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Design principles for adaptive self-organizing systems

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Title: Design principles for adaptive self-organizing systems


1
Design principles for adaptive self-organizing
systems
  • Finding Fluid Form Symposium
  • University of Brighton
  • December 9-10, 2005

Peter Cariani
www.cariani.com
Department of Physiology Tufts Medical
School Boston
2
Organismic biology (undergrad _at_ MIT mid
1970s)Biological cybernetics epistemology
(1980s) Biological alternatives to symbolic
AI Howard Pattee, Systems Science,
SUNY-BinghamtonTemporal coding of pitch timbre
(1990s) Auditory neurophysiology,
neurocomputation How is information represented
in brains? Commonalities of coding across
modality phyla Neural timing nets for temporal
processing Auditory scene analysis Possibilities
inherent in time codes Temporal alternatives to
connectionism signal multiplexing adaptive
signal creation broadcast
My trajectory
3
Elaboration of structures functions over time
in biological, social, and technological realms,
What makes new functions possible (functional
emergence)? Can we put these principles to work
for us?Is structural complexification by itself
sufficient? (No)Notions of function functional
emergence are needed.What kinds of functions?
Sensing, effecting, coordinatingIs pure
computation on symbols sufficient? (No)How are
brains/minds capable of open-ended
creativity?Neural codes, temporal codes, timing
nets Neural coding of pitch in the auditory
systemRethinking the architecture of the brain
Temporal alternatives to connectionism Adaptive
signal creation multiplexing, Broadcast
coordinative strategies
Evolution of ideas
4
Combinatoric vs. creative emergence
5
An example
Exhaustive description
Limited description
All permutations of single digits 0 1 2 3 4 5 6 7
8 9 consisting of 6 tokens
All permutations of 6 arbitrarily defined objects
One well-defined set having 610
permutations BOUNDED
Ill-defined number of sets, each w. 610
permutations UNBOUNDED
6
Describing the world Two perspectives
Omniscent Gods eye view Postulational, ontologi
cal analytical mode
Perspective of the limited observer epistemologic
al empirical mode
Appearance of new structures over time
Violations of expectations Surprise
7
Well-defined vs. ill-defined realms
Exhaustive description Gods eye view
Limited description Limited observer
Environment as ill-defined realm
System-environment as well-defined realm
Description is dependent on set of
observables (environment has as many properties
as one can measure)
Description of all-possible organism-environment
relations
CLOSED WORLD ASSUMPTION
OPEN WORLD ASSUMPTION
No fundamental novelty is possible All novelty is
combinatoric
Combinatoric and Creative emergence
8
New features
CREATING A NEW OBSERVABLE ADDS A NEW
PRIMITIVE THAT INCREASES THE EFFECTIVE
DIMENSIONALITY OF THE SYSTEM
9
Ontology Aristotelian hylomorphism Material
substrate that exists independently of us, yet
whose form is largely ill-defined, incompletely
known Organization is embedded in material
system (e.g. mind is the organization of the
nervous system) Conscious awareness requires a
particular kind of regenerative informational
organization embedded in a material system
(cybernetic functionalism) Aristotle's
Causes Multiple complementary modes of
explanation that answer different kinds of
questions
Philosophy
10
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11
Epistemology Pragmatism (truth of a model
related to its purpose) Perspective of the
limited observer Relativism different
observational frames purposes Analytical,
empirical and pragmatic truths Analytic truths
of convention (non-material truths, finist
mathematics) Empirical truths of measurement,
observation (science) Pragmatic truths of
efficacy aesthetics (engineering,
art) Constructivism epistemic autonomy by
semi-freely choosing our own observables
concepts, we construct ourselves (for better or
worse)
Philosophy
12
We are interested in designing fabricating
systems that autonomously organize themselvesto
elaborate structures improve functions in
response to challenges of their environments in
ways that are meaningful and useful to us and/or
them
Design principles for adaptive, self-organizing
systems
13
Richness of material possibility (e.g. polymeric
combinatorics) Ability to steer stabilize
structure (feedback to structure sensors,
coordination mechanisms, effectors) Means to
interact w. material world (sensing, action
"situatedness", semantics) Means to evaluate
actions re purposes (goal-laden
representations, "intentionality")---------------
--------------------------------------------------
-----------------------gt Material system
capable of adaptive, elaboration improvement
of informational functions
Design principles for adaptive, self-organizing
systems
14
Richness of material possibility (need polymers,
replicated aperiodic structure, Schrodinger's
aperiodic crystal, analog dynamics, ill-defined
interactions)Ability to steer stabilize
structure(need controls on self-production of
internal structure, enzymes)Means to interact w.
material world(Need sensors, effectors, neural
nets)Means to evaluate actions re
purposes(Need natural selection or internal goal
states, limbic system)
Design principles for adaptive, self-organizing
systems
15
Vibratory dynamics of matter Cymatics Bringing
Matter to Life with Sound Hans Jenny
Richness of material possibility Complexity is
easy Steerable complexity is hard
16
Material possibility Steer, stabilize, specify,
inherit Sensorimotor interaction Evaluation
gt ASOS
Design principles for adaptive, self-organizing
systems
VARIATION SELECTION INHERITANCE gt ADAPTATION
Two phases in creative learning
processes Expansive phase generation of
possibility Realm of free open creation e.g.
scientific imagination and hypothesis creation
Contractive phase selection of best
possibilities Realm of clarity rigorous
evaluation e.g. hypothesis testing (clarity,
removal of ambiguity)
17
Analog dynamics and discrete symbols
  • We will also argue that one almost inevitably
    needs
  • mixed analog-digital systems for complex systems
  • i.e. systems w. analog dynamics constrained by
    digital states ("symbols")
  • for reliable replication of function
  • for inheritability of adaptive improvements
  • Analog and digital are complementary modes of
    description
  • analog descriptions - continuous differential
    equations
  • digital descriptions - discrete states ST
    rules/probabilities
  • Digital states or discrete symbols are
    ultrastable basins of attraction

