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Introduction to CHAOSIntroduction to CHAOS
Larry Liebovitch, Ph.D.Larry Liebovitch, Ph.D.
Florida Atlantic UniversityFlorida Atlantic University
20042004
These two sets of data have the same
mean variance power spectrum
Data 1 RANDOMrandom
x(n) = RND
CHAOSDeterministic
x(n+1) = 3.95 x(n) [1-x(n)]
Data 2
etc.
Data 1 RANDOMrandom
x(n) = RND
Data 2 CHAOSdeterministic
x(n+1) = 3.95 x(n) [1-x(n)]
x(n+1)
x(n)
DefinitionCHAOS
Deterministicpredict that value
these values
CHAOS
Small Number of Variables
x(n+1) = f(x(n), x(n-1), x(n-2))
Definition
DefinitionCHAOS
Complex Output
PropertiesCHAOS
Phase Space is Low Dimensional
phase spaced , random d = 1, chaos
PropertiesCHAOS
Sensitivity to Initial Conditions
nearly identicalinitial values
very differentfinal values
PropertiesCHAOS
Bifurcationssmall change in a parameter
one pattern another pattern
Time Series
X(t)
Y(t)
Z(t)
embedding
Phase Space
X(t)
Z(t)
phase space set
Y(t)
Attractors in Phase SpaceLogistic Equation
X(n+1)
X(n)
X(n+1) = 3.95 X(n) [1-X(n)]
Attractors in Phase Space
Lorenz Equations
X(t)
Z(t)
Y(t)
X(n+1)
X(n)
Logistic Equationphase spacetime series d<1
The number of independent variables = smallest integer >
the fractal dimension of the attractor
d < 1, therefore, the equation of the time series that produced this attractor depends on 1 independent variable.
Lorenz Equationsphase spacetime series d =2.03
The number of independent variables = smallest integer >
the fractal dimension of the attractor
d = 2.03, therefore, the equation of the time series that produced this attractor depends on 3 independent variables.
X(t)
Z(t)
Y(t)
X(n+1)
n
Data 1 time series
phase spaced
Since ,the time series was producedby a randommechanism.
d
Data 2 time series
phase spaced = 1
Since d = 1,the time series
was produced by a deterministic
mechanism.
Constructed by direct measurement:Phase Space
Each point in the phase space set has coordinatesX(t), Y(t), Z(t)
Measure X(t), Y(t), Z(t) Z(t)
X(t) Y(t)
Constructed from one variablePhase Space
Takens’ TheoremTakens 1981 In Dynamical Systems and Turbulence Ed. Rand & Young, Springer-Verlag, pp. 366 - 381
X(t+ t)
X(t+2 t)
X(t)
Each point in thephase space sethas coordinatesX(t), X(t + t), X(t+2 t)
velo
city
(cm
/sec
)
Position and Velocity of the Surface of a Hair Cell in the Inner Ear
Teich et al. 1989 Acta Otolaryngol (Stockh), Suppl. 467 ;265 - 279
10-1
-10-1
-10-4 3 x 10-5displacement (cm)
stimulus = 171 Hz
velo
city
(cm
/sec
)
Position and Velocity of the Surface of a Hair Cell in the Inner Ear
Teich et al. 1989 Acta Otolaryngol (Stockh), Suppl. 467 ;265 - 279
5 x 10-6displacement (cm)
stimulus = 610 Hz
-3 x 10-2
3 x 10-2
-2 x 10-5
Data 1RANDOM
x(n) = RND
fractal demension of the phase space set
fra
cta
l dim
en
sio
n
of
ph
as
e s
pac
e s
etembedding dimension = number of values of the data taken at a time to
produce the phase space set
Data 2 CHAOSdeterministic
x(n+1) = 3.95 x(n) [1 - x(n)]
fra
cta
l dim
en
sio
n
of
ph
as
e s
pac
e s
et
fractal demension of the phase space set = 1
embedding dimension = number of values of the data taken at a time to
produce the phase space set
microelectrode
chick heart cell
current source
voltmeter
Chick Heart Cells
v
Glass, Guevara, Bélair & Shrier.1984 Phys. Rev. A29:1348 - 1357
Spontaneous Beating, No External Stlimulation
