universal laws and architectures

John Doyle 道陽Jean-Lou Chameau Professor

Control and Dynamical Systems, EE, & BioE

tech1#Ca

Universal laws and architectures:Theory and lessons from

brains, hearts, cells, grids, nets, bugs, fluids, bodies, planes, docs, fire, fashion,

earthquakes, music, buildings, cities, art, running,cycling, throwing, Synesthesia, spacecraft, statistical mechanics

https://www.cds.caltech.edu/~doyle

The following preview is approved for all audiences.

https://rigorandrelevance.wordpress.com/author/doyleatcaltech/

https://www.cds.caltech.edu/~doyle

Universal laws and architectures:

The videos

• SDN, IOT, IOE, CPS, etc..• Compute/control technology• Industrialization• Money/finance/lobbyists/etc• Society/agriculture/states • Weapons• Bipedalism• Maternal care• Warm blood• Flight• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis

Major transitions

• SDN, IOT, IOE, CPS, etc..• Compute/control technology• Industrialization• Money/finance/lobbyists/etc• Society/agriculture/states • Weapons• Bipedalism• Maternal care• Warm blood• Flight• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis

Major transitions

Theory of• Evolution• Architecture• Complexity• Networks

Our heroes

Evolution ComplexityArchitecture

{case study}UTheorems+

• Lots of aerospace• Wildfire ecology• Earthquakes• Physics:

– turbulence, – stat mech (QM?)

• “Toy”: – Lego– clothing, fashion

• Buildings, cities• Synesthesia

• Brains• Bugs (microbes, ants)• Nets/Grids (cyberphys)• Medical physiology

• SDN, IOT, IOE, CPS, etc.• Compute/control technology• Industrialization• Money/finance/lobbyists/etc• Society/agriculture/states • Weapons• Bipedalism• Maternal care• Warm blood• Flight• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis






Precursorsin 1940s

U{case study}Theorems+

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&docid=Kp2_EoFYinqfrM&tbnid=Y-xY6OI36YAxsM:&ved=0CAUQjRw&url=http://withfriendship.com/user/sathvi/space-shuttle-orbiter.php&ei=4o6MU9K6Oo78oASJ04HAAQ&bvm=bv.68191837,d.cGU&psig=AFQjCNF5hn5FZk8B02sPqiHJP8dFHEMT2A&ust=1401806919354620







2012

Precursorsin 1940s

U{case study}Theorems+

1980s



http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&docid=0z604pyP4NMagM&tbnid=3OtcaBnK9Kls4M:&ved=0CAUQjRw&url=http://www.fas.org/programs/ssp/man/uswpns/air/xplanes/x29.html&ei=j46MU8XqDY2IogTim4GgCg&bvm=bv.68191837,d.cGU&psig=AFQjCNEJsdgBtFV5Fmx0VTCbOyCXgnKskw&ust=1401806847246080

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&docid=0z604pyP4NMagM&tbnid=3OtcaBnK9Kls4M:&ved=0CAUQjRw&url=http://www.fas.org/programs/ssp/man/uswpns/air/xplanes/x29.html&ei=j46MU8XqDY2IogTim4GgCg&bvm=bv.68191837,d.cGU&psig=AFQjCNEJsdgBtFV5Fmx0VTCbOyCXgnKskw&ust=1401806847246080

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&docid=jeuS-HD-V8Q5KM&tbnid=ulkpm9jKi3bBfM:&ved=0CAUQjRw&url=http://pics-on-weapons.blogspot.com/2012_01_01_archive.html&ei=F4-MU-08g_ygBImwgaAG&bvm=bv.68191837,d.cGU&psig=AFQjCNGeEkgx3UaG3-py5H7d4cC-4s205g&ust=1401806985105421

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&docid=jeuS-HD-V8Q5KM&tbnid=ulkpm9jKi3bBfM:&ved=0CAUQjRw&url=http://pics-on-weapons.blogspot.com/2012_01_01_archive.html&ei=F4-MU-08g_ygBImwgaAG&bvm=bv.68191837,d.cGU&psig=AFQjCNGeEkgx3UaG3-py5H7d4cC-4s205g&ust=1401806985105421

2006 - 2014: Department Head Aerospace Engineering and Mechanics2001 - 2014: Professor AEM and Control Science & Dynamical Systems Center1996 - 2001: Associate Professor AEM and CSDSC1995 - 2014: Co-Director Control Science and Dynamical Systems Program1992 - 2004: Director of Grad Studies CSDS Department1990 - 1996: Assistant Professor AEM1989 : Ph.D. Aeronautics, California Institute of Technology

Gary Balas@UMN

1990-2014

Theorems+

• Compute/control technology• Industrialization• Money/finance/lobbyists/etc• Society/agriculture/states • Weapons• Bipedalism/balance

{case study}

U

• Brains• Familiar, accessible• Live demos!• Cheap, reproducible• Open• Unavoidable

Caveats and Issues• Bad scholarship, cinematography, diplomacy, organization• Badly organized (Videos, slides, papers in Dropbox)• More questions than answers• Trailer for videosBut• Applicable• Accessible (even latest theory research is relatively…)• Almost undergrad for almost everything• “Obvious” (if in retrospect)• Eager for feedback (and also new material)

Laws as Tradeoffs

costly

fragile

efficient

robust

Function=Locomotion

Ideal

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&docid=JUnT7VhkheAqIM&tbnid=sBTJOAYzo_vd5M:&ved=0CAUQjRw&url=http://www.ignitumtoday.com/2012/12/19/science-vs-religion-part-i-of-ii/darwin-fish/&ei=G6GMU4CZHtjaoASP-oDADg&bvm=bv.68191837,d.cGU&psig=AFQjCNE3vJOwQvCsfW6ddz0QUCFdhJOg9Q&ust=1401811591086383

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&docid=JUnT7VhkheAqIM&tbnid=sBTJOAYzo_vd5M:&ved=0CAUQjRw&url=http://www.ignitumtoday.com/2012/12/19/science-vs-religion-part-i-of-ii/darwin-fish/&ei=G6GMU4CZHtjaoASP-oDADg&bvm=bv.68191837,d.cGU&psig=AFQjCNE3vJOwQvCsfW6ddz0QUCFdhJOg9Q&ust=1401811591086383

costly

fragile

efficient

robust

4x

>2x

Tradeoffs

Ideal

fragile

robust

“costly”“efficient”

