heteroclinic networks: stability, switching and...
TRANSCRIPT
HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Heteroclinic Networks: stability, switching and
memory
Claire Postlethwaite
with: Peter Ashwin, Jonathan Dawes, Vivien Kirk,Alastair Rucklidge, Mary Silber
Department of MathematicsUniversity of Auckland
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Outline
1 Introduction
2 Stability of Heteroclinic CyclesThe Guckenheimer–Holmes cycle
3 Heteroclinic NetworksThe Kirk–Silber networkDecision network
4 Summary
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Outline
1 Introduction
2 Stability of Heteroclinic CyclesThe Guckenheimer–Holmes cycle
3 Heteroclinic NetworksThe Kirk–Silber networkDecision network
4 Summary
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Side-blotched lizard
The side-blotched lizard (Uta stansburiana) lives in thePacific Coast Range of California.
There are three types of male lizard, each with a differentcoloured throat indicating three types of geneticallydetermined mating strategy.
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Side-blotched lizard
Strategy 2: Be sneaky.
Yellow-throated males aresneaky and can mimic thebehaviour of females.
Strategy 3: Guard your mate.
Blue-throated males defend smallterritories holding just a few fe-males. Because the territories areso small they can guard their matescarefully.
Strategy 1: Have a lot of territory.
Orange-throated males establish large territories,with several females. The more females the moreoften they can mate.
Blue-throated males can take over apopulation of mainly yellow-
throated males.
Yellow-throated
males
cansneakinto
the
territoriesof
orange-throatedmales.
Orange-
throatedmales
cantake
territoryfrom
blue-throatedmales.
[Cartoon taken from http://www.sciencenewsforkids.org]
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Heteroclinic cycles
Consider the ODE:
x = f (x), x ∈ Rn. (1)
An equilibrium ξ of (1) satisfies f (ξ) = 0.
A solution φj of (1) is a heteroclinic connection from ξj toξj+1, if it is backward asymptotic to ξj and forwardasymptotic to ξj+1.
A heteroclinic cycle is a set ofequilibria {ξ1, ..., ξm} and orbits{φ1, ..., φm}, where φj is aheteroclinic connection between ξjand ξj+1, and ξ1 ≡ ξm+1.
ξ1 ξ2
ξ3
ξ4
ξ0
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Heteroclinic cycles in systems with symmetry
Field 1980, Krupa and Melbourne 1995, and many others
In systems with invariant subspaces, heteroclinicconnections can exist robustly.Invariant subspaces arise naturally in systems withsymmetry.
ξ1
ξ2
x1
x2
x3
Z2 symmetry: κ3 : (x1, x2, x3) → (x1, x2,−x3)Fix(κ3) = {(x1, x2, 0)}
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Examples
Population models
Strategy 2: Be sneaky.
Yellow-throated males aresneaky and can mimic thebehaviour of females.
Strategy 3: Guard your mate.
Blue-throated males defend smallterritories holding just a few fe-males. Because the territories areso small they can guard their matescarefully.
Strategy 1: Have a lot of territory.
Orange-throated males establish large territories,with several females. The more females the moreoften they can mate.
Blue-throated males can take over apopulation of mainly yellow-
throated males.
Yellow-throated
males
cansneakinto
the
territoriesof
orange-throatedmales.
Orange-
throatedmales
cantake
territoryfrom
blue-throatedmales.
Fluid dynamics
N. Becker and G. Ahlers
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Classification of eigenvalues
Krupa and Melbourne, 1995
It can be useful to classify the eigenvalues near eachequilibrium.
Radial
Contracting
Expanding
Transverse
ξ1
ξ2
x1
x2
x3
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Analysis of flow near heteroclinic cycles
ξ1
ξ1
ξ2ξ2
ξ3H in
1 H in
2
Hout
1
Local flow: H in
1 → Hout
1
x2 = λux2,
x3 = −λsx3.
Global flow: Hout
1 → H in
2
Linearise flow aroundheteroclinic connections.
Combine local and global flow to get a Poincare map.
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
GH cycle
HeteroclinicNetworks
Summary
Outline
1 Introduction
2 Stability of Heteroclinic CyclesThe Guckenheimer–Holmes cycle
3 Heteroclinic NetworksThe Kirk–Silber networkDecision network
4 Summary
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
GH cycle
HeteroclinicNetworks
Summary
The Guckenheimer–Holmes cycle
Guckenheimer and Holmes, 1988
��
����
����
ξ1
ξ2
ξ3
The cycle exists in a system with symmetry Z3 ⋉ (Z2)3.
Contracting eigenvalue −λs , expanding eigenvalue λu.
Local map gives x2 → xδ3 , δ = λs/λu.
Global map x3 → Ax3.
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
GH cycle
HeteroclinicNetworks
Summary
The Guckenheimer–Holmes cycle
����
��������
����
ξ1
ξ2
ξ3
Poincare map: x → Axδ.
Fixed points exist at x = 0 and x = A1/(1−δ).
A resonance bifurcation at δ = 1 produces a long-periodperiodic orbit.
Period of orbit T ∼1
1− δ.
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
GH cycle
HeteroclinicNetworks
Summary
The Guckenheimer–Holmes cycle
ξ1
ξ2
ξ3
Poincare map: x → Axδ.
