Piecing through the notice below with my renowned ignorance, it occurs to me
to ask: does the brain/ cerebellum demonstrate as much general intelligence
and flexibility in its movements as in its consciously directed thinking?
... In its ability to vary muscle coordination patterns (& structural
alignments) to achieve the same motions? ("there is no one-to-one
correspondence between a desired movement goal, limb motions, or muscle
activity")? (Its capacity, for example, to shift around body weight while
standing in a given position, in order to ease pressures, or to
automatically adjust muscular coordination for walking,say, when a foot is
injured, [without any history of such an injury]). ... In its ability to
improvise new muscle coordination patterns etc to create new movements, (and
"move so elegantly through unpredictable and dynamic environments")? (Its
capacity for example to immediately contort itself strangely to catch a
plate falling at a strange angle, or to writhe every which way to squeeze
out of tight corners).
[Are there any good/standard terms BTW for this flexibility of motor
patterns? "Multimuscularity"?]
Do any AGI's demonstrate any comparable flexibility in trying to solve
problems? This perhaps comes down to Minsky's idea that an AGI should be
able to switch between different "ways to think" - or perhaps one can use
the word "faculties". Are there any systems that can, say, switch flexibly
between different kinds of logic (PLN/NARS say) when one doesn't work? Or
between logic, language, visualisation, geometry etc - to solve the same
problem?
Shouldn't this be a foundational requirement for an AGI - the ability to
switch between faculties/ modalities in solving intellectual problems, as
easily as the body switches between muscle groups in solving motor problems
(and as the brain itself switches)? The capacity to "have its wits about
it"?
[I get almost 0, googling for "multimodal AI"].
*** Redwood Seminar - TODAY ***
Dimensional Reduction in Motor Patterns for Balance Control
Lena H. Ting
Department of Biomedical Engineering, Emory University
and Georgia Institute of Technology,
and Fall 2008 Visiting Miller Professor
Wednesday, Sept. 24 at 12:00
508-20 Evans Hall
How do humans and animals move so elegantly through unpredictable and
dynamic environments? And why does this question continue to pose
such a challenge? We have a wealth of data on the action of neurons,
muscles, and limbs during a wide variety of motor behaviors, yet
these data are difficult to interpret, as there is no one-to-one
correspondence between a desired movement goal, limb motions, or
muscle activity. Using combined experimental and computational
approaches, we are teasing apart the neural and biomechanical
influences on muscle coordination of during standing balance control
in cats and humans. Our work demonstrates that variability in motor
patterns both within and across subjects during balance control in
humans and animals can be characterized by a low-dimensional set of
parameters related to abstract, task-level variables. Temporal
patterns of muscle activation across the body can be characterized by
a 4-parameter, delayed-feedback model on center-of-mass kinematic
variables. Changes in muscle activity that occur following large-
fiber sensory-loss in cats, as well as during motor adaptation in
humans, appear to be constrained within the low-dimensional parameter
space defined by the feedback model. Moreover, well-adapted responses
to perturbations are similar to those predicted by an optimal
tradeoff between mechanical stability and energetic expenditure.
Spatial patterns of muscle activation can also be characterized by a
small set of muscle synergies (identified using non-negative matrix
factorization) that are like motor building blocks, defining
characteristic patterns of activation across multiple muscles. We
hypothesize that each muscle synergy performs a task-level function,
thereby providing a mechanism by which task-level motor intentions
are translated into detailed, low-level muscle activation patterns.
We demonstrate that a small set of muscle synergies can account for
trial-by-trial variability in motor patterns across a wide range of
balance conditions. Further, muscle activity and forces during
balance control in novel postural configurations are best predicted
my minimizing the activity of a few muscle synergies rather than the
activity of individual muscles. Muscle synergies may represent a
sparse motor code, organizing muscles to solve an “inverse binding
problem” for motor outputs. We propose that such an organization
facilitates fast motor adaptation while concurrently imposing
constraints on the structure and energetic efficiency of motor
patterns used during motor learning.
_______________________________________________
Bruno A. Olshausen
Helen Wills Neuroscience Institute, School of Optometry,
and Redwood Center for Theoretical Neuroscience, UC Berkeley
3210F Tolman Hall MC 3192
Berkeley, CA 94720
(510) 642-7250 / 2-7206 (fax) http://redwood.berkeley.edu/bruno
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agi
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