Similar• Tradeoffs• Laws• MechanismsBut simpler• Models?• Experiments?

fragile

robust

crash

short long

hard

harder


Similar• Tradeoffs• Laws• MechanismsBut simpler• Models• Experiments

Simplified inverted pendulum

Act

l

l

l length (to COM)g gravityv control (acceleration)

y

x

θ

v


Act

l

l


y

x

θ

v


Actsin cos

sinO

xl g vy x l

vθ θ θ

θ+ = −= +

=

l

l


y

x

θ

v


Act

gpl

=

Instabilitysin cos

sinO

xl g vy x l

vθ θ θ

θ+ = −= +

=

l

eye vision

Act

delayl

N noiseE error

( ) ET jN

ω =

Simplified sensorimotor control

gpl

=

Instability

Control?

eye vision

slow

Act

delayl

N noiseE error

( ) ET jN

ω =

Simplified sensorimotor control

gpl

=.3sτ ≈

InstabilityControl

brain

Amplification (noise to error)?

eye vision

Act

delay

Control

l

NE

( ) ET jN

ω =

gpl

= .3sτ ≈

Instability

Entropy rate

Energy (L2)

eye vision

Act

delay

Control

l

NE

( ) ET jN

ω =

gpl

= .3sτ ≈

( ) ( )exp ln

expT

pT

τ∞

≥

∫Amplification (noise to error) theorem:

Necessary!

Intuition

( )exp pt

delay τ

Before you can

react

time

state 1pl

∝

.3sτ ≈

Entropy rate

Energy (L2)

( ) ( )exp ln

expT

pT

τ∞

≥

∫

P

+

noise

error

C

( ) ET jN

ω =

( ) ( ){ }sup sup Re( ) 0|T T j T s sωω

= = ≥∞Max modulus

Proof?

( ) ( ) ( )( )

( )

1

exp

( ) ( ) exp

exp

Ms P s s

T M M p p p

T p

P τ

τ

τ

−≥ ≥ ≥

≥

∞ ∞

∞

= − ⇒

= Θ

⇒

( ) ( )1

1

( ) ( )

P p T p

M p p −

= ∞⇒ =

⇒ = Θ

( ) ( )( ) ( ) ( ) ( ) 1

exp

T s M s s j

s s

ω

τ

= Θ Θ =

Θ = −

Undergrad math

0.2 0.4 0.6 0.8 1

2

4

6

8

10

0

Length l, m

.3sτ = Shorter?

( )expT pτ≥

fragility

gpl

=

sin cossinO

x vl g vy x lθ θ θ

θ

=

+ = −= +

delay

noise

error

Length l

.3sτ =

0.2 0.4 0.6 0.8 1

2

4

6

8

10

0

Length l, m

.3sτ = Shorter

fragility

gpl

=

( )exp pτsin cos

sinO

x vl g vy x lθ θ θ

θ

=

+ = −= +

delay

noise

error

Length l

.3sτ =

( )expT pτ≥

0.2 0.4 0.6 0.8 1

2

4

6

8

10( ) ( )exp ln

expT

pT

τ∞

≥

∫

0

Theory

Length l, mdelay

noise

error

Length l

0.2 0.4 0.6 0.8 1

2

4

6

8

10( ) ( )exp ln

expT

pT

τ∞

≥

∫

0

HardHarderTheory

& Data

Length l, mdelay

noise

error

Length l

eye vision

Act

delay

Control

l

N

E

( ) ET jN

ω =

gpl

= .3sτ ≈

Necessary!

Entropy rate

Energy (L2)

( ) ( )exp ln

expT

pT

τ∞

≥

∫Robust to “noise” model

fragile

robust

crash

short long

hard

harder

Law #1 : MechanicsLaw #2 : GravityLaw #3 : Light/visionLaw #4 : ( )expT pτ≥

Yoke Peng Leong

eye vision

Act

delay

Control

l

NE

( ) ET jN

ω =gpl

= .3sτ ≈

Necessary!

Universal lawsMechanics+

Gravity +Light +

( ) ( )exp ln

expT

pT

τ∞

≥

∫fragile

robust

crash

short long

hard

harder

hard harder ???

l

Ol

harder ???

Ol l≤

Ol l≥

Ol

( ) ( )exp ln

expT z pp

z pTτ

∞

+ ≥−

∫

Unstable zeros

( )21 11 1

1 1

O

O

O

O

g gz pl l l

l l lz pz p l l l

l z pl z p

ααααα

= =−

+ −+=

− − −

+ −+ + −= → = =

− − −

Simple analytic formulas

P

+

noise

error

C

( ) ( ){ }sup sup Re( ) 0|T T j T s sωω

= = ≥∞Proof?

( ) ( )

( ) ( ) ( ) ( ) 1

exp

T s M s s js zs ss z

ω

τ

= Θ Θ =

−Θ = −

+

( ) ( ) ( )

( )

( )

1

exp

( ) ( ) exp

exp

Ms zs P s ss z

z pT M M p p pz p

z pT pz p

P τ

τ

τ

−≥ ≥ ≥

≥

∞ ∞

∞

− = − ⇒ + +

= Θ−

+⇒

−

Undergrad math

( )exp z ppz p

τ +−

10

100

Length, m.1 1.5.2

2

.3sτ =

hardest!

( ) ( ) ( )expexp

exl

pn z pp

z pT

pT

τ τ∞

≥ ≥+

−

∫

1Ol l≤ =

Theory

( )exp pτ

( )exp z ppz p

τ +−

10

100

Length, m.1 1.5.2

2

1pl

∝

.3sτ =

hardest!hard

( ) ( ) ( )expexp

exl

pn z pp

z pT

pT

τ τ∞

≥ ≥+

−

∫

hard harder hardest!

What is sensed matters.

