physiology, robotics, and computational biologyaliceabr/ei_talk_fall_2007.pdf · * serves...
TRANSCRIPT
![Page 1: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/1.jpg)
Evolving “Physical” Intelligence:
physiology, robotics, and
computational biology
By Bradly Alicea
EI Meeting, DevoLab, Michigan State University, Fall 2007
![Page 2: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/2.jpg)
Introduction
Research Question: how do we uncover and represent the adaptive and
phylogenetic processes behind “physical” intelligent behavior (e.g.
movement, kinetics, control)?
* examples focus on autonomous physical
intelligence in vertebrates (lampreys
to humans), may generalize to design of
machines (biomimetics).
* paradigm focuses on motility related to
propulsion and “work”; interaction of
multiple physical elements.
* requires approximating a physiological
control system. Application domains:
biomechatronics, robotics, even micro-
machines.
* look at morphology alone, nervous system
alone, and morphology and nervous system
together.
![Page 3: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/3.jpg)
Introduction (con’t)
Jeff Hawkins (Redwood Neuroscience Institute, Palm Technologies): “On
Intelligence”:
Intelligence is an internal mechanism:
* serves “pattern prediction” function
* memory-based, adaptive, hierarchical
* has an effect on behavior, not behavior
in and of itself.
* his focus is on “neocortex”, which is a
specific physiological system.
* idea can be generalized; formalized as
a control system.
![Page 4: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/4.jpg)
Design principles (or principles of
evolvability)Principle #1 – modeling physical intelligence takes into account:
* physical sensory receptors: proprioceptors, nociceptors, muscle spindles. Capture the
collective activity of excitable cell populations.
* adaptability of morphology (e.g. muscle, bone): hypertrophy, fatigue, stress/strain,
regeneration.
Principle #2 – tetanic stimulation, physical exercise, environmental
training = “triad” of inducing adaptability (e.g. physiological plasticity):
* tetanic stimulation: deliver a tetanus (rapid electrical pulse) to muscle, neuronal tissue.
Results in LTP, “virtual” training
* physical exercise: Kaatsu (restrict blood flow to limb, stress muscles in that limb),
Fartlek (alternate intensity of training).
* alternate and extreme environment training: 0-g, force field adaptation, environmental
switching, H2S respiration (reduced metabolic baseline), ischemic preconditioning.
![Page 5: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/5.jpg)
Design principles (or principles of
evolvability) – (con’t)Principle #3 – structural modular
intelligence:
* custom prosthetics (C-leg, foot-ankle
prosthetics, brain-machine interfaces)
replicate “intelligence” locally.
* adaptive walking, reaching, motility,
even thinking.
* function regulated by nervous system,
other morphological systems, environment.
Andy Clark (Natural-Born Cyborgs, 2004),
transformative potential of prosthetics.
Limbs > cells (e.g. living heart valve).
* due to role of proprioception, induces
locally adaptive changes in cell populations
(Smith et.al, Tissue Engineering, 7(2),
131, 2001.
![Page 6: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/6.jpg)
1) Passive Dynamic Walkers (PDWs).
2) stability enforcement mechanisms for intelligent physical behavior.
3) the intelligence of “physical” intelligence.
Part I: Morphology
by Itself
![Page 7: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/7.jpg)
Morphology by itself: PDWs
Andy Ruina and friends: Passive Dynamic Walking (PDW)
* inverted pendulum model: given a stochastic input (simple oscillator, finite
energetic input), stable gait can be physically approximated.
* bipedal: hindlimbs – human gait,
forelimbs – gibbon brachiation.
* no neuromuscular or cognitive
feedback, no mechanotransduction
(e.g. efference copy).
* when environmental conditions
are variable, gait is not stable.
![Page 8: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/8.jpg)
Morphology by itself: PDWs (con’t)
Honda‟s ASIMO: demonstrates basic application of how bipedal gait is
regulated (also falls down a lot).
* afferent signal (tells legs to move)
* morphology reinforces efficiency
of movement.
* efference copy (feedback from
environment)
No “biological” component (e.g. muscle plasticity, neuroplasticity, learning
and memory).
* what would a “biological” controller look like?
![Page 9: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/9.jpg)
Morphology by itself: PDWs (con’t)
Key feature of PDWs: behavior for
“free”.
* bipedal gait = zero net energy
expenditure given constant
movement (no adaptive adjustments).
Stable state discovery: Sherrington
(Integrative Action of the Nervous
System, 1947):
* amputate one limb, insect finds new 'stable phase' for motility.
* robotics/postural sway work: „internal‟ mechanisms perform relevant
computations.
![Page 10: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/10.jpg)
Morphology by itself: stability
enforcement mechanisms
Mechanism #1: “static” allometry:
*controls the size of limbs relative to one
another and body size.
