, learning rates and generalization capabilities are subjects for future research

Jerusalem in Motion


Future research
Future Research field to each movement instead of learning the sequence of force fields.

  • Relative role of each level, muscles, spinal cord, central nervous system

  • The structure of internal models (learning capabilities and generalization capabilities)

  • Virtual Haptic Reality

  • The Robo-Sapiens age

Mathematical Analysis, Simulation, Experiments

Jerusalem in Motion


Turing-like test for motor intelligence: field to each movement instead of learning the sequence of force fields.The Robo-Sapiens age

Building a robot that would be indistinguishable from human being

Jerusalem in Motion


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