Unstable pole Unstable zero

0l l≤0l l≈

robust

short

fragile

mass.1 1

1g 10kg

Fragile to• Up• Length l• Length lo (sense)• Noise• Medial direction• Close one eye• Stand one leg• Alcohol• Age• …

fragile

robust

short long

Robust to• mass• down

delay

Instability

Sensors/actuators

Delay

Entropy rate

Energy (L2)

eye vision

Act

delay

Control

l

E

gpl

= .3sτ ≈

( )( )

exp ln

expQuantized

T

T pτ∞

∝

∫

Necessary!

Robust to “noise” model

Spiking neurons?Discrete axons?Distributed control?

Entropy rate

Energy (L2)

eye vision

Act

delay

Control

l

E

gpl

= .3sτ ≈

( )( )

exp ln

expQuantized

T

T pτ∞

∝

∫

Necessary!

Quantized



fragile

robust

crash

short long

hard

harder

hardest!

crash

Theory & data

fragile

robust

short long

Gratuitously fragile

Example of sparse/bad sensing

fragile

robust

short long

Gratuitously fragile

Look up & shorten

costly

fragile

efficientrobust

Just add a bike?

costly

fragile

efficientrobust

Learn

costly

fragile

efficientrobust

Learn

Learn

fragile

robust



Major transitions

unstable

fragile

robust

crash

short long

hard

harder



Major transitions

fragile

robust

crash

short long

hard

harder


Similar• Tradeoffs• Laws• Mechanisms

But simpler• Models• Experiments

• Compute/control technology• Industrialization• Money/finance/lobbyists/etc• Society/agriculture/states • Weapons• Balance (biped)• Maternal care• Warm blood• Flight (streamlining)• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis

Efficiency/instability/layers/feedback• Efficient but

unstable


Efficiency/instability/layers/feedback• Efficient but

unstable• Needs control

‒ Complex‒ Distributed‒ Layered‒ Active‒ Laws/tradeoffs

How universal?

1. Chandra, Buzi, Doyle (2011) Glycolytic oscillations and limits on robust efficiency. Science

2. Gayme, McKeon, Bamieh, Papachristodoulou, Doyle (2011) Amplification and Nonlinear Mechanisms in Plane Couette Flow, Physics of Fluids

3. Li, Cruz, Chien, Sojoudi, Recht, Stone, Csete, Bahmiller, Doyle (2014) Robust efficiency and actuator saturation explain healthy heart rate control and variability, PNAS

Biology

Physics

Medicine

• Robust efficiency• Undergrad (mostly)• “Obvious” (in retrospect)• Extreme bimodal reviews

+ tutorial videosPapers

Veryuniversal

Chandra, Buzi, and Doyle UG biochem, math, control theory

InsightAccessibleVerifiable

Veryuniversal

too fragile

complex

robustshort

cheaprobust

Balancing

Glycolysis

Law #1Law #2Law #3

Law #1Law #2Law #3

• Efficient but unstable• Needs complex/active control• With associated laws? Specific

Specific

too fragile

complex

robustshort

cheaprobust

Balancing

Glycolysis

Law #1Law #2Law #3

Law #1Law #2Law #3


Specific

Law #4z pTz p+

≥−

Universal

wasteful

fragile

efficient

robust

too fragile

complex

robustshort

cheaprobust

Balancing

Glycolysis

Law #4z pTz p+

≥−

Universal

wasteful

fragile

efficient

robust

• Money/finance/lobbyists• Industrialization• Society/agriculture• Weapons• Balancing• Maternal care• Warm blood• Flight• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis

too fragile

complex

robustshort

cheaprobust

Balancing

Glycolysis

Law #1Law #2Law #3

Law #1Law #2Law #3


Specific

Law #4z pTz p+

≥−

Universal

• Money/finance/lobbyists• Industrialization• Society/agriculture• Weapons• Balancing• Maternal care• Warm blood• Flight • Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis

Flight

unstable

Universal?Specific?

1pt R

¶ + ×Ñ + Ñ = D¶u u u u

Flight and swimming

(streamlining)

wasteful

fragile

efficient

robust

Universal

1pt R

¶ + ×Ñ + Ñ = D¶u u u u

Turbulent

z x

y

uFlow

Blunted turbulent velocity profile

Turbulent

Laminar

wU

wU

wasteful

fragileLaminar

Turbulent

efficient

robust

1pt R

¶ + ×Ñ + Ñ = D¶u u u u

UniversalTurbulent

Physics of Fluids (2011)

z x

y

uFlow

Blunted turbulent velocity profileLaminar

Turbulent wU

wU

wasteful

fragileLaminar

Turbulent

efficient

robust

1pt R

¶ + ×Ñ + Ñ = D¶u u u u

wasteful

fragileLaminar

Turbulent

efficient

robustSharks

Dolphins

• Money/finance/lobbyists• Industrialization• Society/agriculture• Weapons• Balancing• Maternal care• Warm blood• Flight (streamlining)?• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis

too fragile

complex

No tradeoff

robustshort

cheaprobust

Balancing

Glycolysis

• Efficient but unstable• Needs complex/active control• With associated laws?

Specific

Turbulent

efficient

robustSharks

Dolphinsu

Flow

Specific

Specific

• Money/finance/lobbyists• Industrialization• Society/agriculture• Weapons• Balancing• Maternal care• Warm blood• Flight (streamlining)?• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis

too fragile

complex

No tradeoff

robustshort

cheaprobust

Balancing

Glycolysis


Universal

Turbulent

efficient

robustSharks

Dolphinsu

FlowRobust efficiency

• Money/finance/lobbyists• Industrialization• Society/agriculture• Weapons• Balancing (running)• Maternal care• Warm blood• Flight (streamlining)?• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis (metabolism)

too fragile

complex

No tradeoff

cheaprobust

Running

Metabolism


Specific

Universal

Robust efficiency

Specific

• Money/finance/lobbyists• Industrialization• Society/agriculture• Weapons• Balancing (running)• Maternal care• Warm blood• Flight (streamlining)?• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis (metabolism)

too fragile

complex

No tradeoff

cheaprobust

Running

Metabolism


Specific

Universal

Cardiovascular physiology

Robust efficiency

Li, Cruz, Chien, Sojoudi, Recht, Stone, Csete, Bahmiller, Doyle

Robust efficiency and actuator saturation explain healthy heart rate control and variabilityProc. Nat. Acad. Sci. PLUS 2014

