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![Page 1: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/1.jpg)
A general framework for complex networks
Mark ChangiziSloan-Swartz Center for Theoretical Neuroscience
Caltech
![Page 2: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/2.jpg)
Behavioral Non-behavioral
Selected Nervous systems Ant colonies Organisms (cell-networks) Businesses Electronic circuits Computer software
Legos Furniture Buildings
Non-selected Competitive: Ecosystems World-wide web Economies Acquaintances Non-competitive: Crystals/molecules Galaxies
Taxonomy
?
![Page 3: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/3.jpg)
(1) Behavioral complexity
(2) Structural complexity
(3) Connectivity
(4) Parcellation
Four parts to the talk
![Page 4: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/4.jpg)
Part 1
Building behaviors
![Page 5: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/5.jpg)
Nodes and edges not shown
Structures in the network
etc
1
2
3
Behaviors of the network
Behaviors are built out of combinations of structures
![Page 6: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/6.jpg)
structures behaviors
electronic devices basic actions device functions
computer software instructions runs
bird vocalization syllables songs
mammalian behavior muscle actions behaviorscell genes cell types
Legos connections ---------ecosystems food chains ---------
Examples
![Page 7: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/7.jpg)
Universal language approach
Invariant-lengthapproach
?
1
2
3
4
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
How is behavioral repertoiresize increased?
![Page 8: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/8.jpg)
y = 0.8076x + 0.4157R2 = 0.7273
-0.5
0.5
1.5
2.5
-0.5 0.5 1.5 2.5Log number of song types
Log
num
ber
of s
ylla
ble
type
s
Bird vocalization
structural repertoire versus behavioral repertoire
y = 0.8014x + 2.7017R2 = 0.8419
y = 0.2668x + 2.6997R2 = 0.5911
0.5
1.5
2.5
3.5
-2.0 -1.5 -1.0 -0.5
Log index encephalization
Log
## muscles types C (n=8)
Mean #ethobehavior types E (n=8)
Prim
ates
Prob
osci
dea
Rod
entia
Peris
soda
ctyl
aLa
gom
orph
a
Art
ioda
ctyl
a
Did
elph
imor
phia
Car
nivo
ra
Cet
acea
Car
nivo
raC
hiro
pter
a
Inse
ctiv
ora
Prim
ates
Rod
entia
Lago
mor
pha
Art
ioda
ctyl
a
Mammalian behavior
None are universal languages. I.e., none are flat.
Instead, behavior length is invariant.
Log
num
ber
of b
utto
ns
y = 0.114x + 1.4105R2 = 0.8752
1
1.5
2
0 1 2 3Log length of manual
Calculators
y = 0.631x + 0.6669R2 = 0.842
1
1.5
2
1 1.25 1.5 1.75Log length of manual
Televisions
y = 0.4835x + 0.7947R2 = 0.4896
1
1.5
2
1 1.25 1.5 1.75Log length of manual
CD players
y = 0.2529x + 1.1603R2 = 0.5078
1
1.5
2
1.35 1.6 1.85 2.1Log length of manual
VCRs
Electronic user-interface languages
Changizi, 2001, 2002, 2003
y = 0.3423x + 3.6712R2 = 0.8408
3
4
5
-0.25 0.25 0.75 1.25 1.75 2.25
log # cell types
log
# ge
nes
E. coli
Yeast
C. Elegans
Drosophila
Human
Genes and cell types
![Page 9: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/9.jpg)
Computer software also tends to have invariant length behaviors, since programs must run within a feasible amount of time.
Instead of allowing running time to increase, programmers increase the number of instructions, or lines of code, in the program. [This is why, for example, quicksort has more lines of code than bubblesort.]
Computer software too
![Page 10: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/10.jpg)
Part 2
Building structures
![Page 11: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/11.jpg)
Nodes and edges not shown Edges not shown
...is actually...
Structures are built out of combinations of nodes
![Page 12: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/12.jpg)
nodes structures behaviors
electronic circuits components basic functional circuit
computer software operators instructions (or lines of code)
businesses employees basic functional groups
organisms cells basic functional cell combinations
ant colonies ants basic functional ant combinations
nervous systems neurons basic functional neuron combinations
universities faculty teaching combinations -------------
Legos piece connections -------------
ecosystems organism food chains -------------
Examples
![Page 13: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/13.jpg)
Universal language approach
Invariant-lengthapproach
?
How is structure repertoiresize increased?