Chick Heart Cells
voltage
time
Periodically Stimulated2 stimulations - 1 beat
Chick Heart Cells
2:1
Chick Heart Cells
1:1
Periodically Stimulated1 stimulation - 1 beat
Chick Heart Cells
2:3
Periodically Stimulated2 stimulations - 3 beats
periodic stimulation - chaotic response
The Pattern of Beatingof Chick Heart Cells
Glass, Guevara, Bélair & Shrier.1984 Phys. Rev. A29:1348 - 1357
= phase of the beat with respect to the stimulus
The Pattern of Beating of Chick Heart Cells continued
phase vs. previous phase
0.5
0 0.5 1.0
1.0
0 0.5 1.0
i + 1
experiment
i
theory (circle map)
The Pattern of Beatingof Chick Heart Cells
Glass, Guevara, Belair & Shrier.1984 Phys. Rev. A29:1348 - 1357
Since the phase space set is 1-dimensional, the timing between the beats of thesecells can be described by a deterministic relationship.
ProcedureProcedure Time seriesTime series
e.g. voltage as a function of timee.g. voltage as a function of time
Turn the Time Series into a Turn the Time Series into a Geometric ObjectGeometric ObjectThis is called This is called embeddingembedding..
ProcedureProcedure Determine the Topological Determine the Topological
Properties of this ObjectProperties of this ObjectEspecially, the Especially, the fractal dimensionfractal dimension..
High Fractal DimensionHigh Fractal Dimension = Random = chance= Random = chance Low Fractal DimensionLow Fractal Dimension = Chaos = deterministic= Chaos = deterministic
The Fractal Dimension The Fractal Dimension
isis NOTNOT equal to equal to
The Fractal DimensionThe Fractal Dimension
Fractal Dimension:Fractal Dimension:How many new pieces of the How many new pieces of the Time Series are found when Time Series are found when viewed at finer time resolution.viewed at finer time resolution.
X
time
d
Fractal Dimension:Fractal Dimension:The Dimension of the Attractor in The Dimension of the Attractor in
Phase Space is related to thePhase Space is related to theNumber of Independent Number of Independent Variables. Variables.
X
time
d
x(t) x(t+ t)
x(t+2 t)
Mechanism that Generated the DataMechanism that Generated the DataChanced(phase space set)
Determinismd(phase space set) = low
Data
x(t)
t
?
C O L D
LorenzLorenz1963 J. Atmos. Sci. 20:13-1411963 J. Atmos. Sci. 20:13-141
Model
HOT
(Rayleigh, Saltzman)
LorenzLorenz1963 J. Atmos. Sci. 20:13-1411963 J. Atmos. Sci. 20:13-141
Equations
X = speed of the convective X = speed of the convective circulation circulation X > 0 clockwise, X > 0 clockwise, X < 0 counterclockwiseX < 0 counterclockwise
Y = temperature difference Y = temperature difference between rising and falling between rising and falling fluidfluid
Equations
LorenzLorenz1963 J. Atmos. Sci. 20:13-1411963 J. Atmos. Sci. 20:13-141
Z = bottom to top Z = bottom to top temperature minus the temperature minus the linear gradientlinear gradient
Equations
LorenzLorenz1963 J. Atmos. Sci. 20:13-1411963 J. Atmos. Sci. 20:13-141
Phase Space
LorenzLorenz1963 J. Atmos. Sci. 20:13-1411963 J. Atmos. Sci. 20:13-141
Z
X Y
Lorenz AttractorLorenz Attractor
X < 0 X > 0
cylinder of air rotating counter-clockwise
cylinder of air rotating clockwise
IXtop(t) - Xbottom(t)I e t = Liapunov Exponent
Sensitivity to Initial ConditionsSensitivity to Initial ConditionsLorenz EquationsLorenz Equations
X(t)
X= 1.00001
Initial Condition:
differentsame
X(t)
X= 1.