18
Different theoretical approaches tounderstanding
brains and their functions
Dynamical systems approaches
Neural information processing
Symbol- processing
differential growth homeostasis analog
representations processing
states switches branching discrete
19
Requisite sensorimotor loopsInner and outer
loops
20
Von Uexkülls umwelts
21
McCullochsinternal andexternal loops
22
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23
Self-conscious description of the modeling
processHertzian modeling relation measurement
computation
24
The choice of observables
Finding the variables The would-be model maker is
now in the extremely common situation of facing
some incompletely defined "system," that he
proposes to study through a study of "its
variables." Then comes the problem of the
infinity of variables available in this universe,
which subset shall he take? What methods can he
use for selecting them? W. Ross Ashby, "Analysis
of the system to be modeled" in The Process of
Model-Building in the Behavioral Sciences, Ohio
State Press, pp. 94-114 reprinted in Conant, ed.
Mechanisms of Intelligence
25
The choice of observables - analogous problems
  1. Choice of primitive features for classifiers
  2. Evolution of sensory organs in organisms
  3. Choice of sensors for robots

26
Semioticsof adaptivedevices
Feedback to state Feedback to structure
alters functionalities
27
Semiotic relations (Charles Morris)
MEANING
PURPOSE
28
Evaluate re goals Frontal limbic systems
Internally generated pattern sequences
sensory systems
motor systems
29
Adaptivity in percept-action loops (Cariani)
30
Pure computation (state-determined system, no
independent informational transactions w.
environment)
31
Fixed robotic device
Fixed sensors, coordinators, and
effectors Purely reactive and driven by its
inputs Incapable of learning
32
Computationallyadaptivedevice
Trainable machines Neural networks Adaptive
classifiers Genetic algorithms Robots w. adaptive
programs Capable of learning new
percept-action mappings (classifications)
33
Some observations about adaptability
  • Whatever functionalities are fixed, the designer
    must specify
  • works for well-defined problems solutions
  • advantage predictable, reliable behavior
  • drawback problems of specification
  • Whatever is made adaptive must undergo a learning
    phase
  • needed for ill-defined problems solutions
  • some unpredictability of solutions found
  • creative behavior!
  • the more autonomy, the more potentially creative
  • Consequently, there are tradeoffs between
  • adaptability efficiency
  • autonomy/creativity control/predictability