efficientrobust

controls

external disturbances

heart rateventilationvasodilationcoagulationinflammationdigestionstorage…

errorsO2BPpHGlucoseEnergy storeBlood volume…internal nois

High Low

High

Cardio-physiology

k10-1 100 101100

101

too fragile

complex

No tradeoff

expensive

Biology

robustTurbulent

efficient

robust uFlow

Physics

controls




High Low

High

10

100

.1 1.22

Medicine

efficientrobust

• Robust efficiency• Tradeoffs, mechanisms

Neuro

k10-1 100 101100

101

too fragile

complex

No tradeoff

expensive

Biology

robustTurbulent

efficient

robust uFlow

Physics

controls




High Low

High

10

100

.1 1.22

Medicine

efficientrobust

• Foundational• Huge literatures• Data, models, simulation• Yet persistent mysteries

• Robust efficiency• Tradeoffs, mechanisms

Neuro





So far

• Compute/control technology• Industrialization• Money/finance/lobbyists/etc• Society/agriculture/states • Weapons• Bipedalism• Maternal care• Warm blood• Flight• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis

Major transitions

efficient

robust

Glycolysis

Turbulence

Cardio-physiology

Balance

Needs complex control

Efficient but unstableSimilar• Tradeoffs• Laws• MechanismsBut simpler• Models• Experiments

Crash


Warfare (“up”)

Prey (“back”)

Crash

Bad

Weapons

Mass extinction

Crash

• Compute/control technology• Industrialization• Money/finance/lobbyists/etc• Society/agriculture/states• Weapons• Balance (biped)• Maternal care• Warm blood• Flight (streamlining)• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis

Slavery

Famine

Bad

• Compute/control technology• Industrialization• Money/finance/lobbyists/etc• Society/agriculture/states• Weapons• Balance (biped)• Maternal care• Warm blood• Flight (streamlining)• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis

Slavery

Famine

• Crashes “back” can hurt everyone• Crashes “forward” benefit those (few)

that cause and maintain them• Systematic study is missing

forward

back

• Compute/control technology• Industrialization• Money/finance/lobbyists/etc• Society/agriculture/states • Weapons• Bipedalism• Maternal care• Warm blood• Flight• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis

Major transitions

efficient

robust

FastFlexible

Apps

OSHW

Gene

Trans*Protein

CerebellumCortex

Reflex

Needs complex control

Efficient but unstable

Next

10

100

.1 1.5.22

( )exp pτ

Speed ∝ 1/τ

Length lo

Length l

Speed ∝ 1/τ

delay?

noise

error

( ) z pT pz p

τ +≥

−exp

Theory

robust + efficient

Fast

Flexible

accessibleaccountableaccurateadaptableadministrableaffordableauditableautonomyavailablecredibleprocess

capablecompatiblecomposableconfigurablecorrectnesscustomizabledebugabledegradabledeterminabledemonstrable

dependabledeployablediscoverable distributabledurableeffective

evolvableextensiblefail transparentfastfault-tolerantfidelityflexibleinspectableinstallableIntegrityinterchangeableinteroperable learnablemaintainable

manageablemobilemodifiablemodularnomadicoperableorthogonalityportableprecisionpredictableproducibleprovablerecoverablerelevantreliablerepeatablereproducibleresilientresponsivereusable

safety scalableseamlessself-sustainableserviceablesupportablesecurablesimplestablestandardssurvivable

tailorabletestabletimelytraceableubiquitousunderstandableupgradableusable

efficient

robust

sustainablerobust + efficient

efficient

Fast

Flexible

IEEE Conference on Decision and ControlDecember 2015 Osaka, Japan

Hard Limits on Robust ControlOver Delayed and Quantized Communication Channels

With Applications to Sensorimotor Control


Robust efficiency and actuator saturation explain healthy heart rate control and variability

PNAS PLUS 2014

Old story

0 100 200 300 4000 100 200 300 400406080100120140160180

sec

0 100 200 300 4000 100 200 300 400406080100120140160180

sec

( )[ ] [ ]( )2, 2 2 2[ ]

vp vp total as as vs vs a

T O

p ap

av vs

c P V c P c P c P

v O M F O O

= −

− + ⋅= −

+ +

as as s

vs vs s r

ap ap

l

r p

c P Fc P F Qc P

Q

Q F

== −=

−

−

( ) ( ) ( )2

2 2 22 * 2 * 2 *2 2min P as as o Hq P P q O O q H H dt − + ∆ − ∆ + −

∫



PNAS PLUS 2014

0 100 200 3000 100 200 300406080100120140160180

sec



PNAS PLUS 2014

Data

Fast

Slow

How? Why?• Mechanism• Tradeoff

• Architecture• Speed• Flexibility• Robustness• Virtualization• DiversityFast

Slow

Cortex

eye vision

Actdelay

Object motion

Error+

slow

Slow

FlexibleHigh Res

vision

Fast

Inflexible

Cortex

eye vision

Act

slow

delay

VORfast

Object motion

Head motion

Error+

Slow

Flexible

vision

Fast

InflexibleLow Res

VOR Vestibular Ocular Reflex (VOR)

Does not use “vision”?

Slow

FlexibleHigh Res

vision

Fast

InflexibleLow Res

VOR

Tradeoff

Mechanism

Minimal cartoon

Cortex

eye vision

Act

slow

delay

VORfast

Object motion

Head motion

Error+

Cortex

eye vision

Act

slow

delay

VOR

fast

Object motion

Head motion

Error+

These signals must “match”

Next important piece?