0
0
Deterministic, Non-ChaoticDeterministic, Non-Chaotic
X(n+1) = f {X(n)}
Accuracy of values computed for X(n):
1.736 2.345 3.2545.455 4.876 4.2343.212
Deterministic, ChaoticDeterministic, Chaotic
X(n+1) = f {X(n)}
Accuracy of values computed for X(n):
3.455 3.45? 3.4?? 3.??? ? ? ?
Initial ConditionsInitial Conditions X(t X(t00), Y(t), Y(t00), Z(t), Z(t00)...)...
Clockwork Universedetermimistic non-chaotic
Cancomputeall future
X(t), Y(t), Z(t)...Equations
Initial ConditionsInitial Conditions X(t X(t00), Y(t), Y(t00), Z(t), Z(t00)...)...
Chaotic Universedetermimistic chaotic
sensitivityto initial
conditionsCan notcomputeall future
X(t), Y(t), Z(t)...Equations
Lorenz Strange AttractorLorenz Strange Attractor
Trajectories from outside:
pulled TOWARDS it
why its called an attractor
starting away:
Lorenz Strange AttractorLorenz Strange Attractor
Trajectories on the attractor:
pushed APART from each othersensitivity to initial
conditions
starting on:
““Strange”Strange”attractor is fractalattractor is fractal
phase space set
not strange strange
““Chaotic”Chaotic”sensitivity to initial conditionssensitivity to initial conditions
time series
not chaotic chaotic
X(t)
t
X(t)
t
Shadowing TheoremShadowing Theorem
If the errors at each integration step are small, there is an EXACT trajectory which lies within a small distance of the errorfull trajectory that we calculated
There is an INFINITE number of trajectories on the attractor. When we go off the attractor, we are sucked back down exponentially fast. We’re on an exact trajectory, just not on the one we thought we were on.
Shadowing TheoremShadowing Theorem
4. We are on a “real”
trajectory.
3. Pulled backtowards the
attractor.
2. Error pushesus off
the attractor.
1. We start here.
Trajectorythat we actually
compute.
Trajectory that we
are trying to
compute.
Sensitivity to initial Sensitivity to initial conditions means that the conditions means that the
conditions of an experiment conditions of an experiment can be quite can be quite similarsimilar, but , but
that the results can be quite that the results can be quite differentdifferent..
TUESDAY
++
10 µlArT
10 µl
WEDNESDAY
ArT
++
A = 3.22
X(n)
n
X(n + 1) = A X(n) [1 -X (n)]
A = 3.42
X(n)
n
X(n + 1) = A X(n) [1 -X (n)]
A = 3.62
X(n)
n
Bifurcation
Start with one value of A. Start with x(1) = 0.5. Use the equation to compute x(2) from x(1). Use the equation to compute x(3) from x(2) and so on... up to x(300).
x(n + 1) = A x(n) [1 -x(n)]
Ignore x(1) to x(50), these are the transient values off of the attractor. Plot x(51) to x(300) on the Y-axis over the value of A on the X-axis. Change the value of A, and repeat the procedure again.
x(n + 1) = A x(n) [1 -x(n)]
Sudden changes of the pattern indicate bifurcations ( )
x(n)x(n)
The energy in glucose is transfered to ATP. ATP is used as an energy source
to drive biochemical reactions.
