Finding Fluid Form Symposium
University of Brighton
December 9-10, 2005
Design principles for
adaptive self-organizing systems
Peter Cariani
www.cariani.com
Department of Physiology
Tufts Medical School
Boston
My trajectory
Organismic biology (undergrad @ MIT mid 1970s)
Biological cybernetics & epistemology (1980s)
Biological alternatives to symbolic AI
Howard Pattee, Systems Science, SUNY-Binghamton
Temporal 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
Evolution of ideas
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, coordinating
Is 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 system
Rethinking the architecture of the brain:
Temporal alternatives to connectionism
Adaptive signal creation & multiplexing,
Broadcast coordinative strategies
Combinatoric vs. creative emergence
Combinatoric emergence:
New combinations
of pre-existing primitives
D M
1
F
5
Creative emergence:
De novo creation of new primitives
Sets of primitives
(axioms, atoms, states)
Add *, a
Processes for
combining primitives
1-2-F
1-M
D-F-1
2-F-M
M-D
Sets of
possible
combinations
of primitives
Process for
constructing
new
primitives
D M
F 1
*

5
1-2-F
D-F-1
-M
2-F-M
2-F-*
M-D
An example
Exhaustive description
Limited description
All permutations of
single digits
0123456789
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
Describing the world: Two perspectives
Omniscent
“God’s eye view”
Postulational,
ontological
analytical mode
Perspective of
the limited observer
epistemological
empirical mode
Appearance of new
structures over time
Violations of expectations
“Surprise”
Well-defined vs. ill-defined realms
Exhaustive description
God’s eye view
Limited description
Limited observer
System-environment as
well-defined realm
Environment as
ill-defined realm
Description of
all-possible
organism-environment
relations
Description is
dependent on set of observables
(environment has as many properties
as one can measure)
CLOSED WORLD ASSUMPTION
No fundamental novelty is possible
All novelty is combinatoric
OPEN WORLD ASSUMPTION
Combinatoric and
Creative emergence
New features
Effect of adding a new observable
CREATING A NEW OBSERVABLE ADDS A NEW PRIMITIVE
THAT INCREASES THE EFFECTIVE DIMENSIONALITY OF THE SYSTEM
Philosophy
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
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)
Design principles for
adaptive, self-organizing systems
We are interested in
designing & fabricating systems that
autonomously organize themselves
to 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
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")
----------------------------------------------------------------------------------------
=> Material system capable of adaptive,
elaboration & improvement of informational
functions
Design principles for adaptive, self-organizing systems
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)
Vibratory dynamics of matter
Cymatics:
Bringing Matter to Life
with Sound
Hans Jenny
Richness of material possibility
Complexity is easy
Steerable complexity is hard
QuickTime™ and a
USBVision decompressor
are needed to see this picture.
Design principles for adaptive, self-organizing systems
VARIATION + SELECTION + INHERITANCE => ADAPTATION
Material possibility+ Steer, stabilize, specify, inherit
+ Sensorimotor interaction + Evaluation => ASOS
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)
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
Different theoretical approaches to
understanding brains and their functions