Cortex

eye vision

Actslow

delay

VOR

fast

Object motion

Head motion

Error+

Tune gain?

gain

VOR and visual gain must match

Cortex

eye vision

Actslow

delay

VOR

Object motion

Head motion

Cerebellum

Error+

ErrorTune gain

gain AOS

AOS = Accessory Optical system

fast

Cortex

eye vision

Actslow

delay

VOR

This fast “forward”

path is necessary

Object motion

Head motion

Cerebellum

Error+

ErrorTune gain

gain AOS


fastdirect,fast

feedback

slow

Cortex

eye vision

Actslow

delay

VOR

Object motion

Head motion

Cerebellum

Error+

ErrorTune gain

gain AOS


fastdirect,fast

feedback

slow

This fast “forward”

pathNeeds

“feedback” tuning

slowest

Cortex

vision

delayVOR

Cerebellum

gainAOS

vision

delay

Cerebellum

gainAOS

Cortex

Cerebellum

gainAOS

distributed compute

(& control)

“Grey” boxes

Cortex

vision

Actdelay

VOR

Cerebellum

gainAOS

vision

delay

Cerebellum

gainAOS

Cortex

vision

delay

Cerebellum

gainAOS

“White”cables vision

Actslower

delay

fast

Cerebellum

gainAOS

slowest

slowerfast

slowest

vision

Actdelay

gain

sparse communication, heterogeneous

delays

Slow

FlexibleHigh Res

vision

Fast

InflexibleLow Res

VOR

Tradeoff

Mechanism

Minimal cartoon

Cortex

eye vision

Act

slow

delay

VORfast

Object motion

Head motion

Error+

Vision

VOR

Tune

extreme heterogeneity

Slow

FlexibleHigh Res

Fast

InflexibleLow Res

secs

msecs

hours

Vision

VOR

Tune

delaylog10

low reshigh res(flexible)

extreme heterogeneity

why?

Ideal

secs

msecs

hours

Vision

VOR

Tune

delaylog10

low reshigh res(flexible)

why?

Ideal

• These dimensions• Tradeoff• Extreme heterogeneity

High resolution vision robust w/motion• Object motion• Self motion

Vision

Motion

High resolution vision robust w/motion• Object motion• Self motion

heterogeneous delays

secs

msecs

hours

errorlow high

delaylog10

Plannavigate

Reflex

Tune

secs

msecs

hoursPlan

navigate

Reflex

Tune

errorlow high

810> ×delaylog10

Plannavigate

Reflex

Tune


Plannavigate

Reflex

Tune

Robust

RobustFragile

Plannavigate

Reflex

Tune

Robust

RobustFragile

secs

msecs

hoursPlan

navigate

Reflex

Tune

errorlow high

810> ×delaylog10

Plannavigate

Reflex

Tune

heterogeneous delays and errors 1110> ×

A KU PK

F B

M P

LZ YMQ

RH

O

V JW

TB

NC

A

G

X YQ

A

D T

A KU PK

F B

M P

LZ YMQ

RH

O

V JW

TB

NC

A

G

X YQ

A

D T

HIGH RES

FAST

secs

msecs

hours

Vision

VOR

delaylog10

low res?high res(flexible)

A KU PK

F B

M PLZ YMQ

RH

O

V JW

TB

N C A

G

X YQ

A

D T

HIGH RES

FAST

secs

msecs

hours

Vision

VOR

delaylog10

low res?high res(flexible)

A KX Y

FAST

low resolution ≠ noisy

secs

msecs

hoursPlan

navigate

Reflex

Tune

error?low high

810> ×delaylog10

Ideal

heterogeneous delays and errors 1210> ×

( )( )

1

exp ln

exp

G

G pg

τ∞

∝

∫

Tune

error?

( )( )

1

exp ln

exp

G

G pg

τ∞

∝

∫

2x

x ∞

Cortexeye vision

Actslow

delayVOR

Object

Head

Cerebellum

Error+

Tunegain

fast

Extreme system level

heterogeneity

secs

msecs

hoursdelaylog10

error

Plannavigate

Reflex

Tune

Why?

log10 axon diameter

log10axons

per nerve

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

Sciatic

Extreme component

level heterogeneity

axons per

nerve

axons per

nerve

10-1 100 101100

102

104

106 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

mean axon diameter (microns)

Sciatic

610> ×mean axon diameter (microns)

axons per

nerve

10-2 100 102100

102

104

106 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

Sciatic

610> ×mean axon area

log10 axon diameter

log10axons

per nerve

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

Sciatic

Extreme component

level heterogeneity

Why?

log10 axon diameter

log10axons

per nerve

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

Sciatic

secs

msecs

hoursdelaylog10

error

Plannavigate

Reflex

Tune

Cortexeye vision

Actslow

delayVOR

Object

Head

Cerebellum

Error+

Tunegain

fast

• Extreme heterogeneity• Connect scales

log10 axon diameter

log10axons

per nerve

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

Sciatic

secs

msecs

hoursdelaylog10

error

Plannavigate

Reflex

Tune

Cortexeye vision

Actslow

delayVOR

Object

Head

Cerebellum

Error+

Tunegain

fast

• Extreme heterogeneity• Connect scales• Mechanistic physiology• Integrated• Quantitative• Tradeoffs as “laws”

log10 axon diameter

log10axons

per nerve

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

Sciatic

secs

msecs

hoursdelaylog10

error

Plannavigate

Reflex

Tune

Cortexeye vision

Actslow

delayVOR

Object

Head

Cerebellum

Error+

Tunegain

fast

Assume:resource (cost)(to build and maintain nerve)∝ area α

α

Assume:

resource (cost)(to build and maintain nerve)

∝ area α

α

Assume:

resource (cost)(to build and maintain nerve)

∝ area α

αAssume

lengths and areas are

given

log axon diam µm

logaxons

per nerve

7 bits

1 bit

3 bits

resource (cost) ∝ area αarea α

axon diameter

axonsper

nerve

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

Sciatic

too big

resource (cost) ∝ area α

axon radius (µm)

N=axons

per nerve

0 10

2

4

6

ρ =

2nerve area = Nα πρ=

resource (cost) ∝ area α

7 bits

1 bit

3 bits

Why not make axon radius small?(Maximize mutual information.)

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

SciaticIdeal?

axon radius

axonsper

nerve

Why not make axon radius small?(Maximize mutual information.)