* basis for metabolic efficiency (cost of
locomotion decreases as body weight
increases in quadrupeds).
Body weight + limb shape +
forces in environment = cost of
transport.
* linear function, true for many varieties of
quadruped (see graph).
* cost of transport ~ muscle power (output)
needed for specific tasks and environments.From: Herr et.al (J. Experimental Biology,
205, 2005)
![Page 11: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/11.jpg)
Morphology by itself: stability
enforcement mechanisms (con’t)
From: Bejan and Marden, J. of
Experimental Biology, 209, 238.
“Constructal” effects across phylogeny
(energy needs during locomotion = strong
positive selection on morphology):
* vary environment (air, water; variables = Reynolds
number, surface reaction forces)
* vary mode of locomotion (running, swimming;
variables embodied in velocity, frequency, force).
* linear scaling for all verts/inverts. Swimming
(fishes), flying (birds, bats, insects), running
(mammals, reptiles) all “cluster” along same trend
line (force production vs. body mass).
![Page 12: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/12.jpg)
Morphology by itself: stability
enforcement mechanisms (con’t)Mechanism #2: matched volumes. MacIver‟s
simulation of Apternotus albifrons (Nelson and
MacIver, J. Experimental Biology, 202(10),
1999):
Weakly electric fish have a
special sensory modality called
electroreception.
* “active” (e.g. field generated around
organism).
* originated from neuromuscular system,
important in navigation.
* “map” at right is the electrosensory
field as it overlaps with “short-time
motor volume”.
![Page 13: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/13.jpg)
Morphology by itself: stability
enforcement mechanisms (con’t)
Active sensing in context of set matching:
* actively sense at time t; at every t, iteratively
create vol(x) based on current environment.
* fill in space vol(x) with form(y); shift ith set
of motor commands towards leading front of
movement and exploration (optimize degree of
isomorphy).
* tail bending behavior (Behrend, Neuroscience,
13, 171-178, 1984); introduces "critical"
exploration points.
* electrodermal potential changes during tail
bending, potentially shifts the phase of short-
time motor volume.
* a "memory" of interaction (sensory inputs|limb size x muscle power); acts as an integrator
mechanism (allometric scaling in development and evolution ensures control).
![Page 14: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/14.jpg)
1) biological A.I. (hybrots = cortical cells for computational environments)
2) neural coding (movement vector) and applicability to A.I. problems.
3) future advances: molecular models.
Part II: Nervous System
by Itself
![Page 15: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/15.jpg)
Nervous system by itself: biological
AI = hybrots
In experiments by Reger et.al (Artificial Life, 10, 2000), hindbrain of
lamprey explanted and connected to Khepera robot.
* artificial photoreceptors from robot body provided input channel to Muller
cells, play the role of sensorimotor integration in lamprey brainstem.
* sensors on the robot's body = inputs to neural system. Resulting control
loop allows for adaptive behavior.
![Page 16: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/16.jpg)
“Brain-in-a-dish”: collective
output, environmental
feedback (simulation).
* at left is an example of an
adaptive flight control
system.
* software is used to find
“taxic” information in neural
output.
* signals “mapped” to degrees
of freedom in the simulation
(roll, pitch, and yaw).
Nervous system by itself: biological
AI = hybrots (con’t)
DeMarse and Dockendorf, IEEE International Joint
Conference on Neural Networks, 3, 1548-1551,
2005.
![Page 17: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/17.jpg)
Nervous system by itself: biological
AI = hybrots (con’t)
Control systems called hybrots have been
used to map neural signals to “skilled”
behaviors, such as drawing on an easel.
* cell culture of cortical neurons that
selectively grow connections between
neurons and show postsynaptic
modification (neuroplasticity).
* systems inform general processes behind
learning and memory in systems where
biology and machines are tightly coupled.
![Page 18: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/18.jpg)
Nervous system by itself: applied
neural codingPole balancing (neural integrator keeps pole
from falling due to inertia or gravity):
* 1 DOF, “toy” problem.
* reinforcement learning methods solve this problem well
(actor-critic model).
* perceptron can be used to calculate and encode information
for movement direction, velocity, etc.
* does not approximate complex physiologically-based
functions (dampening, rate limiting).
See Broussard and Kassardjian, Learning
and Memory, 11, 127, 2004.
![Page 19: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/19.jpg)
In mammals, neurons in premotor and motor cortex (PMC) contribute
to planning and directionality of movement:
* activity onset is 1-2 seconds before actual behavior.
* a "population code" (collective encoding of single behavioral events by
neuronal cell populations) has been found to exist.
* population coding may be important for other functions (memory encoding,
satiety states, etc).
Movement vector: Georgeopoulos et al (Journal of Neuroscience, 2, 1987):
* single cell activity in premotor and motor cortex predicts direction
of movement, mental rotation, force and velocity parameters.