![Page 14: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/14.jpg)
None are universal
languages.
Instead, structure length is invariant.
(Also true in competitivenetworks)
y = 0.2191x + 1.081R2 = 0.5204
0
1
2
-1.5 -1 -0.5 0
Log index of neuron encephalization
Log
# ne
uron
type
s
Neocortex: Networks of neurons
y = 0.1225x - 0.1926R2 = 0.4827
-0.5
0.5
1.5
0 2 4 6 8
log colony size
log
# of
phy
sica
l cas
tes
Ant colonies: Networks of ants
y = 0.0564x + 0.6062R2 = 0.24890
1
2
3
0 5 10 15
log # cells
log
# ce
ll ty
pes
Organisms: Networks of cells
y = 0.4262x + 0.1379R2 = 0.7488
0
1
2
0 1 2 3
Log # of components
Log
# of
com
pone
nt ty
pes Circuits: Networks of electronic components
y = 0.5512x - 0.6548R2 = 0.6952
0
1
2
3
2 3 4 5
log # students (~ log # faculty)
log
# de
part
men
ts
(log
# fa
culty
type
s)
Universities: Networks of faculty
y = 0.7089x + 0.2707R2 = 0.9043
0
1
2
3
0 1 2 3 4
Log # Lego pieces
Log
# Le
go p
iece
type
s
Legos: Networks of connectable pieces
Changiz et al., 2002
# node types versus network size
![Page 15: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/15.jpg)
y = 0.0137x + 2.3338R2 = 0.0024
1.5
2
2.5
3
-0.5 0 0.5 1 1.5 2 2.5
~ number of neurons
~ m
ean
# ne
uron
s ac
ross
y = 0.0841x + 0.8305R2 = 0.5334
0.5
1
1.5
0 0.5 1 1.5 2 2.5
Log N
Log
# ne
uron
type
s
Neocortex: # neuron types versus brain size
Brains thus appear to have invariant length structures
Cortical modules, barrels...: Number of neurons across versus network size
Invariant-length structures: Minicolumns and modules (below)
# neuron types increases in larger nervous networks: neocortex and retina
y = 0.1592x + 0.2311R2 = 0.8964
0.5
1
1.5
4.5 5 5.5 6 6.5
log # optic f ibers
log
# re
tinal
neu
ron
type
sgoldfish
cat
human
Retina: # neuron types versus brain size
![Page 16: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/16.jpg)
Computer software also has invariant length structures
Invariant-length structures: Lines of code
# operator types increases in larger programs:
y = 1.021x - 0.5733R2 = 0.9368
-1
1
3
5
7
0 1 2 3 4 5 6 7
log N
log
# lin
es o
f co
de
n=144
Lines of code versus program size
y = 0.3937x + 0.4352R2 = 0.8415
0
1
2
3
4
5
0 1 2 3 4 5 6 7
log Nlo
g #
oper
ator
s
n=185
# operator types versus program size
![Page 17: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/17.jpg)
Part 3
Connectivity and network diameterfor behaviors
![Page 18: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/18.jpg)
Behavior is combinatorial, and thus the structures must all be “close”.
And this can only be accomplished via edges,and edges are between nodes.
Edges not shown
...is actually...
Keeping structures “close” with edges
![Page 19: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/19.jpg)
nodes edges structures behaviors
electronic circuits components wires
computer software operators program flow edges
businesses employees communication
nervous systems neurons axons/dendrites
Legos piece linksecosystems organism trophic edges
Examples
![Page 20: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/20.jpg)
=2
* * * * *
For behavioral networks, expect...network diameter1/v, for N.
Payoff: scales up very slowly, saving wire.Cost: Behaviors are longer (roughly v times longer).
~N1
1~N1/2
2
invariant
How is node-degree increased?
Behavior not redundant, but wire too costly
Wire cost low, but diameter too high and thus
behavior increasingly redundant
![Page 21: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/21.jpg)
y = 0.5906x + 0.0593R2 = 0.9382
0
1
2
0 0.5 1 1.5 2 2.5 3log # edges
log
N
Electronic circuits
N ~ Vgray2/3
Nsyn ~ Vgray1
Neocortex
node-degree network diameter
electronic circuits ~N0.7 1.3
neocortex ~N0.5 2
How electronic circuits and neocortex scale
![Page 22: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/22.jpg)
Some other consequences of a node-degree increase
• Node density decreases
- neocortex: ~Vgray-1/3.