34
Evolution/adaptive construction of new sensors
sensory evolution immune systems perceptual
learning
capable of learning new perceptual
categories new feature primitives (new
observables)
35
Epistemic autonomy
When a system can choose its own categories
through which it perceives and acts on the world
that system achieves some limited degree of
epistemic autonomy. A rudimentary
electrochemical device was built by cyberneticist
Gordon Pask in 1958 that grew its own sensors to
create its own relevance criteria.
36
"With this ability to make or select proper
filters on its inputs, such a device explains the
central problem of epistemology. The riddles of
stimulus equivalence or of local circuit action
in the brain remain only as parochial problems."
. Warren McCulloch,
preface ,Gordon Pask (1961)
. An Approach to Cybernetics.
37
Principles of action/use 1. Front-ends for
trainable classifiers Useful in ill-defined
situations where one does not a priori know what
features are adequate to effect a
classification 2. Adaptive, self-organizing
sensors Grow structures over analog-VLSI
electrode arrays in order to sense new aspects of
the world. Use biochemical and/or biological
systems coupled to an electrode array 3.
Materially-based generator of new behaviors
(adaptive pattern-generators) Similar steerable,
ill-defined systems could be used to generate new
patterns (sound, images) in an open-ended way
that is not at all obvious to the
observer/controller 4. Epistemic
autonomy Device chooses how it will be connected
to the outside world what aspects of the
material world (categories) are relevant to it.
(Symbol grounding, frame problem)
38
Feedback to state vs. feedback to structure
A thermostat is limited in the information that
it can gain from its environment by the fixed
nature of its sensors. It has feedback to state,
but not feedback to structure. The amount of
information that such a system can extract from
its environment is finite at any time, and
bounded by its fixed structure. A system capable
of sensory evolution or perceptual learning has
the ability to change its relation to its
environs. Such a system has an open-ended set of
observational primitives. It has both feedback to
state and feedback to structure. The amount of
information that such a system can extract from
its environment is finite at any time, but
unbounded. Such a system is open-ended.
39
Analog dynamics without inheritable constraint
(Hans Jenny)
40
von Neumann's kinematic (robotic)
self-reproducing automaton (1948)
41
Inheritable constructionanalog dynamics
constrained selected by discrete symbols
Purely analog adaptive system must be
trained each generation
Genetic algorithm Pattern grammar for guiding
construction constrained search
Symbolically-encoded memory permits results of an
optimization process to be passed to
subsequent generations
42
The homeostat
43
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44
Relation to Ashby's homeostat
Analog sensor/controller
Uniselector 25x25x25x25 390k construction
possibilities -gt variety of the control system,
unconstrained search
Evaluation of ability to control inputs
45
Relation to Ashby's homeostat
Analog sensor/controller
Uniselector 25x25x25x25 390k construction
possibilities -gt variety of the control system,
unconstrained search
Evaluation of ability to control inputs
46
Ashby's homeostat
Analog controller (ill-defined structure)
Adaptive analog controller Structure of
particular controllers is unknown to
designer Requisite variety for control is the
number of alternative controllers available 25x25x
25 390,625
Environment
47
The homeostat the brain
  • A few cybernetics-inspired accounts of brain
    function
  • Sommerhoff (1974) Logic of the Living Brain
  • Klopf, The Selfish Neuron
  • Arbib, The Metaphorical Brain
  • Most successful neuroscientific application of
    cybernetics
  • W.Reichardt's analysis of fly optomotor loop
  • The homeostat never caught on as a brain metaphor
  • Some possible reasons
  • Homeostats never were cast in terms of neural
    nets
  • No obvious digital uniselector function in the
    brain
  • Predominance of problems of pattern recognition
    and
  • formulation of coherent action over simple
    problems of internal regulation

