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• Whirlwind tour of nonlinear dynamics• Overview of higher form vision areas• Marroquin illusion• Detailed analysis of competitive
networks in rivalry
Nonlinear Dynamics: Equilibria & Linearization
• Coupled, first order equations• Solve for steady states or
equilibrium points• Compute Jacobian matrix• Evaluate at each steady state• Determine eigenvalues
Linearized Stability Analysis
€
dxdt
= F(x,y)
dydt
=G(x,y)
€
J =
∂F∂x
∂F∂y
∂G∂x
∂G∂y
x,y=Equilibrium
• All real(eig) < 0, asymptotic stability• Any real(eig) > 0, unstable• Pure imaginary eig: theorem does not
apply
Nonlinear Oscillations• Limit cycles: cannot exist in linear systems• Only one general theorem: Hopf Bifurcation
Theorem• Conservative oscillations (analogous to
linear systems) can exist, but not in neural systems
• Chaos can occur in > 2 dimensions• All neural oscillations are limit cycles!
Hopf Bifurcation Theorem
• Equilibrium is asymptotically stable for b < a
• Pair of pure imaginary eig for b = a• For all other eig, real(eig) < 0• Equilibrium point unstable for b > a• Asymptotically stable limit cycle for
b > a; or unstable for b < a
€
dr X
dt=
r F
r X ,β( )
Hopf Addendum
• Limit cycle emerges with infinitesimal amplitude
• Frequency = Im(eig)/2pi• Hodgkin Huxley Euations exhibit
Hopf bifurcation
€
dr X
dt=
r F
r X ,β( )
Nonlinear Oscillations Caveats• Not all limit cycles emerge via Hopf
bifurcations• Example: Mammalian cortical neurons• Conservative oscillations (analogous to
linear systems) can exist, but not in neural systems
• Chaos can occur in > 2 dimensions• All neural oscillations are limit cycles!
Conduction & Spiking Dynamics
• Two-compartment model (Rinzel et al)• Excitatory neurons: slow AHP currents• Simple but accurate cubic model (Wilson, 1999)
dVdt = – a V2 – b V V – ENa – R V – EK + Iinput
τ Rd Rdt = – R + cV2
(conductance)x(potential)
Phase Plane & Spike Generation
-0.2 0 0.2 0.4 0.6 0.8-0.2
0
0.2
0.4
0.6
0.8
1
V
R
-0.2 0 0.2 0.4 0.6 0.8-0.2
0
0.2
0.4
0.6
0.8
1
V
R
0 10 20 30 40 50 60 70 80 90 100-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
V (N
orm
alize
d Un
its)
Time (arbitrary units)
Fit to Human Action Potentials
-80
-60
-40
-20
0
20
40
0 1 2 3
HumanEqn (9.10)
Pote
ntia
l (m
V)
Time (msec)
A
-80
-60
-40
-20
0
20
40
0 1 2 3
HumanEqn (9.10)
Pote
ntia
l (m
V)
Time (msec)
A
Foehring et al, 1991
Spike Rate Adaptation• Human excitatory cortical neurons:
slow hyperpolarizing current• Causes spike rate adaptation
dVdt = – a V2 – b V V – E Na – R V – EK – H V – EK +I
τ Rd Rdt = – R + cV2
τ Hd Hdt = – H + gV2 Very slow K+ current
Spike Frequency Adaptation
0 100 200 300 4000
50
100
150
200
Time (ms)
B
Spik
e Ra
te (H
z)
0 20 40 60 80 100 120 140 160 180 200-100
-80
-60
-40
-20
0
20
40I = 0.85
Time (ms)
Pote
ntia
l (m
V)
AfterHyperpolarization
A
Controlled by neuromodulators (dopamine, serotonin)
Lyapunov Functions & Memory
• Positive definite function U(t) around an equilibrium
• dU/dt < 0 along trajectories in a region surrounding equilibrium
• Then equilibrium is asymptotically stable• Lyapunov fcns. always exist, but not unique
€
dUdt
=∂U∂xii
∑ dxidt
Lyapunov Functions & Memory• Apply where linearization fails• Permit estimate of domain of attraction
€
dxdt
= −y − x 3
dydt
= x − y 3
€
U(x,y) =x 2 + y 2
2dUdt
= −x 4 − y 4
Form Pathway Connections
Retina & LGN: Local contrast differencesV1: Contour & edge orientationsV2: Curvature, anglesV4: Elliptical object shapes, T & Y junctionsHigher Areas: Combines V4 info to represent faces & objects
Retina VisualCortex
FaceArea
V4
Form Pathway Connections
Area to area feedback connectionsSkipping connectionsFeedback local but patchy
V1 V4 TEO TEV2
V4 & FFA Activation
0
0.2
0.4
0.6
0.8
1
1.2
1.4
V1 V4 FFA
ConcentricRadialParallelFaces
Resp
onse
(% s
igna
l cha
nge)
Cortical Area
0
0.2
0.4
0.6
0.8
1
1.2
1.4
V1 V4 FFA
ConcentricRadialParallelFaces
Resp
onse
(% s
igna
l cha
nge)
Cortical Area
0
0.2
0.4
0.6
0.8
1
1.2
1.4
V1 V4 FFA
ConcentricRadialParallelFaces
Resp
onse
(% s
igna
l cha
nge)
Cortical Area
0
0.2
0.4
0.6
0.8
1
1.2
1.4
V1 V4 FFA
ConcentricRadialParallelFaces
Resp
onse
(% s
igna
l cha
nge)
Cortical Area
* *
**
Gallant et al, 2000: V4 damage
Normal V4Damaged V4
But: Orientation Discrimination normal (normal V1)
Receptive Field Size Increases
0.1°
1°
10°
100°Receptive Field Sizes
RF DiameterKobatake & TanakaOp de Beeck et al
RF D
iam
eter
Cortical Area
Diameter = 0.09°·Area3.04
V1 V2 V4 TEO TE
Construction of V4 Units
3x
3x
Σ
Rectification&
Gain Control
V1 V2 V4
3x
3x
Σ
Rectification&
Gain Control
fMRI & Distance from MeanMean Face
1
1.2
1.4
1.6
2 4 6 8 10 12 14 16Face geometry (%)
FFA neurons increase firing with distance from mean faceNature Neuroscience, October, 2005
Competitive Marroquin Model
τEdE n
d t= – E n +
100P +2
10+H n2+P +
2
where P = S Marroquin – 0.6 I k exp – R nk5 σ5– R nk5 σ5Σ
n ≠ k
τId I n
d t= – I n + E n
τHdH n
d t= – H n +gE n
Slow adapt (400-900 ms) Ca++ - K+ potential McCormick, Avoli
Spatial Competitive Inhibition
Describe neurons by spike rate equations
Sigmoid Nonlinearity
Competitive Model (demo)
• Spatially Regional Winner Take All• Winner slowly adapts, so new winners
emerge• Model generates gamma distribution