Dynamical
systems
approaches
Neural
information
processing
differential growth
homeostasis
analog
representations
processing
Symbolprocessing
states & switches
branching
discrete
Requisite: sensorimotor loops
Inner and outer loops
action
interaction w.
environment
perception
metabolism: self-production
steering: percept-action coordinations
Von Uexküll’s umwelts
McCulloch’s
internal and
external loops
Self-conscious description of the modeling process:
Hertzian modeling relation: measurement & computation
A
Realm of
symbolic
description
Initial
Conditions
Si
Predictive model
Formal rules
(Syntactic)
Predicted result S p
Observed result S o
Encoding
(Semantic)
measure
Realm of
material
action
Observer's
choices:
what to predict
what to measure
(Pragmatic)
measure
Physical laws
World 1
World 2
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 ModelBuilding in the Behavioral Sciences, Ohio State Press, pp. 94-114; reprinted in
Conant, ed. Mechanisms of Intelligence
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
Effect of adding a new observable
Semiotics
of adaptive
devices
Feedback to state
Feedback to structure
alters functionalities
Semiotic relations (Charles Morris)
OTHER
SYMBOLS
Syntactics
rules on symbol - types
SYMBOL
(Functional state)
Pragmatics
valuations
SYSTEM-GOALS
PURPOSE
MEANING
Semantics
percept-action linkages
EXTERNAL
WORLD
B
Evaluate
re: goals
Frontal &
limbic
systems
DESIRES
DRIVES
BRAIN
THOUGHTS
Pragm atic
linkages
motor
Motor outputs
systems
Syntactic
linkages
Ex ternal
se mantic
linkages
SENSATIONS
Sens ory inputs
WORLD
Internally
generated
pattern
sequences
sensory
systems
Adaptivity in
percept-action
loops (Cariani)
C
Syntactic Axis
Semantic
Axis
Si
Sf
coordination
²
²
measure
evaluate
²
control
Pragmatic
Axis
environment
² = alter structure alter function
Pure computation (state-determined system, no
independent informational transactions w. environment)
Fixed robotic device
Fixed sensors,
coordinators,
and effectors;
Purely reactive
and driven
by its inputs;
Incapable of
learning
Computationally
adaptive
device
Trainable machines
Neural networks
Adaptive classifiers
Genetic algorithms
Robots w. adaptive
programs
Capable of learning
new percept-action
mappings
(classifications)
feature
vector
measure
computation
² training
action
vector
control
test
performance
environment
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
Evolution/adaptive construction of new sensors
sensory evolution
immune systems
perceptual learning
capable of learning
new perceptual
categories
new feature
primitives
(new observables)
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.”
"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.
From: "Physical analogues to the growth of a concept", Symposium on
the Mechanization of Thought Processes, National Physical Laboratories,
November 24-28, 1958, H.M.S.O., London, Volume II, p.919.
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)
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.
Analog dynamics without inheritable constraint (Hans Jenny)
QuickTime™ and a
USBVision decompressor
are needed to see this picture.
von Neumann's kinematic (robotic) self-reproducing automaton (1948)
Inheritable construction
feature
vector
computation
²
analog dynamics constrained &
selected by discrete symbols
measure
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
action
vector
²
construct
all parts of
the device
control
²
physical
construction
A 3
(mutation)
construction
possibilities
A B C D
   