Glycolysis
+- -
periodic
TheoryMarkus and Hess 1985 Arch. Biol. Med. Exp. 18:261-271
Glycolysis
time
sugar input ATP output
chaotic
time
time time
ExperimentsHess and Markus 1987 Trends. Biomed. Sci. 12:45-48
cell-free extracts from baker’s yeast
Glycolysis
ATP measured by fluorescence glucose input time
ExperimentsHess and Markus 1987 Trends. Biomed. Sci. 12:45-48
Periodicfl
uo
resc
ence
Glycolysis
Vin
GlycolysisExperimentsHess and Markus 1987 Trends. Biomed. Sci. 12:45-48
Chaotic
20 min
GlycolysisMarkus et al. 1985. Biophys. Chem 22:95-105
Bifurcation Diagram
chaos
theory
experiment
GlycolysisMarkus et al. 1985. Biophys. Chem 22:95-105
ADP measured at the same phase each time of the input sugar flow cycle(ATP is related to ADP)
period of the input sugar flow cycle
# =
period of the ATP concentration
frequency of the input sugar flow cycle
Phase TransitionsHaken 1983 Synergetics: An Introduction Springer-Verlag
Kelso 1995 Dynamic Patterns MIT Press
Tap the left index fingerin-phase with the tickof the metronome.
Try to tap the right index
finger out-of-phase with the
tick of the metronome.
Phase TransitionsHaken 1983 Synergetics: An Introduction Springer-Verlag
Kelso 1995 Dynamic Patterns MIT Press
As the frequency of the metronome increases, the right finger shifts from out-of-phase to in-phase motion.
Position of Right Index FingerPosition of Left Index Finger
A. TIME SERIES
Phase TransitionsHaken 1983 Synergetics: An Introduction Springer-Verlag
Kelso 1995 Dynamic Patterns MIT Press
ADD
ABD
Position of Right Index Finger
360o
0o
B. POINT ESTIMATE OF RELATIVE PHASE
180o
Self-Organized Phase TransitionsHaken 1983 Synergetics: An Introduction Springer-Verlag
Kelso 1995 Dynamic Patterns MIT Press
2 sec
This bifurcation can be explained as a change in a potential energy
function similar to the change which
occurs in a physical phase
transition.
syst
em p
ote
nti
al
scal
ing
par
amet
er
Phase TransitionHaken 1983 Synergetics: An Introduction
Springer-Verlag Kelso 1995 Dynamic Patterns MIT Press
Small changes in parameters can produce large changes in behavior.
+
10cc ArT
++
9cc ArT
Bifurcations can be used to test if a system is deterministic.
Deterministic Mathematical Model Experiment
observed bifurcationspredicted bifurcations
Match ?
The fractal dimension of the phase space set tells us if the data was
generated by a random or deterministic mechanism.
ExperimentalDatax(t)
t
X(t+ t)Phase Space
Set
X(t)
The fractal dimension of the phase space set tells us if the data was
generated by a random or a deterministic mechanism.
Mechanism that generated the experimental data.
Deterministic Random
d = low d
The fractal dimension of the phase space set tells us if the data was
generated by a random or a deterministic mechanism.
EpidemicsSchaffer and Kot 1986 Chaos ed. Holden,
Princeton Univ. Press
400015000
0 0
measlesNew York
time series:
phase space:
chickenpox
EpidemicsOlsen and Schaffer 1990 Science 249:499-504
dimension of attractor in phase space
measles chickenpox
Kobenhavn 3.1 3.4 Milwaukee 2.6 3.2St. Louis 2.2 2.7New York 2.7 3.3
EpidemicsOlsen and Schaffer 1990 Science 249:499-504
SEIR models - 4 independent variables
S susceptible E exposed, but not yet infectious I infectious R recovered
EpidemicsOlsen and Schaffer 1990 Science 249:499-504
Conclusion: measles: chaotic chickenpox: noisy yearly cycle
time series: voltageKaplan and Cohen 1990 Circ. Res. 67:886-892
normal fibrillation death
D = 1chaos
D = random
Phase spaceV(t), V(t+ t)
ElectrocardiogramECG: Electrical recording of the
muscle activity of the heart.
8
time series: voltageBabloyantz and Destexhe 1988 Biol. Cybern. 58:203-211
normal
D = 6chaos
ElectrocardiogramECG: Electrical recording of the
muscle activity of the heart.
