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Michael Arbib: CS564 - Brain Theory and Artificial Intelligence
University of Southern California, Fall 2001
Lecture 10.
The Mirror Neuron System Model (MNS) 1
Reading Assignment:
Schema Design and Implementation of
the Grasp-Related Mirror Neuron System
Erhan Oztop and Michael A. Arbib
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Visual Control of Grasping in Macaque Monkey
A key theme of
visuomotor coordination:
parietal affordances
(AIP)
drive
frontal motor
schemas
(F5)
F5 - grasp
commands in
premotor cortex
Giacomo Rizzolatti
AIP - grasp
affordances
in parietal cortex
Hideo Sakata
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Mirror Neurons
Rizzolatti, Fadiga, Gallese, and Fogassi, 1995:
Premotor cortex and the recognition of motor
actions
Mirror neurons form the subset
of grasp-related premotor
neurons of F5 which discharge
when the monkey observes
meaningful hand movements
made by the experimenter or
another monkey.
F5 is endowed with an
observation/execution matching
system
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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F5 Motor Neurons
F5 Motor Neurons include all F5 neurons whose firing is related to motor
activity.
We focus on grasp-related behavior. Other F5 motor neurons are related to orofacial movements.

F5 Mirror Neurons form the subset of grasp-related F5 motor neurons of F5
which discharge when the monkey observes meaningful hand movements.
F5 Canonical Neurons form the subset of grasp-related F5 motor neurons
of F5 which fire when the monkey sees an object with related affordances.
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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What is the mirror system (for grasping) for?
Mirror neurons: The cells that selectively discharge when the
monkey executes particular actions as well as when the monkey
observes an other individual executing the same action.
Mirror neuron system (MNS): The mirror neurons and the brain
regions involved in eliciting mirror behavior.
Interpretations:
• Action recognition
• Understanding (assigning meaning to other’s actions)
• Associative memory for actions
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Computing the Mirror System Response
The FARS Model:
Recognize object affordances and determine appropriate grasp.
The Mirror Neuron System (MNS) Model:
We must add recognition of
 trajectory and
 hand preshape
to
 recognition of object affordances
and ensure that all three are congruent.
There are parietal systems other than AIP adapted to this task.
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Further Brain Regions Involved
cIPS:
cIPS
cIPS
caudal
intraparietal
sulcus
Axis and
surface orientation
Detection of
biologically meaningful
stimuli (e.g.hand
actions)
Motion related activity
(MT/MST part)
STS:
Superior
Temporal
Sulcus
7b (PF):
Rostral part of
the posterior
parietal lobule
Spatial coding
7a (PG):
for objects,
caudal part of analysis of
the posterior motion during
parietal lobule
interaction of
objects and
self-motion
Mainly somatosensory
Mirror-like responses
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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cIPS cell response
Surface orientation selectivity
of a cIPS cell
cIPS
cIPS
Sakata et al. 1997
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Key Criteria for Mirror Neuron Activation
When Observing a Grasp
a) Does the preshape of the hand correspond to the grasp
encoded by the mirror neuron?
b) Does this preshape match an affordance of the target object?
c) Do samples of the hand state indicate a trajectory that will bring the hand to
grasp the object?
Modeling Challenges:
i) To have mirror neurons self-organize to learn to recognize grasps in the
monkey’s motor repertoire
ii) To learn to activate mirror neurons from smaller and smaller samples of a
trajectory.
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Initial Hypothesis on Mirror Neuron Development
The development of the (grasp) mirror neuron system in a healthy
infant is driven by the visual stimuli generated by the actions
(grasps) performed by the infant himself.
The infant (with maturation of visual acuity) gains the ability to map other
individual’s actions into his internal motor representation.
[In the MNS model, the hand state provides the key representation for this
transfer.]
Then the infant acquires the ability to create (internal) representations for
novel actions observed.
Parallel to these achievements, the infant develops an action prediction
capability (the recognition of an action given the prefix of the action and the
target object)
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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The Mirror Neuron System (MNS) Model
Object features
Visual Cortex
cIPS
7b
Ob ject
a ffor da nc e
e xtr a ction
H an d
m otion
d ete ction
S TS
AIP
Ob ject af ford a nce
-h an d state
a ssociation
H an d
sha p e
r ecog nition
F5cano nical
M otor
p rog ra m
(Gr a sp )
Integrate
tem po ral
ass ociation
Act ion
M irro r
Feed back r ecog nition
H an d -Obje ct
sp atia l re lation
a na lysis
7a
(M irr or
N eu r ons)
F5mirror
M otor
p rog ra m
(Re a ch)
M otor
e xe cu tion
M1
F4
Ob ject
loca tion
MIP/LIP/VIP
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Implementing the Basic Schemas of the Mirror Neuron System
(MNS) Model
using Artificial Neural Networks
(Work of Erhan Oztop)
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Opposition Spaces and Virtual Fingers
The goal of a successful
preshape, reach and grasp
is to match the opposition
axis defined by the virtual
fingers of the hand with
the opposition axis defined
by an affordance of the
object
(Iberall and Arbib 1990)
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Hand State
Our current representation of hand state defines a
7-dimensional trajectory F(t) with the following components
F(t) = (d(t), v(t), a(t), o1(t), o2(t), o3(t), o4(t)):
d(t): distance to target at time t
v(t): tangential velocity of the wrist
a(t): Aperture of the virtual fingers involved in grasping at time t
o1(t): Angle between the object axis and the (index finger tip – thumb tip)
vector [relevant for pad and palm oppositions]
o2(t): Angle between the object axis and the (index finger knuckle – thumb tip)
vector [relevant for side oppositions]
o3(t), o4(t): The two angles defining how close the thumb is to the hand as
measured relative to the side of the hand and to the inner surface of the palm.