48
The brain as an adaptive self-organizing system
  • Ideas that flow from cybernetics and theoretical
    biology
  • Brains as signal self-production systems
  • related to reverberant loops (a la Lorente,
    Lashley, Hebb, McCulloch, Pitts many others)
  • 2) Brains as pattern-resonance systems
  • related to Lashley, Hebb, many others
  • 3) Brains as multiplexed signaling and storage
    systems
  • holographic paradigms, Longuet-Higgins,
    Pribram,John
  • 4) Brains as mass-dynamics, broadcast systems
  • 5) Brains as communications nets that create new
    signals
  • 6) Brains as temporally-coded pulse pattern
    systems
  • I believe all this is possible using temporal
    pattern codes.

49
Regeneration of parts
50
Von Neumanns kinematic self-reproducing automaton
51
Autopoiesis and autocatalysis
52
Symbolically-guided self-production
53
Autopoiesis and autocatalysisLife is built upon
cycles of self-production
54
Brain function may be based on self-productions
of spike patternsHebbian reverberant eigenstates
and regenerative temporal patterns
McCulloch Pitts (1943) Nets with circles render
activity independent of time and semi-autonomous
re the environment von Foerster (1948) brain
eigenstates as a form of ST memory
55
Why the mind is in the head Warren
McCulloch L.A. Jeffress, ed. Cerebral Mechanisms
of Behavior (The Hixon Symposium, Wiley, 1951,
reprinted in Embodiments of Mind, MIT, 1965,
concluding lines)
This brings us back to what I believe is the
answer to the question Why is the mind in the
head? Because there, and only there, are hosts
of possible connections to be formed as time and
circumstance demand. Each new connection serves
to set the stage for others yet to come and
better fitted to adapt us to the world, for
through the cortex pass the greatest inverse
feedbacks whose function is the purposive life of
the human intellect. The joy of creating
ideals, new and eternal, in and of a world, old
and temporal, robots have it not. For this my
Mother bore me.
56
The brain as aself-regeneratingpattern-resonance
system
57
The same resonance is true of all bodies
which can yield notes. Tumblers resound when a
piano is played, on the striking of certain
notes, and so do window panes. Nor is the
phenomenon without analogy in different
provinces. Take a dog that answers to the name
"Nero." He lies under your table. You speak of
Domitian, Vespasian, and Marcus Aurelius
Antonius, you call upon all the Roman Emperors
that occur to you, but the dog does not stir,
although a slight tremor of his ear tells you
of a faint response of his consciousness. But
the moment you call "Nero" he jumps joyfully
towards you. The tuning fork is like your dog. It
answers to the name A.Ernst Mach, Popular
Lectures, The fibers of Corti c. 1865
Tuning in nervous systems Minds as
pattern-resonances
58
Pattern resonances neural assemblies emitting
annotative tag signals that elaborate a
regenerating signal pattern
59
Temporal pattern codes
Phase-locking in visual neurons (Horseshoe crab
ommatidium, 5-15 Hz flashes)
Phase-locking in auditoryl neurons Cat auditory
nerve fibers, 250 Hz tone
Javel,
60
Phase-locking in auditory nerve fibers
250 Hz tone
61
Frequency and time in the auditory nerve
62
Phase-locking of discharges in the auditory nerve
Cat, 100x _at_ 60 dB SPL
63
Temporal codingin the auditory nerve
Work with Bertrand Delgutte Cariani Delgutte
(1996) Dial-anesthetized cats. 100
presentations/fiber 60 dB SPL Population-interval
distributions are compiled by summing together
intervals from all auditory nerve fibers. The
most common intervals present in the auditory
nerve are invariably related to the pitches heard
at the fundamentals of harmonic complexes.
64
Phase-locking in visual thalamus (LGN)
Stimuli Drifting sinusoidal gratings
65
Color vision
66
Temporal coding of taste
67
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68
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69
Neural timing nets
RECURRENT TIMING NETS Build up pattern
invariances Detect periodic patterns Separate
auditory objects
FEED-FORWARD TIMING NETS Temporal sieves
Extract (embedded) similarities Multiply
autocorrelations
70
Potential advantages of temporal pattern pulse
codes timing nets
  • Multiplexed signal transmission
  • Orthogonality of patterns less interference
  • Flexible multimodal integration
  • Encoding of signal identity in itself (logical
    type)
  • Liberate signals from wires
  • Broadcast of signals selective reception
  • Nonlocal computational operations
  • Mass action (statistical representations)
  • Open-ended creation of new signal primitives