ElectrocardiogramECG: Electrical recording of the
muscle activity of the heart.
time series: time between heartbeatsBabloyantz and Destexhe 1988 Biol. Cybern. 58:203-211
normal
D = 6chaos
fibrillation deathD = 4chaos
induced arrhythmiasD = 3chaos
Evans, Khan, Garfinkel, Kass, Albano, and Diamond 1989 Circ. Suppl. 80:II-134
Zbilut, Mayer-Kress, Sobotka, O’Toole and Thomas 1989 Biol. Cybern, 61:371-381
ElectroencephalogramEEG: Electrical recording of the nerve
activity of the brain.Mayer-Kress and Layne 1987 Ann. N.Y. Acad. Sci. 504:62-78
time series: V(t) phase space:
D=8 chaos
V(t)
V(t+ t)
Rapp, Bashore, Martinerie, Albano, Zimmerman, and Mees 1989 Brain Topography 2:99-118
Babloyantz and Destexhe 1988 In: From Chemical to Biological Organization ed. Markus, Muller, and Nicolis, Springer-Verlag
Xu and Xu 1988 Bull. Math. Biol. 5:559-565
ElectroencephalogramEEG: Electrical recording of the nerve
activity of the brain.
Different groups find different dimensions
under the same experimental conditions.
ElectroencephalogramEEG: Electrical recording of the nerve
activity of the brain.
mental task
quiet awake, eyes closed
quiet sleep
brain virus: Creutzfeld- Jakob
Epilepsy: petit mal
meditation: Qi-kong
ElectroencephalogramEEG: Electrical recording of the nerve
activity of the brain.perhaps:High Dimension
Low Dimension
Random Markov
How to compute the next x(n):Each t pick a random number 0 < R < 1
If open, and R < pc, then close.
If closed, and R < po, then open.
Random Markov
t
closed
If closed:probability to open in thenext t=po
If open:probability to close in the next t = pcopen
Deterministic Iterated MapLiebovitch & Tóth 1991 J. Theor. Biol. 148:243-267
x(n) = the current at time nx(n+1) = f (x(n))
open
closed
x(n+1)
x(n)
0 x(1) 0 x(2)0
x(3)
0
x(2)
Deterministic Iterated MapLiebovitch & Tóth 1991 J. Theor. Biol. 148:243-267
How to compute the next x(n):
Tacoma Narrows Bridge
Thursday November 7, 1940Good modern review (explaining why the explanation given in physics textbooks is wrong): Billah and Scanlan 1991 Am. J. Phys. 59:118-124
Tacoma Narrows Bridge
Equation of simple, forced resonance:x + Ax + Bx = f ( t )
Equation of flutter that destroyed the Tacoma Narrows Bridge:
x + Ax + Bx = f ( x, x )
Tacoma Narrows Bridge
Scanlan and Vellozzi 1980 in Long Span Bridges ed. Cohen and Birdsall pp. 247-263 NYAS
Wind Tunnel Tests
AIRFOILORIGIONAL
TACOMA NARROWS(
Tacoma Narrows Bridge
The drag on an airplane wing (A) increases with wind speed.
Wind Tunnel Tests
0.3
0.2
0.1
0
0.1
0.2
A2OTN
U NB
A
*The drag on the OTN (original Tacoma Narrows) bridge changes sign as the wind speed increases, it enters into positive feedback.
Like a small molecule, relentlessly kicked by the surrounding heat fromone state to another.
The change of states is driven bychance kT thermal fluctuations.
CLOSEDrandom
OPEN
ener
gy
Random
DeterministicLike a lilttle mechanical machine with sticks and springs.The change of states is driven by coherent motions that result from the structure and the atomic, electrostatic, and hydrophobic forces in the channel protein.
CLOSED OPEN
ener
gy
deterministic
Analyzing Experimental Data
In principle, you can tell if thedata was generated by a random or a deterministic mechanism.
The Good News:
Analyzing Experimental Data
In practice, it isn’t easy.
The Bad News:
Need Lots of Data
• Very large data sets: 10d?• Sampling rate must cover the attractor evenly.
Sample too often: only see 1-d trajectories.
Sample too rarely; don’t see the attractor at all.