   
construction
language
select from
existing alternatives
test
environment
performance
The homeostat
Relation to Ashby's
homeostat
feature
vector
measure
Analog
sensor/controller
computation
²
²
construct
all parts of
the device
control
²
physical
construction
A 3
Uniselector
25x25x25x25 = 390k
construction possibilities ->
variety of the control system,
unconstrained search
action
vector
(mutation)
construction
possibilities
A B C D
   
   
construction
language
select from
existing alternatives
test
Evaluation of ability
to control inputs
environment
performance
Relation to Ashby's
homeostat
feature
vector
measure
Analog
sensor/controller
computation
²
²
construct
all parts of
the device
control
²
physical
construction
A 3
Uniselector
25x25x25x25 = 390k
construction possibilities ->
variety of the control system,
unconstrained search
action
vector
(mutation)
construction
possibilities
A B C D
   
   
construction
language
select from
existing alternatives
test
Evaluation of ability
to control inputs
environment
performance
Ashby's homeostat
Adaptive analog
controller
Structure of
particular controllers
is unknown to designer
Requisite variety for
control is the number of
alternative controllers
available
25x25x25 = 390,625
Analog
controller
(ill-defined structure)
Uniselector
evaluate
(in bounds?)
Environment
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
The brain as an adaptive self-organizing system
Ideas that flow from cybernetics and theoretical biology:
1) 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.
Regeneration of parts
Von Neumann’s kinematic self-reproducing automaton
A
effect on survival
of whole system
genetic
replication of plans
construction
plans
byproduct D
F(D)
plans
F(A)
F(B)
F(C)
replication
of constructor
A+B+C
genetic
expression
apparatus
(universal
constructor)
Autopoiesis and autocatalysis
B
raw
materials
metabolic
loops
byproducts
Symbolically-guided self-production
C
set boundary
conditions
genetic
control
genetic
expression &
reproduction
genetic plans
(symbolic memory)
raw
materials
byproducts
Autopoiesis and autocatalysis
Life is built upon cycles of self-production
B
raw
materials
metabolic
loops
byproducts
Brain function may be based on self-productions of spike patterns
Hebbian 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
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.
The brain as a
self-regenerating
pattern-resonance
system
Tuning in nervous systems
Minds as pattern-resonances
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
Pattern resonances: neural assemblies emitting annotative
tag signals that elaborate a regenerating signal pattern
Inc oming sensory s ignals
Higher-order, more
complex interactions
Creation of new primitive time patterns
Primary
interactions
Secondary
interactions
Higher-order
interactions
Figure 7. Time-coded broadcast schema for asynchronous, heterarchical global integration.
Temporal pattern codes
Interspike interval code
Temporal Multiple intervals in same spike train
pattern
codes
Higher-order interval pattern
Phase-locking in auditoryl neurons
Cat auditory nerve fibers, 250 Hz tone
Phase-locking in visual neurons
(Horseshoe crab ommatidium, 5-15 Hz flashes)
Javel,
Miller, Ratliff, and Hartline (1961) How cells receive stimuli.
Scientific American 215(3):222-238.
Phase-locking in auditory nerve fibers
250 Hz tone
Javel E, McGee JA, Horst W, Farley GR, Temporal mechanisms in auditory stimulus coding.
In: G. M. Edelman, W. E. Gall and W. M. Cowan, ed, Auditory Function: Neurobiological
Bases of Hearing, Wiley: New York 1988; p. 518.
Frequency and time in the auditory nerve
Phase-locking of discharges in the auditory nerve
Cat, 100x @ 60 dB SPL
Temporal coding
in 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.
Phase-locking in
visual thalamus (LGN)
Stimuli:
Drifting
sinusoidal
gratings
Color vision
NaCl
Quinine HCl
Temporal coding
of taste
NTS
Temporal coding of taste
NaCl
Quinine HCl
Chorda
tympani
NTS
HCl
Sucrose
Chorda
tympani
Sucrose
HCl
NTS
NTS
Chorda
tympani
Chorda
tympani
Figure 12. Typical temporal patterns of response to four different stimuli recorded
from the chorda typani nerve and the NTS. The stimuli were: 0.1M NaCl, 0.1M
quinine HCl, 0.5M sucrose and 0.1M HCl. Time markers indicate 5 sec intervals. The
chorda tympani recordings are from the whole nerve, NTS recordings are from single
neurons. The tracings of relative spike frequency shown in this figure were obtained
during neural recording sessions by the output of amplified neural activity through a
spike amplitude window discriminator to a counting rate meter, the output of which
was displayed on a Brush pen writer.
From: Ellen Covey, Temporal Neural Coding of Gustation (1980),
Ph.D. thesis, Duke University.
Figure 12. Typical temporal patterns of response to four different stimuli recorded
from the chorda typani nerve and the NTS. The stimuli were: 0.1M NaCl, 0.1M
quinine HCl, 0.5M sucrose and 0.1M HCl. Time markers indicate 5 sec intervals. The
chorda tympani recordings are from the whole nerve, NTS recordings are from single
A. Time-division multiplexing in telephony (signals A-E)
A1
B1
C1
D1
cycle 1, slot s A-E
E1
A2
B2
C2
D2
cycle 2, slot s A-E
E2
time
B. Time-division multiplexing in neural systems
Scanning
Synchrony
Feature
detector
units
(neural
channels)
Sorting
by
synchronous
activation
time
Neural timing nets
FEED-FORWARD TIMING NETS
• Temporal sieves
• Extract (embedded) similarities
• Multiply autocorrelations
Si(t)
Sj(t)
two sets
of input
spike trains
Si(t) S j(t - )
individual
multiplicative
term
S (t ) S (t -t)
 i m j m
convolution
time-series
term m
Time
t
Relative delay
RECURRENT TIMING NETS
• Build up pattern invariances
• Detect periodic patterns
• Separate auditory objects
All time delays present