Nervous system by itself: applied
neural coding (con’t)
![Page 20: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/20.jpg)
The collective activity of cells results
in the encoding of desired behavioral
states.
* average activity of a population is greatest
in a certain direction(e.g. 45, 90, 155 degrees
from straight ahead).
* used as the driving
force behind Brain-
Machine Interface
(BMI) technology.
Nervous system by itself: applied
neural coding (con’t)
![Page 21: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/21.jpg)
Nervous System by itself: future
advances -- molecular modelsMechanostimulation:
* activates stress pathway in cell populations.
* within minutes of stimulation, series of genes
upregulated (enhanced expression).
* in preconditioning, low levels of perturbation
increase robustness of system to acute shocks.
* depending on stimulus (environmental setting),
different regulatory patterns should result.
* patterns not well understood: what are the
effects of environmental switching, mutation of
genes involved in stimulus response, long-term
adaptation?
![Page 22: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/22.jpg)
Nervous System by itself: future
advances -- molecular models (con’t)
Signaling pathways in memory-associated plasticity in brain (left - CREB)
and hypertrophy-associated plasticity in muscle (right - IGF):
Activity of pathways change across
training, interaction with environment.
* One emergent property of gene
expression and regulation = change in
morphology and internal state (figures:
http://www.biocarta.com).
Presence of hormone receptors, proteins and mRNAs in specific concentrations
(activity-dependence). Contributes to plasticity outcome (“increased/decreased
capacity” of tissues).
![Page 23: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/23.jpg)
How do we piece together the interesting aspects of morphology and neural
systems into one unified framework/approach?
1) functional allometry/epigenetic matching
2) neurobiological control theory
Part III: Morphology and
Brain Together
![Page 24: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/24.jpg)
Allometry: different anatomical segments are genetically “linked”. Consequences
for growth regulation and function within and between species.
Y = ax + b, Y = axb, Y = -Ax2 + Bx – c
Functional effects of allometry:
Herr et.al (J. Experimental Biology, 205, 2005):
* allometric scaling is a feature of "optimal“
locomotion and goal-directed behavior. Limb
length, circumference, brain size, metabolic
rate ~ body mass.
* provides a mechanism for determining
"optimal" scaling.
* Collins et.al (Science, 307, 1996) have
found that there is an optimal ratio of 1.06
between the length of the shank and thigh
in human bipedalism.
Morphology + Brain: functional
allometry/epigenetic matching
![Page 25: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/25.jpg)
Morphology + Brain: functional
allometry/epigenetic matching (con’t)Epigenetic Matching: motorneuron population ~ target tissue (allometry and
growth regulation of target tissues ~ evolution and adaptability of nervous
system):
Streidter (Principles
of Brain Evolution,
Sinauer, 2006)
* finite pool of
motorneurons,
finite volume
of muscle target
tissue (myocytes).
* if axon from
motorneuron does
not innervates target
tissue = apoptosis.
![Page 26: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/26.jpg)
Morphology + Brain: functional
allometry/epigenetic matching (con’t)
Katz and Lasek (PNAS USA, 75(3), 1977): Type I and Type II evolution.
* Type I: “linkage” (neuron-to-myocyte matching; innervational “linkage”
between two sets of cells).
* conservation via hormone action, high degree of epistasis, high degree of
evolution (no developmental constraint).
Type II: no autonomous preservation of axonally-mediated matches (no
innervational linkage between two sets of cells).
* depends on function of interactome, serves as evolutionary constraint
(unless mutation introduced for both motorneuron pool and muscle mass,
complexity remains low).
![Page 27: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/27.jpg)
Morphology + Brain:
neurobiological control theory
Computational Neurobiology of Reaching and
Pointing: Reza Shadmehr (Johns Hopkins) and
Steven Wise.
* internal states not a black box, play an
important role in regulating behaviors
(normal and pathological).
* internal “model” is a statistical mechanism
(others are more interested in the internal
model as anatomical ROI).
* internal model = memory-based
displacement mechanism. Updates =
incoming physical sensory information,
visual information, and prior states.
![Page 28: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/28.jpg)
Morphology + Brain: neurobiological
control theory (con’t)Reaching involves contributions from
both the CNS and constraints imposed
by limb geometry (230 and 137):
*anatomical stiffness ~ constraints.
Stiffness = stability.
* disease states (e.g. Parkinson‟s):
represents perturbation of neural
mechanisms involved with “normal”
movement (135).
* cerebellar, basal ganglia components
of learning system = nuclei, synapses
mediated by neurotransmitters (456).
Reinforcement learning mechanism.