Why it’s Hard to Tell Random from Deterministic Mechanisms
Why it’s Hard to Tell Random from Deterministic Mechanisms
Analyzing the Data is Tricky• Choice of lag time t for the
embedding.– lag too small: the variable
doesn’t change enough, derivatives not accurate.
– lag too long: the variable changes too much,
derivatives not accurate.• Method of evaluating the dimension.
Mathematics is Not Known• Embedding theorems are only proved
for smooth time series.
Why it’s Hard to Tell Random from Deterministic Mechanisms
How Many Times Series Values?
N = Number of valuesin the time series
needed to correctlyevaluate the dimension
of an attractorof dimension D
NwhenD = 6
How Many Times Series Values?
Smith 1988Phys. Lett. A133:283 42D 5,000,000,000
Wolff et al. 1985Physica D16:285 30D 700,000,000
Wolf et al. 1985Physica D16:285 10D 1,000,000
How Many Times Series Values?
Nerenberg & Essex 1990Phys. Rev. A42:7065
D+22
_______1________
kd1/2[A In (k)](D+2)/2
D/22(k-1) ((D+4)/2) (1/2) ((D+3)/2)
x[ ]
200,000
How Many Times Series Values?
Ding et al. 1993Phys. Rev. Lett. 70:3872 10D/2
(D/2)! D/2
10
1,000
Gershenfeld 1990 preprint 2D
Lorenz
t 0X(t+ t)
X(t)
Lorenz
X(t+ t)
X(t)
t Just Rightt correlation time
Lorenz
X(t+ t)
X(t)
8t
Takens’ TheoremIf
X(t+ t)
X(t)
Then,the lag plot constructed from the data
dX(t)dt
X(t)
Is a linear transformation of the real phase space
dX(t)dt
because
X(t+ t) - X(t) t
Since the fractal dimension is invariant under a linear
transformation, the fractal dimension of the lag plot is
equal to the fractal dimension of the real phase space set.
Takens’ Theorem
If the data does not satisfy these assumptions then we are not guaranteed that the fractal dimension of the lag plot is equal to the fracfal
dimension of the real phase space set.
The ion channel current is not smooth, it is fractal (bursts within bursts) and therefore
not differentiable.
Thus the assumptions of the theroem are not met and we are not guaranteed that the fractal dimension of the lag plot is equal to the fractal
dimension of the real phase space set.
For example:
Osborne & Provenzale 1989 Physica D35:381
They used a Fourier series to generate a fractal time series whose
power spectra was 1/f . They randomized the phases of the terms
in the Fourier series so that the fractal dimension of the real phase space set was infinite. But, they found that the
fractal dimension of the lag plots was as low as 1.
For example:
RANDOMLY pick numbers
Pathological example where an infinite dimensional random process
has a LOW dimension attractor
6 6 6 6 6 6 6
6 6 6 6 6 6 6
6 6 6 6 6 6 6
6 6 6 6 6 6 6
Time Series: 6, 6, 6, 6, 6, 6, 6, 6 ... Phase Space:
Pathological example where an infinite dimensional random process
has a LOW dimension attractor
D = 0
6
6 6
Organization of the Vectors in the Phase Space Set
Kaplan and Glass 1992 Phys. Rev. Lett. 68:427-430
small
average direction
Random no uniform flow
Organization of the Vectors in the Phase Space Set
Kaplan and Glass 1992 Phys. Rev. Lett. 68:427-430
large
Deterministic
average direction
uniform flow
Surrogate Data SetTheiler et al. 1992 Physica D58:77-94
original phase space set
surrogate phase space set
same
original time series
surrogate time series
RANDOM
same first ordercorrelations
higher orders scrambled
Surrogate Data SetTheiler et al. 1992 Physica D58:77-94
surrogate phase space set
different
surrogate time series
same first ordercorrelations
higher orders scrambled