71
Music, brain, and time
In the image of the digital computer, we
conceptualize brains as distributed logic
machines. However, temporal correlation
machines may prove to be a better
metaphor. Temporal expectancies in
perception Temporal patterning of body
processes Temporal structure of
movement Temporal expectations and reward
structure (dopamine system, conditioning)
Temporal memory traces Music may have the
profound effects that it does because 1) it
directly impresses its temporal structure on the
activity of many neuronal populations, and 2)
the neural codes computations underlying
experience are inherently temporal.
Andy Partridge, xtc
72
Structural complexity alone is not
sufficientPure computation alone is not
sufficientRequisitesSensors effectorsMixed
digital-analog designFeedback to structure,
self-productionInheritable, replicable
(digital) plansCombinatorics of digital
stringsRich analog, ill-defined dynamicsGoal
states and steering/selection mechanismsPossibil
ity of brain as temporally-coded
self- organizing system
Conclusions
Design principles for self-organizing systems
73
Temporal coding of sensory information
74
From cochlea to cortex
Primary auditory cortex (Auditory forebrain)
Auditory thalamus
Inferior colliculus (Auditory midbrain)
Lateral lemniscus
Auditory brainstem
Auditory nerve (VIII)
Cochlea
75
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76
Phase-locking to a 300 Hz pure tone
Period histogram (1100 Hz)
First-order interval histogram (1500 Hz)
spikes
Evans, 1982
77
Auditory nerve
78
Vowel Formant Regions
79
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80
Codes are defined in terms of their functional
roles
What spike train messages have the same
meanings? (functional equivalence classes)
What constitutes a difference that makes a
difference?
Temporal codes are neural codes in which timings
of spikes relative to each other are essential to
their interpretation.
81
Neural resonances
82
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83
Phase-locking of an LGN unit to a drifting
sinusoidal grating
Temporal modulation frequency
Interval Histograms
PST Histograms
4 Hz
8 Hz
16 Hz
32 Hz
64 Hz
84
Adaptation adjustment
Adaptive systems
Sensing measurement
Depending upon the self-modification process,
adaptive systems change in different ways.
They become tuned to their environments, on the
percept on the action side internally
anticipating events, forecasting effects New
sensors create new linkages with the external
world new perceptual primitives new
observables new modes of adjustment New
effectors create new modes of action
85
Switching between reverberant states
86
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87
Frequency ranges of (tonal) musical instruments
10k 8 6 5 4 3 2 1 0.5 0.25
88
Measurement and tuning
Measurement mediates interactions with external
world permitting behavior contingent upon
perception
Adaptive systems that create their own
measurements are possible (we may be such
systems)
Tuning involves adjustment of internal relations
to external relations, i.e. adaptive resonance
It is possible to envision brains and minds as
resonant systems that operate on patterns rather
than coupled via energetic relations
89
Areas of self-modifying media
Self-modifying computers Coevolution between
humans and computers Emergent human-machine
couplings Pasks Conversation theory Computers
need means of independently accessing the world
and creating their own concepts (epistemic
autonomy) Self-organizing materials Electrochemi
cal Ferromagnetic Biological-silicon
interfaces Intelligent materials Mixed
digital-analog feedback systems
90
Phase-locking of an LGN unit to a drifting
sinusoidal grating
Temporal modulation frequency
Interval Histograms
PST Histograms
4 Hz
8 Hz
16 Hz
32 Hz
64 Hz
91
Phase-locking in visual thalamus (LGN)
Stimulus Drifting sinusoidal gratings
92
(No Transcript)
93
Neural timing nets
RECURRENT TIMING NETS Build up pattern
invariances Detect periodic patterns Separate
auditory objects
FEED-FORWARD TIMING NETS Temporal sieves
Extract (embedded) similarities Multiply
autocorrelations
94
Build-up and separation of two auditory objects
Two vowels with different fundamental frequencies
(F0s) are added together and passed through the
simple recurrent timing net. The two patterns
build up In the delay loops that have recurrence
times that correspond to their periods.
95
Methodological issuesWhat distinguishes sensing
from other kinds of informational operations?A
sensing process must be contingent, it must have
two or more possible outcomes to reduce
uncertainty, whereas A computation (formal
operation) must be logically-determined, it must
always produce the same outcome given the same
initial state
Sensing vs computing Contingent vs.
logically-necessary truths
96
Computers and brains
  • Digital computers presently are capable of
    recombination-based creativity, but do not
    presently create new primitives for themselves.
  • Brains, on the other hand, are self-modifying
    systems with rich analog dynamics that can serve
    as substrates for formation of new informational
    primitives.
  • Contemplation of self-modifying systems is
    essential if we are to construct artificial
    systems that can create meaning for themselves.
  • We need such systems when problems are
    ill-defined, or when we desire open-ended
    creative possibilities.