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

SciaticIdeal?

axon radius

axonsper

nerve

Tradeoffs

spike rate ρ∝ spike speed ρ∝

(propagation)

Tradeoffsin

spikingneurons

axon radius (µm)

N=axons

per nerve

ρ =0 1

0

2

4

6


Ideal?

axon radius (µm)

N=axons

per nerve

ρ =0 1

0

2

4

6

2N αρ

∝

2R α αρρ ρ

∝ ∝

spike rate (bits/time) R N

ρρ

∝∝


spike speed 1delay sT

ρ

ρ

∝

∴ ∝

R Tαλ∴ =

spike rate (bits/time) R N

ρρ

∝∝

axon radius (µm)

N=axons

per nerve

ρ =0 1

0

2

4

6

2R

T

α αρρ ρ

α

∝ ∝

∝2nerve area = Nα πρ=

1delay sTρ

∝ feasible

infeasible

R Tαλ=R Tαλ≤

R

fast

slow

low res

high res

R Tαλ∴ =

1delay sTρ

∝

R Tαλ=

R

fast

slow

1/R

delay

fast

slow

R Tαλ=

low res

high res

2

resource =

nerve area = Nα πρ=

low res

high resR

2

resource =

nerve area = Nα πρ=

1/R

delay

fast

slow

low res

high res

( )?R Tαλ=

1/R

delay

fast

slow

R Tαλ=

1/R

delayfeasible

infeasiblefast

slow

low res

high res

R Tαλ=

1 bit

3 bits

Assuming fixed lengthand area

1/R

delay

feasible

infeasiblefast

slow

low res

high res

R Tαλ=

delay

fast

slow

Plannavigate

Reflex

Tune

errorlow high

Ideal

Ideal

• Extreme heterogeneity• Connect scales• Mechanistic physiology• Integrated• Quantitative• Tradeoffs as “laws”

P

x

vu

T Delay of T

(head motion)

LK

LQ

LQ Channel(neural signaling)

Q Quantizer

Reflex controller(VOR: Motion compensation)LK

reflex (delayed)

state

Presenter

Presentation Notes

Fig 6.

P

x

vu

T Delay of T

(head motion)

LK

LQ

LQ Channel(neural signaling)

Q Quantizer


reflex (delayed)

R Tαλ=

state

Presenter

Presentation Notes

Fig 6.

P

x

v

T Delay of T

(head motion)

LK

LQ Q Quantizer


reflex (delayed)

state[ ]( 1) ( ) ( ) ( )Lx t ax t Q u t T v t+ = − − +

u

Presenter

Presentation Notes

Fig 6.

P

x

vu

r

(head motion)

(target motion)

LK HK

LQ HQ

remote sensingadvanced warninghigh level planning

delay

reflex (delayed)

Presenter

Presentation Notes

Fig 6.

P

x

r (target motion)LK

HK

HQ

remote sensingadvanced warninghigh level planning

delay

reflex (delayed)

[ ]( 1) ( ) ( ) ( )H rx t ax t Q u t T r t T+ = − − + −

|| || 1r ∞ ≤

||| || ?x ∞ =

[ ]( )HQ u t T−

Presenter

Presentation Notes

Fig 6.

P

x

v

ur

L Hwarnedreflex

(delayed)

|| || || || 1v rδ∞ ∞≤ ≤

( 1) ( )x t ax t+ = +

||| || ?x ∞ =

Presenter

Presentation Notes

Fig 6.

( ) ( )1

|| || ,|| || 1 1

1 1 min sup || || = 2 2

0,

LL

Ti

v r i

H r

T R Rx a a a a

a T Tδ

δ δ∞ ∞

−∞

≤ ≤ =

− −+ − + −

≠ ≤

∑warned

quant costdelayed

delay costdelayed

quant cost

Theorem

P

x

v

ur

L Hwarnedreflex

(delayed)

|| || || || 1v rδ∞ ∞≤ ≤

( 1) ( )x t ax t+ = +

Presenter

Presentation Notes

Fig 6.

.1

1

10cost

Delayed (Tc)24

Warned (Tw)

value

-4-2

delay

quant

delay Ts

quant

ctrltotal state

total delay

.1

1

10

0

warned, plan, tune

delayed, fast, reflex

delay >>1bits >>1

small, constant bits & delay

few uniformly large axonslarge errors

diverse, small axonssmall errors

Extremely different

Presenter

Presentation Notes

clear all;close all;clc; LW = 2; T = linspace(0,5,100);P = T; Xmin = P;Ind = P;Xd = P;E = P;Delay = P;Bits = P;Cost = P;U = P; p = 0.5; for ii = 1:length(T); d = linspace(0.1,5,1000); xd = d;e = d;x = d;u = d; for k = 1:length(d) xd(k) = max([0,d(k)-T(ii)+1]); e(k) = 1/(2^(p*d(k))-1); x(k) = xd(k)+e(k); u(k) = 1 + inv((2^(p*d(k)))-1); end [Xmin(ii),Ind(ii)] = min(x); Xd(ii) = xd(Ind(ii)); E(ii) = e(Ind(ii)); Delay(ii) = d(Ind(ii)); Bits(ii) = p*d(Ind(ii)); Cost(ii) = x(Ind(ii)); U(ii) = u(Ind(ii)); end figure;semilogy(T,Xd,T,E,T,Xd+E,T,U,'LineWidth',2); set(gca,'Xdir','reverse');ylim([0.1,10]); xlabel('Advanced warning (Tw)');ylabel('Cost'); figure;semilogy(T,Delay-T,T,Delay,T,Bits,'LineWidth',2); set(gca,'Xdir','reverse');ylim([0.1,10]); xlabel('Advanced warning (Tw)');ylabel('Optimal value'); %%% T = linspace(-5,0,100);P = T; Xmin = P;Ind = P;Xd = P;E = P;Delay = P;Bits = P;Cost = P;U = P; p = 0.5; for ii = 1:length(T); d = linspace(0.1,5,1000); xd = d;e = d;x = d;u = d; for k = 1:length(d) xd(k) = max([0,d(k)-T(ii)+1]); e(k) = 1/(2^(p*d(k))-1); x(k) = xd(k)+e(k); u(k) = 1 + inv((2^(p*d(k)))-1); end [Xmin(ii),Ind(ii)] = min(x); Xd(ii) = xd(Ind(ii)); E(ii) = e(Ind(ii)); Delay(ii) = d(Ind(ii)); Bits(ii) = p*d(Ind(ii)); Cost(ii) = x(Ind(ii)); U(ii) = u(Ind(ii)); end figure;semilogy(T,Xd,T,E,T,Xd+E,T,U,'LineWidth',2); set(gca,'Xdir','reverse');ylim([0.1,10]); xlabel('Computational delay (Tc)');ylabel('Cost'); figure;semilogy(T,Delay-T,T,Delay,T,Bits,'LineWidth',2); set(gca,'Xdir','reverse');ylim([0.1,10]); xlabel('Computational delay (Tc)');ylabel('Optimal value');