DETERMINISTIC
original phase space set
original time series
Time Series phase space
Experiments
Dimension
Low = deterministicHigh = random
examples: ECG, EEG
WEAK
vary a parameter
Experiments
predicted by a nonlinear
model
STRONGsee behavior
electrical stimulation of cells, biochemical reactions
examples:
Control
system outputNon-Chaotic System
control parameter
Control
system outputChaotic System
control parameter
Control of Chaoslight intensity of a laser
Roy et al. 1992 Phys. Rev. Lett. 68:1259-1262
NO CONTROL
0 0.5 msec
Intensity
Control of Chaoslight intensity of a laser
Roy et al. 1992 Phys. Rev. Lett. 68:1259-1262
CONTROL
0 0.2 msec
Intensity
Control
Control of Chaoslight intensity of a laser
Roy et al. 1992 Phys. Rev. Lett. 68:1259-1262
CONTROL
0 0.2 msec
Intensity
Control
Control of Chaosmotion of a magnetoelastic ribbon
Ditto, Rauseo, and Spano 1990 Phys. Rev. Lett. 65:3211-3214
electromagnets
magnetoelasticribbon
B = 0
B > B1
Control of Chaosmotion of a magnetoelastic ribbon
Ditto, Rauseo, and Spano 1990 Phys. Rev. Lett. 65:3211-3214
Control of Chaosmotion of a magnetoelastic ribbon
Ditto, Rauseo, and Spano 1990 Phys. Rev. Lett. 65:3211-3214
sensor
X
Xn = X (t = nT)
2 TB = Bo sin ( t)
Control of Chaosmotion of a magnetoelastic ribbon
Ditto, Rauseo, and Spano 1990 Phys. Rev. Lett. 65:3211-3214
iterationnumber
0 - 2359
2360 - 4799
4800 - 7099
7100 - 10000
none
period 1
period 2
period 1
control
Control of Chaosmotion of a magnetoelastic ribbon
Ditto, Rauseo, and Spano 1990 Phys. Rev. Lett. 65:3211-3214
4.5
4.0
3.5
3.0
2.50 2000 4000 6000 8000 10000
Iteration Number
Xn
Control of Biological Systems
The Old WayBrute Force Control.
BIG machine
BIG powerHeart
Amps
Control of Biological Systems
The New WayCleverly timed, delicate pulses.
little machine
little power
mA
Heart
The Old WayForces drive the system between stable states.
How do we think of biological systems?
How do we think of biological systems?
Force D Force E
Stable State B
Stable State A Stable State C
How do we think of biological systems?
The New Way
Hanging around for a
while in one condition
forces the system into
another condition.
Dynamics of A
Dynamics of B
How do we think of biological systems?
Unstable State B
Unstable State A Unstable State C
Summary of Chaos
FEW INDEPENDENT VARIABLES
Behavior is so complex that it mimics random behavior.
Summary of Chaos
The value of the variables at the next instant in time can be calculated from their values at
the previous instant in time.
xi (t+ t) = f (xi (t))
DYNAMICAL SYSTEMDETERMINISTIC
Summary of Chaos
x1(t+ t) - x2(t+ t) = Ae t
SENSITIVITY TO INITIAL CONDITIONS
NOT PREDICTABLE IN THE LONG RUN
Summary of Chaos
STRANGE ATTRACTOR
Phase space is low dimensional (often fractal).
Books About Chaos
J. GleickChaos: Making a New Science 1987 Viking
introductory
Books About Chaos
F. C. MoonChaotic and Fractal Dynamics 1992 John Wiley & Sons
intermediate mathematics
Books About Chaos
J. Guckenheimer & P. Holmes Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields 1983 Springer-VerlagE. Ott Chaos in Dynamical Systems 1993 Cambridge Univ. Press
advanced mathematics
A. V. Holden Chaos 1986 Princeton Univ. Press
E. & L. Moskilde Complexity, Chaos and Biological Evolution 1991 Plenum
reviews of chaos in biologyBooks About Chaos
Books About Chaos
J. Bassingthwaighte, L. Liebovitch, & B. West Fractal Physiology 1994 Oxford Univ. Press
reviews of chaos in biology