97
Neural resonances
98
Overview I Measurement in adaptive systems
We discuss the semiotics and functional
organization of different adaptive systems.
Adaptive systems reorganize their internal
structure in order to improve their performance.
We consider how systems with sensors,
effectors, and coordinative faculties can
adaptively modify their internal structures and
functions. We consider how this adaptivity
leads to emergent functions and behaviors.
99
Overview IV Creativity, autonomy, and
specification
  • Creativity has two levels
  • Recombination of existing primitives
  • De novo creation of new kinds of primitives
  • Inherent tradeoffs
  • Specifiability vs. autonomy
  • Predictability/reliability vs. creativity

100
Homeostat
101
Grey Walter's device
102
Conceptions of emergence
Appearance of new structures, functions,
behaviors Novelty that was not predictable from
what came before
Varieties
Structural emergence (appearance of new
structures, org. levels) Computational
emergence (unexpected results) Thermodynamic
emergence (dissipative systems) Functional
emergence (flight, color vision)
Emergence-relative-to-a-model (perspectivist,
operationalist)
103
Methodological issues
  • How can we identify the existence of information
    processing operations in artificial and natural
    systems?
  • How can we distinguish measurement, computation,
    and effector operations from each other in an
    unknown material system?
  • How can we detect changes in these
    functionalities, such that we know that our
    devices or organisms have modified them
    adaptively?
  • We need operational distinctions.
  • We need to be able to parse a state-transition
    graph.

104
Recognizing determinate contingent events
105
State-transitionsand observer-operations
How do we distinguishmeasurementsand
computations(such that wecan alsodetect
changesin system behavior)?
106
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107
Emergence relative to an observerWhat does the
observer have to do to his/her own model to
continue successfully predicting the material
systems behavior?
108
Evolution of observer's model
109
Opening up the sensory interfaceBreak-out
strategies for creating new observables
1) construction of new sensors 2) modification of
existing sensors 3) interposition of sensory
prostheses 4) active measurements 5) creation of
new internal sensors
110
Prosthesisaugmentation of functionalities
All technology is prosthesis.
111
Operational states and procedures in a
scientific model
Explicate realm of symbols (well-defined)
Implicate realm of material process (ill-defined)
112
Active measurement
113
Neural assemblies as internal sensors
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