.1

1

10

Cost(system)

Delayed (Tc)0246

Warned (Tw)

Optimal value(parts)

-6-4-20

delay

quant

delay Ts

quant(bits)

ctrltotal state

total delay

.1

1

10

warned, plan, tune


Presenter

Presentation Notes


.1

1

10Cost

0246Warned (Tw)

delay → 0

ctrltotal state

ctrl ≈ constant

Cost = size of signal with worst-case disturbance

total state≈ quant << 1

Presenter

Presentation Notes


.1

1

10

Cost

Delayed (Tc)0246

Warned (Tw)-6-4-20

delay

quant

ctrltotal state

ctrl ≈ constant


total state≈ delay >> 1

Extremely different

Cost = size of signal with worst-case disturbance

Presenter

Presentation Notes


0246Warned (Tw)

Optimalvalue

delay Ts

quant(bits)

.1

1

10

.1

1

10Cost

delay

ctrltotal state

delay >>1bits >>1

diverse small axonssmall errors

warned, planning,tuning, precision

Presenter

Presentation Notes


Delayed (Tc)0246

Warned (Tw)

Optimal value

-6-4-20

delay Ts

quant(bits)

total delay

.1

1

10

Extremely different

Presenter

Presentation Notes


.1

1

10

Cost(system)

Delayed (Tc)0246

Warned (Tw)

Optimal value(parts)

-6-4-20

delay

quant

delay Ts

quant(bits)

ctrltotal state

total delay

.1

1

10

warned, plan, tune


Presenter

Presentation Notes


delay

fast

slowPlan

navigate

Reflex

Tune

errorlow high

Presenter

Presentation Notes


.1

1

10cost(error)

delayquant

ctrldelay

fast

slowPlan

navigate

Reflex

Tune

errorlow high



Presenter

Presentation Notes


1/R

delay

fast

slow

low res

high res

R Tαλ=

Presenter

Presentation Notes


24Warned (Tw)

valuedelay Ts

quanttotal delay.1

1

10

0

1/R

delay

fast

slow

low res

high res

R Tαλ=


Presenter

Presentation Notes


.1

1

10cost

24Warned (Tw)

value

delayquant

delay Ts

quant

ctrltotal state

total delay.1

1

10

0

error

delay

fast

slow plannav

Reflex

Tune

low high

1/R

delay

fast

slow

low res

high res

R Tαλ=

Presenter

Presentation Notes


.1

1

10cost

24Warned (Tw)

value

delayquant

delay Ts

quant

ctrltotal state

total delay.1

1

10

0

delay

fast

slow plannav

Reflex

Tune

low high

1/R

delay

fast

slow

low res

high res

R Tαλ=

error

Presenter

Presentation Notes


P

x

vu

rL H

delay

fast

slow plannav

Reflex

Tune

low high

1/R

delay

fast

slow

low res

high res

R Tαλ=

error

Presenter

Presentation Notes


1/R

delay

fast

slow

low res

high res

R Tαλ=

delay

fast

slow plannav

Tune

low high

error

reflex

reflex

Presenter

Presentation Notes


Delayed (Tc)

value

-4-2

delay Ts

quant

total delay

.1

1

10

0

.1

1

10cost delay

quant

total state

1/R

delay

fast

slow

low res

high res

R Tαλ=

delay

fast

slow plannav

Tune

low high

error

reflex

reflex

Presenter

Presentation Notes


delay

fast

slow plannav

Tune

low higherror

reflex

Presenter

Presentation Notes


axon diameter

axonsper

nerve

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

Sciatic

Delayed (Tc)

value

-4-2

delay Ts

quant

total delay

.1

1

10

0

Presenter

Presentation Notes


.1

1

10cost

Delayed (Tc)

value

-4-2

quant

delay Ts

quant

ctrl

total state

total delay

.1

1

10

0axon diameter

axonsper

nerve

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

Sciatic


Presenter

Presentation Notes


24Warned (Tw)

valuedelay Ts

quanttotal delay.1

1

10

0axon diameter

axonsper

nerve

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

Sciatic

Presenter

Presentation Notes


.1

1

10cost

24Warned (Tw)

value

delayquant

delay Ts

quant

ctrltotal state

total delay.1

1

10

0

warned, plan,tune, precision

axon diameter

axonsper

nerve

-1 0 10

2

4

6 Optic

Vestibular

Olfactory

Auditory

Aα

cranial

spinal

Sciatic

Presenter

Presentation Notes


secs

msecs

hoursPlan

navigate

Reflex

Tune


errorlow high

810> ×delaylog10

Plannavigate

Reflex

Tune

delay

fast

slow plannav

Reflex

Tune

low higherror

Presenter

Presentation Notes


P

x

vu

rL H

delay

fast

slow plannav

Reflex

Tune

low high

1/R

delay

fast

slow

low res

high res

R Tαλ=

error

Presenter

Presentation Notes


From: “Understanding Vision: theory, models, and data”, by Li Zhaoping, Oxford University Press, 2014

Eye muscle

From: “Understanding Vision: theory, models, and data”, by Li Zhaoping, Oxford University Press, 2014

Layered feedback

R R R R R……

CC

“reflex”

“cortex”brains

• Localized control (LPs)‒layered‒scalable‒local model, synthesis, implement

Distributed/local control

“grey” boxes and “white” cables

• Communications‒sparse‒delayed, quantized

……

w

P

R

w

P

R

w

P

R

w

P

R

w

P

R ……

CC“reflex”