Time patterns reverberate
through delay loops

Recurrent,
indirect inputs

Coincidence
units
Direct inputs
Input time sequence
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
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
Conclusions
Design principles for self-organizing systems
Structural complexity alone is not sufficient
Pure computation alone is not sufficient
Requisites
Sensors & effectors
Mixed digital-analog design
Feedback to structure, self-production
Inheritable, replicable (digital) plans
Combinatorics of digital strings
Rich analog, ill-defined dynamics
Goal states and steering/selection mechanisms
Possibility of brain as temporally-coded selforganizing system
Temporal coding of sensory information
.
Pitch period
# intervals
Characteristic frequency (kHz)
Pitch period
0
Peristimulus time (ms)
5
10
15
20
Interspike interval (ms)
25
From cochlea to cortex
Primary
auditory cortex
(Auditory forebrain)
Auditory thalamus
Inferior colliculus
(Auditory midbrain)
Lateral lemniscus
Auditory brainstem
Auditory nerve (VIII)
Cochlea
Phase-locking to a 300 Hz pure tone
Period histogram
(1100 Hz)
First-order interval histogram (1500 Hz)
# spikes
Evans, 1982
Auditory nerve
Vowel Formant
Regions
Time domain analysis of auditory-nerve fiber firing rates.
Hugh Secker-Walker & Campbell Searle, J. Acoust. Soc. 88(3), 1990
Neural responses to /da/ @ 69 dB SPL from Miller and Sachs (1983)
Low
CF
F0
F1
F2
F3
High
CF
Peristimulus time (ms)
Neural pulse codes
Average discharge rate
Codes are defined in terms
of their functional roles
Ratechannel
codes
Interspike interval code
Multiplexed intervals
Temporal
pattern
codes
What spike train messages
have the same meanings?
(functional equivalence classes)
Higher-order interval pattern
Burst length, interburst interval
What constitutes
a difference
that makes a difference?
Spike latency
reference times
Timeof-arrival
codes
PST or latency pattern
Interneural synchrony
Temporal codes are neural
codes in which timings of
spikes relative to each other
are essential to their
interpretation.
Limbic & paralimbic areas
Neural resonances
long-term
memory
Unimodal
and multimodal
association
areas
Frontal cortical areas
pragmatic
evaluations
memory deliberation
higher
semantic
resonances
planning
memory
global workspace
memory
motor
preparations
perceptual
resonances
memory
Motor systems
motor
outputs
sensory
inputs
controlled
variables
(consequences
of actions)
Primary sensory
pathways
uncontrolled
variables
(contingent events)
ENVIRONMENT
Incoming sensory signals
Higher-order, more
complex interactions
Creation of new primitive time patterns
Primary
interactions
Secondary
interactions
Higher-order
interactions
Temporal
modulation
frequency
Phase-locking of an LGN unit to a
drifting sinusoidal grating
Interval Histograms
PST Histograms
1000
40
4 Hz
50
2000
40
1000
8 Hz
16 Hz
200
10
32 Hz
50
20
64 Hz
0
0
1000
2000
3000
4000
5000
Peristimulus time (ms)
0
100
200
300
400
500
All-order interval (ms)
Raw spike train data courtesy of Andrzej Przybyszewski & Dan Pollen
Adaptive systems
Adaptation ~ adjustment
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
Switching between reverberant states
A
Stimulus-driven switching between
reverberant circuits (after Hebb, 1965)
Potential
stimuli
S1
CNS
B
Potential
responses
Reverberant patterns
as switchable eigenstates
Contingent
sensory input
A
R1
R2
S2
B
Epistemic
cut
Resonant
state | A
Resonant
state | B
Motor
response
for A
Motor
response
for B
R3
S3
Functional organization of the perceptual side
Evaluation in terms
of manifold implications
(associations, plans
cognitive schemas)
selfsustaining
patterns
Evaluation in terms
of basic system-goals
(limbic system)
Attentional
facilitation
of image
formation
Attentional
facilitation
of image
formation
Buildup of
sensory
images
Early
sensory
codng
receptor surfaces
Sensory
transduction
Structure of
environmental
events
Frequency ranges of (tonal) musical instruments
27 Hz
110
Hz
262
Hz
440
Hz
880
Hz
10k
8
6
5
4
3
2
1
0.5
0.25
4 kHz
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
Areas of self-modifying media
Self-modifying computers
Coevolution between humans and computers
Emergent human-machine couplings
Pask’s Conversation theory
Computers need means of independently
accessing the world and creating their
own concepts (epistemic autonomy)
Self-organizing materials
Electrochemical
Ferromagnetic
Biological-silicon interfaces
Intelligent materials
Mixed digital-analog feedback systems
Temporal
modulation
frequency
Phase-locking of an LGN unit to a
drifting sinusoidal grating
Interval Histograms
PST Histograms
1000
40
4 Hz
50
2000
40
1000
8 Hz
16 Hz
200
10
32 Hz
50
20
64 Hz
0
0
1000
2000
3000
4000
5000
Peristimulus time (ms)
0
100
200
300
400
500
All-order interval (ms)
Raw spike train data courtesy of Andrzej Przybyszewski & Dan Pollen
Phase-locking in
visual thalamus
(LGN)
Stimulus:
Drifting
sinusoidal
gratings
Phase-locking in visual neurons
(Horseshoe crab ommatidium)
Miller, Ratliff, and Hartline (1961) How cells receive stimuli.
Scientific American 215(3):222-238.
Neural timing nets
FEED-FORWARD TIMING NETS
• Temporal sieves
• Extract (embedded) similarities
• Multiply autocorrelations
Si(t)
Sj(t)
two sets
of input
spike trains
Si(t) S j(t - )
individual
multiplicative
term
S (t ) S (t -t)
 i m j m
convolution
time-series
term m
Time
t
Relative delay
RECURRENT TIMING NETS
• Build up pattern invariances
• Detect periodic patterns
• Separate auditory objects
All time delays present


