Transcript Brain_Regions_Involved_in_USCBP_Reaching_Models

Brain Regions Involved in
USCBP Reaching Models
A High Level Overview
Brain Regions
• Cheol’s Models
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Motor cortex (M1)
Spinal cord
Basal Ganglia (BG)
Dorsal Premotor (PMd, providing input)
• Jimmy’s Models
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Parieto-occipital area (V6a)
Lateral intraparietal area (LIP)
BG
PMd (specifically F2)
Issues In Model Integration
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Unified View of M1
Interactions between PMd and M1
Role of the BG
Involvement of the Cerebellum
M1 Modeling
• Cheol
– Top-down model – directional tuning with
supervised and unsupervised learning
– Bottom-up model – input and output maps
with controlling muscle synergies
• Jimmy
– Robotic control model – trajectory generator,
inverse kinematics, PD controllers (probably
not all in M1)
Cheol’s Top-Down M1 Model
• Directional tuning of M1
neurons tuned using
supervised learning
and unsupervised
learning
• Arm choice learned
with reinforcement
learning
– Jimmy: Equivalent
to noisy WTA based
on executability
– Cheol: connecting to
unified view of motor
learning
Possible motor
procedures in the
motor cortex
• Inverse dynamics and
muscle models
learned using
temporal difference
learning in an actorcritic architecture
• The actor may
correspond to the
motor cortex.
Trajectory
Generator
Joint static
Level
Planning
ACTOR
Inverse
Dynamics
Joint “force”
Level
Planning
Inverse
Muscle Model
Muscle
Level
Planning
Motoneurons
(spinal cord)
Arm
CRITIC
Evaluator
Of Mvmt
TD error
Cheol’s Bottom-up M1 model
(based on feedback signal)
Target location
(premotor)
IDM: mapping the error direction
to muscle synergy
(directly related to directional tuning)
ISM
Motor Cortex
Model (map)
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Feedback signal
(premotor)
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(with optimal feedback controller)
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Muscle Synergy
Forward model
Pesaran et al. (2006) indicated that PMd neurons encoded
both target location and feedback signal.
ILGA Motor Controller
• Input - reach target in
wrist-centered
coordinates
• Dynamic Motor
Primitives – generate
reach trajectory
• Inverse Kinematics –
pseudo-inverse of
Jacobian matrix
• PD controllers – one for
each DOF
Interactions Between PMd and M1
• Our views of the role of PMd are very
similar
• Jimmy
– PMd (F2) provides M1 with target location in
wrist-centered coordinates
• Cheol
– Supra-motor-cortex coding in PMd may be
feedback error (target location in handcentered reference frame) and/or target
location in the fixation point coordinates.
ILGA: F2 Integrates Bottom-Up and
Top-Down Reach Target Signals
Tanne et al (1995)
• Rostral F2 performs
target selection based
on parietal and
prefrontal input
• Caudal F2 encodes
selected target and
initiates reach
– F6 detects go signal
and disinhibits via BG
Reconciliation with FARS view of
PMd
• FARS implicated F2 in conditional action
selection and F4 in reach target selection
• However many studies show F2 to contain
directionally tuned neurons that discharge prior
to reaching
• F4 contains bimodal (visual / somatosensory)
neurons that respond when objects approach
their somatosensory receptive field on the arm
or hand
F2 vs. F4: Experimental Data
• Neurons in F2 are broadly tuned to multidimensional
direction in a reaching task (Caminiti, 1991; Fu et al., 1993)
• Pesaran, Nelson & Andersen (2006) – PMd neurons encode
relative positions of eye, hand, and target
– PMd contains combined signals.
– MIP contains more (target-eye) coding – fixation point coordinate
• F4 bimodal visual-tactile neurons have very large visual and
somatosensory receptive fields and visual field is anchored
to somatosensory field
– But most don’t fire for stimuli farther than 25cm away (Graziano et al.,
1997) - Not suitable for encoding reach target!
– May be involved in feedback control of reach-grasp coordination –
tactile RFs may contribute to transition from visual- to haptic-based
control
Role of the BG
• Cheol
– Adaptive critic in actor-critic architecture
• Jimmy
– Adaptive critic gated by internal state
– Action disinhibition
• Role in previous USCBP models
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DA / DAJ – action disinhibition
ILGM – reward signal
Extended TD – adaptive critic
Bischoff BG model – next-state prediction
BG Disinhibition of Action
• ILGA’s use of the basal
ganglia to disinhibit
actions is largely
consistent with its role
in the Dominey-Arbib and Dominey-ArbibJoseph Models
• The cortical target of context-dependent biases
are different
BG as an Adaptive Critic
• The basal ganglia’s role as an adaptive critic is not very
controversial
• However, each of our models uses it to learn different
parameters
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Cheol’s top-down model – to modify arm selection
Cheol’s bottom-up model – to learn inverse models
ILGA – kinematic parameters and contextual bias
ACQ – executability and internal state-dependent desirability
• Does this imply several actor/critic combinations (1:1,
N:1, 1:N, N:N)?