“cortex”

physical plant

disturbance

act/sense

sense act/sense

body + tools+ environment

sensors + muscles

brains


……

w

P

w

P

w

P

w

P

w

P

• Physical plant‒unstable dynamics‒distributed‒sparse

• Disturbance (w)‒worst case‒advanced warning T

physical plant

disturbance


act/sense

sense act/sense

body + tools+ environment

……

w

P

R

w

P

w

P

R

w

P

w

P

R ……


• Actuation and sensing‒saturates‒sparse ‒delayed, quantized



physical plant

disturbance


act/sense

sense act/sense

sensors + muscles

……

w

P

R

w

P

R

w

P

R

w

P

R

w

P

R ……





CC“reflex”

“cortex”


physical plant

disturbance


act/sense

sense act/sense

…

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

…

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

P

C

P

C

P

C

P

C

P

C …

…

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

…

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C ………

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C …

……

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C ……

……

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C ……

……

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C ……

• Communications‒ sparse‒delayed, quantized

• Actuation and sensing‒ saturates‒ sparse ‒delayed, quantized

• Physical plant‒unstable dynamics‒distributed‒ sparse


Scalability?





• Localized control ‒modeling, synthesis‒ implementation

…

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

…

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

P

C

P

C

P

C

P

C

P

C …

…

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

…

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C

w

P

C ………

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……

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Scalable





• Localized control‒model, synthesis, implement‒ layered

…

…

……

w

P

R

w

P

R

w

P

R

w

P

R

w

P

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CC

“reflex”

“cortex”

Scalable

……

w

P

R

w

P

R

w

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w

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CC“reflex”

“cortex”


physical plant

disturbance


act/sense

sense act/sense

……

w

P

R

w

P

R

w

P

R

w

P

R

w

P

R ……

CC“reflex”

“cortex”

physical plant

disturbance


act/sense

sense act/sense

Fast

Flexible

RobustRobust

……

w

P

R

w

P

R

w

P

R

w

P

R

w

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CC“reflex”

“cortex”

physical plant

disturbance


act/sense

sense act/sense

Fast

Flexible



R R R R R ……

CC“reflex”

“cortex”


FastFlexible

Delay vs resolution tradeoff.


A KU PK

F B

M P

LZ YMQ

RH

O

V JW

TB

NC

A

G

X YQ

A

D T

Cortexeye vision

Actslow

delay

fast

Object

Head

Cerebellum

+

ErrorTune gain

gainAOS

Slow

Flexible

Fast

Inflexible

Reflex

vision

VOR

Color?

Motion

Motion

Stare at the intersection

Marge Livingstone

colorvision

Stare at the intersection.

Stare at the intersection

Stare at the intersection.

Cortexeye vision

Actslow

delay

fast

Object

Head

Cerebellum

+

ErrorTune gain

gainAOS

Slow

Flexible

Fast

Inflexible

Reflexvision

VOR

Color?

Motion

Motion

Slow

Flexible

Fast

Inflexible

vision

VOR

Color

Motion

Cortex

eye vision

Actslow

delay

Object +

colorvision

Slow

colorvision

Motion

OK, color decisions are rarely urgent.

Slow

Flexible

Fast

Inflexible

vision

VOR

Motion

colorvision

Slow

Cortexeye vision

Actslow

delay

fast

Object motion

Head motion

Cerebellum

+

ErrorTune gain

gain AOSReflex

RNAP

DnaK

σ

RNAP

σDnaK

rpoH

FtsH Lon

Heat

σ

σ mRNA

Other operons

σ

DnaK

DnaK

ftsH

Lon

Sensorimotor

Vastly more different

than similar

Linux OS

Bacterial cell

Cortexeye vision

Actslow

delay

fast

Object motion

Head motion

Cerebellum

+

ErrorTune gain

gain AOSReflex

RNAP

DnaK

σ

RNAP

σDnaK

rpoH

FtsH Lon

Heat

σ

σ mRNA

Other operons

σ

DnaK

DnaK

ftsH

Lon

MechanismsLinux OS

Bacterial cell

Universals are profound

Fast

Slow

Flexible Inflexible

Apps

OS

HW

Gene

Trans*

ProteinCerebellum

Cortex

Reflex

Fast

Slow

Flexible Inflexible

Apps

OS

HW

Gene

Trans*

ProteinCerebellum

Cortex

Reflex


• Tradeoffs• Evolvability

Fast

Slow

Flexible Inflexible

Apps

OS

HW

Gene

Trans*

ProteinCerebellum

Cortex

Reflex

Tradeoffs

DiverseNot

Diverse Universals are profound

• Hourglass diversity

• Tradeoffs• Evolvability

“Constraints that

deconstrain”

Fast

Slow

Flexible Inflexible

Apps

OS

HW

Gene

Trans*

ProteinCerebellum

Cortex

Reflex

Horizontal Transfer

Horizontal Transfer

Tradeoffs

Illusion

DiverseNot



• Tradeoffs

• Horizontal transfer

• Evolvability

• Tunable (illusion)

Fast

Slow

Flexible Inflexible

Apps

OS

HW

Gene

Trans*

ProteinCerebellum

Cortex

Reflex

DiverseNot

Horizontal Transfer

Virtual internalhidden

Tradeoffs

Illusion



• Tradeoffs


• Virtual/Internal• Hijacking

• Evolvability


Horizontal Transfer

Fast

Slow

Flexible Inflexible

Apps

OS

HW

Gene

Trans*

ProteinCerebellum

Cortex

Reflex

DiverseNot

Horizontal Transfer

Virtual internalhidden

Tradeoffs

Illusion

Diverse/Digital Universals are profound


• Tradeoffs



• Robustness

• Evolvability


Horizontal Transfer

• Digital

• SDN, NFV, IOT, IOE, CPS• Compute/control technology• Industrialization• Money/finance/lobbyists/etc• Society/agriculture/states • Weapons• Bipedalism (balance,

cardiovascular physiology)• Maternal care• Warm blood• Flight (turbulence)• Mitochondria• Oxygen• Translation (ribosomes)• Glycolysis• Cells


• Tradeoffs



• Robustness

• Evolvability


• Digital


universal laws and architectures

Documents