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Curve recognition
The general problem: associate N-dimensional space curves with object
affordances
A special case: The recognition of two (or three) dimensional trajectories
in physical space
Simplest solution: Map temporal information into spatial
domain. Then apply known pattern recognition techniques.
Problem with simplest solution: The speed of the moving
point can be a problem! The spatial representation may
change drastically with the speed
Scaling can overcome the problem. However the scaling
must be such that it preserves the generalization ability of
the pattern recognition engine.
Solution: Fit a cubic spline to the sampled values. Then normalize and resample from the spline curve.
Result:Very good generalization. Better performance than using the Fourier
coefficients to recognize curves.
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Curve recognition
Curve recognition system demonstrated for hand drawn numeral recognition
(successful recognition examples for 2, 8 and 3).
Spatial resolution: 30
Network input size: 30
Hidden layer size: 15
Output size: 5
Training : Back-propagation
with momentum.and
adaptive learning rate
Sampled points
Point used for spline interpolation
Fitted spline
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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STS hand shape recognition
Color Coded Hand
Feature Extraction
Step 1 of hand shape
recognition: system
processes the color-coded
hand image and generates a
set of features to be used by
the second step
Model Matching
Step 2: The feature vector
generated by the first step is used
to fit a 3D-kinematics model of the
hand by the model matching
module. The resulting hand
configuration is sent to the
classification module.
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
Precision grasp
Hand Configuration
Classification
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STS hand shape recognition 1:
Color Segmentation and Feature Extraction
Color Expert
Preprocessing
(Network weights)
Training phase: A color expert is generated by training a feed-forward network to approximate
human perception of color.
Features
NN augmented
segmentation system
Actual processing: The hand image is fed to the augmented segmentation system. The color
decision during segmentation is done by consulting color expert.
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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STS hand shape recognition2:
3D Hand Model Matching
Feature Vector
Error minimization
Result of feature
extraction
A realistic drawing of hand
bones. The hand is
modelled with 14 degrees
of freedom as illustrated.
Grasp Type
Classification
The model matching algorithm minimizes the error
between the extracted features and the model hand.
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Virtual Hand/Arm and Reach/Grasp Simulator
A precision pinch
A power grasp
and
a side grasp
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Power grasp time series data
+: aperture; *: angle 1; x: angle 2; : 1-axisdisp1; :1-axisdisp2; :
speed; : distance.
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Core Mirror Circuit
Object affordance
Association
(7b) Neurons
Mirror Neurons
(F5mirror)
Mirror Neuron
Output
Hand state
Motor Program
(F5 canonical)
Object
Affordances
Object affordance hand state association
Integrate
temporal
association
Mirror Feedback
Motor
program
F5canonical
Hand shape
recognition
& Hand
motion
detection
Mirror
Feedback
Hand-Object
spatial relation
analysis
Action
recognition
(Mirror
Neurons)
Motor
program
Motor
execution
F5mirror
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Connectivity pattern
Object affordance (AIP)
STS
F5mirror
7b
Motor Program
(F5canonical)
Mirror Feedback
7a
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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A single grasp trajectory viewed from three different
angles
The wrist trajectory during the
grasp is shown by square traces,
with the distance between any
two consecutive trace marks
traveled in equal time intervals.
How the network classifies the
action as a power grasp. Empty
squares: power grasp output;
filled squares: precision grasp;
crosses:
side grasp output
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Power and precision grasp resolution
(a)
Note that the modeling yields
novel predictions for time course
of activity across a population of
mirror neurons.
(b)
Precision Pinch
Mirror Neuron
Power Grasp
Mirror Neuron
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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Research Plan
Development of the Mirror System
Development
of Grasp Specificity in F5 Motor and Canonical Neurons
Visual Feedback for Grasping: A Possible Precursor of the Mirror Property
Recognition of Novel and Compound Actions and their Context
The
Pliers Experiment: Extending the Visual Vocabulary
Recognition of Compounds of Known Movements
From Action Recognition to Understanding: Context and Expectation
Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001. Lecture 10. MNS Model 1
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