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Cheol’s top-down model – actor / critic
Cheol’s bottom-up model – actor / critic
ILGA – actor / critic
ACQ – actor / multiple critics
M1 & BG roles in Cheol’s unified view
The reinforcement learning framework will replace “optimization of a taskrelated cost function” with “maximization of a task-related reward function”
which also accounts for actuators’ limitation
Visual signal
(world representation)
Action-oriented
perception ?
The critic encodes the current task-related reward function. The reward or
an action value is defined only when we have an “objective”.
So, the critic will try to encode which action might be the best action in
terms of reward (action value) to achieve a certain objective.
It will monitor that the current movement’s performance. If the
performance is changed,
Critic the
X critic will give the information of the next
best action. And it will facilitate changing the actor accordingly.
Target related
signal
(vision-task-related)
If there are multiple tasks, there should be multiple critics.
Send limitation
of isthe
This arrow
actuators the
viaactor.
TD error
representation
of the actuator
What is now the critic’s role? It will encode the objective function and
provide the “teaching” signal to the actor through TD error: if TD error is
zero, we don’t need to change the actor, and so on.
TD error.
Critic
(motor-task-related)
It represent the current maximum capability of the motor actuators.
Send limitation of the
actuators via
unsupervised
Any motorlearning
actuators
So, if the motor actuators are based on muscles, it will be the
muscle synergies and the limitation of muscle-based actuator.
If there is a stroke on it, the maximum capability is changed and the
limitation of the world increases.
If there is a rehabilitation, the maximum capability is changed again
and the limitation decreases.
M1 & BG roles in Cheol’s unified view
PLoS model
Jool’s variability data
Reaching
module
Coordination
manager
Critic
Critic
actor
Representation of
the actuators
Because of the stroke on a motor cortex, we have a
change in limitation (performance change) of the
corresponding actuator. The action choice module
will encode which arm is better in a certain direction.
So when the performance of the affected arm
decreased, it will say that the best action is using the
unaffected arm. (i.e. behavioral compensation).
Can we connect these ideas with the words
executability and desirability? In general, the
objective function contains both concepts I think.
Hierarchical Optimal Feedback Controller
Todorov et al (2005) found a similar idea on
hierarchical optimization of the plants. But the
reinforcement learning framework will provide the
more general framework of the motor system learning
and may be more applicable
Grasping
module
Maybe separated
obtaining of those two
modules (early learning)
In this coordination problem, we may have an
objective of the coordination. As an example, we
can weigh more on faster movement, or on the
accurate movement, or accurate grasping.
So based on the different objective, we may have
variability in coordination.
However, this coordination is not free from the
actuators. First, if there is a signal dependent
noise, we cannot have too fast movement. (This
limitation is already in the Hoff-Arbib model).
Second, too large initial aperture can assure the
more accurate grasping but will give a limitation
of the reaching module (slower reaching).
Motor cortex model
Kambara et al. (2008) showed the possibility and I
also would implement it with map reorganization!
Involvement of the Cerebellum
• Schweighofer’s Modeling – corrects for
nonlinearities in arm control
• Cheol – what about learning projections
from cerebellum to M1?
References
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Caminiti, R., Johnson, P.B., Galli, C., Ferraina, S., Burnod, Y. (1991) Making Arm
Movements within Different Parts of Space: The Premotor and Motor Cortical
Representation of a Coordinate System for Reaching to Visual Targets. The Journal
of Neuroscience, 11(5): 1182-1197.
Fu, Q.G, Suarez, J.I., Ebner, T.J. (1993) Neuronal Specification of Direction and
Distance During Reaching Movements in the Superior Precentral Premotor Area and
Primary Motor Cortex of Monkeys. Journal of Neurophysiology, 70(5): 2097-2116.
Graziano, M.S.A., Hu, X.T., Gross, C.G. (1997) Visuospatial Properties of Ventral
Premotor Cortex. Journal of Neurophysiology, 77: 2268-2292.
Tanne, J., Boussaoud, D., Boyer-Zeller, N., Roiuller, E.M. (1995) Direct visual
pathways for reaching movements in the macaque monkey. NeuroReport, 7: 267-272.
Pesaran, B., Nelson, MJ., Andersen, RA. (2006) Dorsal premotor neurons encode the
relative position of the hand, eye, and goal during reach planning. Neuron 51, 125134
Buneo, CA., Jarvis, MR., Batista, AP., Andersen RA, (2002) Direct visuo-motor
transformation for reaching, Nature 416, 632-636.
Todorov, E., Li, W., Pan X., (2005) From task parameters to motor synergies: A
hierarchical framework for approximately optimal control of redundant manipulator, J
Robot Syst. 22(11), 691-710.
Kambara, H., Kim, K., Shin, D., Sato, M., Koike, Y., (2006) Motor control-learning
model for reaching movements, IJCNN2006