Fixed points exist at x = 0 and x = A1/(1−δ).
A resonance bifurcation at δ = 1 produces a long-periodperiodic orbit.
Period of orbit T ∼1
1− δ.
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
GH cycle
HeteroclinicNetworks
Summary
Noisy heteroclinic cycles
Stone and Holmes, 1990
Consider additive noise to a heteroclinic cycle.
Mean passage time past an equilibrium
T ∼log η
λu
where η is the noise amplitude
0 20 40 60 80 100 120 140 160 180 2000
0.2
0.4
0.6
0.8
1
0 20 40 60 80 100 120 140 160 180 2000
0.2
0.4
0.6
0.8
1
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Outline
1 Introduction
2 Stability of Heteroclinic CyclesThe Guckenheimer–Holmes cycle
3 Heteroclinic NetworksThe Kirk–Silber networkDecision network
4 Summary
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Heteroclinic networks
A heteroclinic network is a connected union of heterocliniccycles.
ξ1 ξ2
ξ3
ξ4
ξ0
Stability conditions of the network as a whole may bequite complicated.
Both resonance and noise in networks can givecomplicated behaviour.
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Kirk and Silber network
Kirk and Silber, 1994
A
Y
X
PB
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Kirk and Silber network
Subspace dynamics
A
Y
X
PB
A
B
x1
x2B
X
Y
P
x2
x3
y3
A
X
Y
P
x1
x3
y3
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Kirk and Silber network
Construction of maps
A
B
Y
X
Hin,AB
Hout,YB
Hout,XB
The position the trajectory hits H in,AB determines which
equilibrium is visited next.
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Kirk and Silber network
Construction of maps
Plot the Poincare section with polar coordinates.
0
log x2
2ππ2
πθ3
If the network is attracting trajectories can switch one waybut not the other.
If network is not attracting, there can be periodic orbitslying close to either or both of the sub cycles.
Kirk, Postlethwaite and Rucklidge, SIADS 201221 / 29
HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Noisy Kirk and Silber network
Armbruster, Stone and Kirk, 2003
Consider additive noise.
Depending on parameters, the ‘noise ellipse’ can becentered at the origin in H
in,AB .
Proportion of times each cycle visited proportional toshaded area.
−11 −10 −9 −8 −7 −6 −5−1.05
−1
−0.95
−0.9
−0.85
−0.8
−0.75
−0.7
−0.65
−0.6
−0.55
log (noise)
log(proportion)
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Noisy Kirk and Silber network
Armbruster, Stone and Kirk, 2003
For certain parameter sets, noise ellipse can move intobasin of attraction of one cycle or the other.
This is termed lift-off.
Lift-off can reduce switching.
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Decision network
Ashwin and Postlethwaite, 2013
replacements
v1
v2
v3 v4
v5 v6 v7 v8
Four sub-cycles, each with four equilibria.
Deterministic case is very complicated: numerics showswitching between sub cycles.
Addition of noise can allow memory.
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Decision network with memory
v1
v2
v3 v4
v5 v6 v7 v8
We can choose parameters to have lift-off occur in the x3direction as the trajectory passes ξ5.
−0.01 −0.005 0 0.005 0.010
200
400
600
800
1000
1200
1400
1600
x1(a) on H
in
5
−0.06 −0.04 −0.02 0 0.02 0.04 0.060
50
100
150
200
250
300
x3(b) on H
out
5
−4 −3 −2 −1 0 1 2 3 4
x 10−4
0
50
100
150
200
250
300
x3(c) on H
in
2
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Decision network with memory
Without memory: With memory:
−3 −2 −1 0 1 2 3
x 10−5
−1.5
−1
−0.5
0
0.5
1
1.5x 10
−5
x4
x3−2 −1 0 1 2 3
x 10−4
−1.5
−1
−0.5
0
0.5
1
1.5x 10
−5
x4
x3
Red: trajectories which visited ξ5 on the previous loop.
Black, blue, green: trajectories which visited ξ6, ξ7 and ξ8.
Can see lift-off in the x3 direction for red points.
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
KS network
Decision network
Summary
Transition matrices
Without memory: With memory:
0.46 0.26 0.18 0.100.47 0.23 0.20 0.100.49 0.23 0.18 0.100.50 0.22 0.18 0.10
0.63 0.33 0.03 0.020.49 0.27 0.16 0.080.54 0.24 0.13 0.090.52 0.27 0.14 0.07
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Outline
1 Introduction
2 Stability of Heteroclinic CyclesThe Guckenheimer–Holmes cycle
3 Heteroclinic NetworksThe Kirk–Silber networkDecision network
4 Summary
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HeteroclinicNetworks
ClairePostlethwaite
Outline
Introduction
HeteroclinicCycles
HeteroclinicNetworks
Summary
Summary
Heteroclinic cycles and networks can lose stability andproduce nearby long-period periodic orbits in resonancebifurcations.
Noisy heteroclinic cycles look like periodic orbits.
Noisy heteroclinic networks can have much morecomplicated behaviour, including switching and memory.
Acknowledgements
Jonathan Dawes, Alastair Rucklidge, Vivien Kirk, Mary Silber,Peter Ashwin.
London Mathematical Society.
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