- circuits
• Wires (and somas) thicken
- neocortex: R~Vgray1/9
- circuits
• White matter disproportionately increases
- neocortex: Vwh~Vgray4/3 [disproportionate due to wire thickening]
![Page 23: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/23.jpg)
Also…node-degree increases in larger
software
![Page 24: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/24.jpg)
Part 4
Parcellation
![Page 25: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/25.jpg)
The partition problem
Broadly expect that # partitions scales up disproportionately slowly as network size increases
![Page 26: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/26.jpg)
Theory for neocortex
y = 2.0219x - 0.4448R2 = 0.9835
1.0
2.0
3.0
0.75 1.25 1.75log # areas (A)
log
# ed
ges
(G)
# edges vs # areasin sensory subnetw orks
predicted slope = 2
total # area-edges ~ A2Well-connectedness
![Page 27: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/27.jpg)
Economical well-connectednessimplies …
y = 0.4601x + 1.1997R2 = 0.5289
y = 0.5076x + 1.2097R2 = 0.6413
0
1
2
3
-1 0 1 2log # neurons
log
extr
apol
ated
tota
l num
ber
of
area
s
expected slope = 1/2
all animals:non-monotremes (dotted):
# areas ~ N1/2
y = 0.4575x + 0.6189R2 = 0.6777
0
1
2
0.0 0.5 1.0 1.5
log # neurons
log
D
degree vs # neuronssensory areasfrom all sources
predicted slope = 1/2
area degree ~ N1/2
![Page 28: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/28.jpg)
Parcellation also increases disproportionately slowly in other behavioral networks
y = 0.7028x - 1.3932
R2 = 0.9097
-1
0
1
2
3
4
0 1 2 3 4 5 6 7log N
log
# m
odul
es
n=77
# program modules vs program size
Computer software
y = 0.2122x + 0.1896R2 = 0.3013
0
0.5
1
1.5
1 2 3 4
Log # employeesLo
g #
divi
sion
s
n=53
# divisions vs # employees
Businesses
Probably electronic circuits too (partition problem)
![Page 29: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/29.jpg)
Conclusions
![Page 30: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/30.jpg)
Behavioral Non-behavioral
Selected Nervous systems Ant colonies Organisms (cell-networks) Businesses Electronic circuits Computer software
Legos Furniture Buildings
Non-selected Competitive: Ecosystems World-wide web Economies Acquaintances Non-competitive: Crystals/molecules Galaxies
Summary
1. Invariant-length structures2. Invariant-length behaviors3. Invariant network diameter (via slow increase in degree)4. Parcellation increases
1. Invariant-length structures
1. Invariant-length structures
?
![Page 31: A general framework for complex networks Mark Changizi Sloan-Swartz Center for Theoretical Neuroscience Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e8e5503460f94b911aa/html5/thumbnails/31.jpg)
?
short partition(MATRIXELREC edgelist[MATNUM], int p, int r){ float pivot; short i, j; MATRIXELREC temp; pivot=edgelist[p].weight; i=p-1; j=r+1;
while (i<j) { do j--; while (edgelist[j].weight > pivot);
do i++; while (edgelist[i].weight < pivot);
if (i<j) { temp=edgelist[j]; edgelist[j]=edgelist[i]; edgelist[i]=temp; } else return j; }}
void quickedgesort(MATRIXELREC edgelist[MATNUM], int p, int r){ int q;
if (p<r) { q=partition(edgelist,p,r); quickedgesort(edgelist,p,q); quickedgesort(edgelist,q+1,r); }}
Software code,carved at its joints
temp; r} else r return j; while partition } q } quickedgesort(MATRIXELREC (edgelist[MATNUM], p, int r) { float; short i, j; temp; pivot=edgelist[p].weight; j i=p-1; j=r+1; int = (i<j) { do j--; quickedgesort edgelist pivot i while MATRIXELREC (edgelist[j].weight >); void do; while (edgelist[i].weight < pivot); great if (i<) pivot r {temp=edgelist[j]; =edgelist[i]; ++ edgelist[i]=<) { q partition(,p,r); short (edgelist,p,); (edgelist,q+,); }} quickedgesort 1edgelist[MATNUM], int p, MATRIXELREC edgelist[j] int r) { int q; if (p
Same software code,but with nodes scrambled
The long-term grand goal:
The ability to parse complex networks so as to reveal their underlying program.