Time patterns reverberate
through delay loops

Recurrent,
indirect inputs

Coincidence
units
Direct inputs
Input time sequence
Build-up and separation of two auditory objects
Vowel [ae]
F0 = 100 Hz
Period = 10 ms
Vowel [er]
F0 = 125 Hz
Period = 8 ms
Characteristic delay channel (ms)
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.
Time (ms)
Sensing vs computing
Contingent vs. logically-necessary “truths”
Methodological issues:
What 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
Computers and brains
•Digital computers presently are capable of recombinationbased 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.
Limbic & paralimbic areas
Neural resonances
long-term
memory
Unimodal
and multimodal
association
areas
Frontal cortical areas
pragmatic
evaluations
memory deliberation
higher
semantic
resonances
planning
memory
global workspace
memory
motor
preparations
perceptual
resonances
memory
Motor systems
motor
outputs
sensory
inputs
controlled
variables
(consequences
of actions)
Primary sensory
pathways
uncontrolled
variables
(contingent events)
ENVIRONMENT
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.
Overview IV: Creativity, autonomy, and specification
• Creativity has two levels:
1) Recombination of existing primitives
2) De novo creation of new kinds of primitives
• Inherent tradeoffs:
Specifiability vs. autonomy
Predictability/reliability vs. creativity
Homeostat
Grey Walter's device
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)
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.
Recognizing determinate & contingent events
State-transitions
and
observer-operations
How do we
distinguish
measurements
and
computations
(such that we
can also
detect changes
in system
behavior)?
B
Measurement
observed
"pointer
reading"
Computation
Prediction
P
A =
A
B
two or more
possible
outcomes
R
1
Test
reference
state
C
D
PB
Epistemic cuts
(points of contingency)
R
2
Emergence relative to an observer:
What does the observer have to do to his/her own model to continue
successfully predicting the material system’s behavior?
Predictability
Deviation
Recovery of
predictability
MODEL
Observer
observables
sensors
Organism
or
Device
ENV
stabil ity
ne w sens or evol ve s in device
stabil ity
Evolution of observer's model
Add new states to V2
(no new state variables)
larger N
within
same D
Original state space
V2
semantic
emergenc e
V1
V2
V3
Addition of
new state
variableV3
syntactic
emergenc e
larger N
within
larger D
V1
V2
V1
same N
within
same D,
different
state-transitions
N = number of states
D = dimensionality of
state space
Opening up the sensory interface:
Break-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
Prosthesis:
augmentation of
functionalities
All technology is prosthesis.
feature
vector
structural
device
boundary
prosthesis
functional
device
boundary
²
existing
sensors
coordination
adaptive
construction ofa
sensory
prosthesis
test
performance
environment
action
vector
Operational states and procedures
in a scientific model
Explicate realm
of symbols
(well-defined)
Computation
sequence of states
following initial state A
Measurement
Test
Prediction
A
B
two or more
possible outcomes
("pointer readings")
P
P
A =
C
D
B
sequence of states after B
R
1
reference
state
Epistemic cuts
(points of contingency)
R
2
Implicate realm
of material process
(ill-defined)
Active measurement
Measurement
Computation
reference
state
A
sequence of computations
with initial state A
B
sequence of computations
with initial state B
R
R1
R
1
R2
R2
"preparing
the system"
R3
R3
two or more
possible outcomes
("pointer readings")
different prepared
reference states
result in different
measurements
physical actions taken to bring the
system into reference states
R1, R2 or R3 (motor actions)
(R is the passive reference state,
without any active preparation)
Action
actions based on the
outcome of the computations
(contingent upon A)
actions based on the
outcome of the computations
(contingent upon B)
intentions to make measurements
R, R1, R2, or R3 (motor commands)
Neural assemblies as internal sensors
discrete
features
action
vector
coordination
analog-digital
boundary internal
sensors
internal
iconic analog
representations
²
adaptive
constructionof
new sensors
test
structural boundary
of device
performance
environment