![Page 29: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/29.jpg)
Morphology + Brain: neurobiological
control theory (con’t)Internal Model: Computational function of
cerebellum:
* internal model is highly
conserved across vertebrates.
* general (innate) and specific
(acquired) internal models.
* innate: general limb
movements, environmental
resistance.
* specific: single and related
sets of objects.
![Page 30: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/30.jpg)
How does evolution of the nervous system and morphology (as a unified
system) proceed phylogenetically?
* what “strategies” (e.g. combination of mutations, adaptations) are used to achieve a
derived form?
* three slides with hypothetical phylogenies only suggestive (focus on locomotive gait --
could have happened many different ways, and actually has in terms of convergent
evolution).
Postscript: “solutions” for
evolving physical
intelligence
![Page 31: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/31.jpg)
Phylogenetic “solutions” application
domain: morpho-functional machines
Defined as the co-evolution of morphology and control unit:
* change functionality by changing control parameters and shape.
* evolve whole system in pieces, or modules (specialized substructures or
distinct behaviors).
* evolve morphology (morphogenesis) semi-independently from neural
controller.
* evolution of both morphology and neural mechanisms define a particular
evolutionary derivation (but multiple evolutionary “strategies”).
![Page 32: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/32.jpg)
Phylogenetic “solutions” to evolving
physical intelligence
At left: how Type I and II
evolution may proceed:
Cladogenesis requires
generalized capacity for
plasticity.
* one mutation, may trigger
endocrine plasticity.
Anagenetic taxa may
require two specialized
mutations.
* morphology and nervous
system specialized but not
evolvable.
![Page 33: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/33.jpg)
Phylogenetic “solutions” to evolving
physical intelligence (con’t)
At left: how static allometry
in hindlimb evolves along
mode of gait.
* gene controlling thigh
plasticity evolves before
common ancestor of C,
D, E, and F.
* bipedalism evolves in F
(requires other associated
mutations).
* genes “unlinked” by thigh
plasticity mutation, “relinked”
when bipedalism arises.
![Page 34: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/34.jpg)
Phylogenetic “solutions” to evolving
physical intelligence (con’t)
At left: how to move from
one physically intelligent
mode to another in
evolution:
* three behavior-related
mutations to go from
specialized quadruped to
a biped (probably more).
* also anatomical changes
(joint morphology, spinal
cord alignment).
* behavioral mutations >
anatomical mutations
(which come first)?
![Page 35: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/35.jpg)
Conclusions“Physically” Intelligent Systems:
1) Consider morphology and physiology together
* provides a mechanism for dynamic behavior
* emergent features of physiological interactions – constrained by morphology
2) Dissociate morphology and physiology for purposes of understanding
phylogeny
* shared derived characters (changes in phylogeny required for behavior,
match phenotype?)
* possible control mechanisms (morphology, genes, regulatory mechanisms)
3) Computational Principles
* What else is needed? What other tools can be deployed?
![Page 36: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/36.jpg)
Additional Notes:
![Page 37: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/37.jpg)
Comparative function and main neural centers:
Each brain center has a
specific computational
function:
* integration, acquisition,
encoding, and recall of
information.
* work together as an
anatomical network
to send feedforward
information to limbs.
* cross-talk between
networks.
Interacting Neural Systems and
Crosstalk: an “inconvenient truth”
![Page 38: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/38.jpg)
Notes on Passive Sensing
(according to me)Passive sensing in the context of moving a limb towards a target:
* uncontrolled manifold hypothesis (Domkin et.al,
Experimental Brain Research, 163(1), 2005). Arm
has many DOFs with which it can potentially
reach an object.
* no finite sensory envelope, dynamic opposition
of forces from environment determine manifold
for movement.
* lots of behavioral variability as compared
with orthogonal manifold (set of solutions
chosen by CNS).
* scaling (geometry) of limbs important to
constrain what functional manifolds look
like in adulthood (also limits mathematical
solutions for SI|LS x MP).
* motor primitives in spinal cord (see Mussa-Ivaldi and Arbib) – combinatorially
put together to drive outputs based on current environmental demands.
![Page 39: physiology, robotics, and computational biologyaliceabr/EI_talk_fall_2007.pdf · * serves “pattern prediction” function * memory-based, adaptive, hierarchical * has an effect](https://reader034.vdocuments.net/reader034/viewer/2022042105/5e83e10ed590305c057b3b87/html5/thumbnails/39.jpg)
Bayesian-Systems Model of
Adaptation via Molecular Pathways
A preliminary “model” of signal
transduction in a cell w.r.t. motor
performance (mechanotransduction
and control).
* expression of genes in tissues ~
properties of tissues. Each set of
relationships for single cell, many of
these in parallel ~ tissue.
* may be able to approximate emergent
changes in tissues ~ changes in
performance, morphological adaptation
(ability to encode adaptive changes).