Formation of connectome: Formation of connectome: nature versus - - PowerPoint PPT Presentation

Formation of connectome: nature versus nurture Formation of connectome: nature versus nurture

Alexei Koulakov, Cold Spring Harbor Lab Alexei Koulakov, Cold Spring Harbor Lab

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Cold Spring Harbor Laboratory, New York

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Cell body (soma) Dendrites Action potential (spike) Axon Synapse

How the brain works

~1010 neurons ~1014 synapses

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How much information can be stored in connections?

log H  

~

Ns

N 

10

~10 N

neurons in cortex

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~10 s

synapses per neuron

log ~ 400 terabytes ~ 45 years of HD video H Ns N 

Wei, Tsigankov, Koulakov, Annals of NYAS (2013)

Genomic bottleneck

5 9

3 10 bp ~ 1 GB 

How can 1GB of information set up 400 TB of connections? Obviously, each synapse cannot be specified in the genome individually Human genome contains

• f information

Some simplifying rules are necessary Genome 1GB Cortical networks 400TB development rules evolution

Sperry, PNAS (1963) Wei, Tsigankov, Koulakov, Annals of NYAS (2013) Zador, Nature

• Commun. (2019)

Genomic bottleneck

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Genome 1GB Cortical networks 400TB

x  y 

Neurodevelopmental rules contained in the genome (1GB) carry information about the capacity of humans for intelligent behavior development rules evolution

Q2: How can genetic information be combined with experience- dependent network plasticity? Q1: What are the rules for genes to specify connections?

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Brain regions

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Cortical maps: topography

Kalatsky and Stryker, 2003

Many visual areas represent world topographycally

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Maps = connections

Supe rio r Co llic ulus

T ha la mus

Superior colliculus Visual cortex Retina Thalamus axons

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Topographic map

Eye Superior colliculus

How does a neuron know its position and know where to go?

axons

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Chemoaffinity hypothesis

Roger Sperry

Nobel Prize in Medicine 1981

I t seems a necessary conclusion … t hat t he cells and f ibers of t he brain and cord must carry some kind of individual ident if icat ion t ags, by which t hey are dist inguished one f rom anot her … —Roger Sperry, 1963

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Molecules define neuron’s position

Brown et al Cell (2000)

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Horizontal direction is imprinted in the eye and in the target by molecules

Retina EphA Superior colliculus ephrins-A axons

EphA/ephrin-A chemorepulsion

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Electrostatic model

( )

A A i i i

H q r  





H is minimized - repulsion ephrin-A level = EphA level =

• Total number of bound EphA receptors

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Vertical axis

Retina EphB Superior colliculus ephrins-B

EphB/ephrin-B chemoattraction

X Y

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Attraction in Y direction: another type of charge

ephrin-B level = EphB level =

( ) ( )

A A B B i i i i i i

H q r q r    

 

 

• The same axon carries two charges of different colors that interact with two potentials.

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Monte Carlo simulation

/ E T

probability e 

ephrin-B ephrin-A EphA or EphB or

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More general set of rules:

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( ) ( )

A A B B i i i i i i

H q r q r    

 

 

 

, , i i i A B

H M q r

    

 



 1 1 M         

, ,

ˆ ( )

ij i j i j

H W M W q

   

 



i

ij jr

W   

Matrix was optimized by evolution to yield the capacity for general intelligence

M

A1: Genetic information is converted into connectivity patterns by virtue of molecular tags (Sperry) Q2: How can genetic information be combined with experience dependent network plasticity?

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Data courtesy David Feldheim (UCSC)

Activity of neurons during topography formation: retinal waves

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What happens if there are no retinal waves

McLaughlin et al, (2003)

Disrupted retinal waves (2 -/- mice) Normal retinal waves Retina Superior colliculus Retina Superior colliculus Conclusion: axons with correlated activity are attracted to each other in the target

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Attraction between connections in the target

Correlated activity: Strong attraction Uncorrelated activity: Weak attraction

i

r 

j

r  i j

 

2

activity ij i j ij

H C U r r    



 

Correlation in activity due to waves in retina Distance in retina

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Brain versus physics

2 1

i j grav i j i j

r H m G r m



   



  2 1

i j elect i j i j r

r r H q q k



      2 ( )

ij activity i i j j

U r C r H       

charges do not separate, cannot introduce potential

 

2 2

( ) exp / 2

i j i j

U r r r r         

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Correlated activity leads to sharper projections

Retina SC, γ = 0 γ = ¼ γ = 1 … through attraction between axons neighboring in retina

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Unified Hamiltonian:

 

, ,

( ) 2

i i ij i j i ij A B

H M q r C U r r

    

    

 

  

molecules defined by genes nature experience learning nurture

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Sperry Hebb

2

i j ij ij ik im km ij ijkm

H M q W C W W U

   

   

 

Test of the model: maps is ephrin knockout mice

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Maps in ephrin knockouts

r c

Feldheim, Kim, Bergemann, Frise, Barbacid, and Flanagan, 2000

Mutant ephrinA -/- mouse Normal mouse Tracer injection in the eye

Superior colliculus 28

We have plenty of intuition about systems with attraction - gravity

Collapse induced by gravity Collapse induced by attraction between axons

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Maps in ephrin knockouts

Feldheim, Kim, Bergemann, Frise, Barbacid, and Flanagan, 2000

retina retina SC SC

Tsigankov and Koulakov (2006)

r c

Superior colliculus

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Topographic maps in ephrin triple-knockout (ephrin-A2/3/5 -/-) mice

retina Superior colliculus Data from David Feldheim (UCSC) and Jianhua Cang (Northwestern) (2007) A B

20 40 60 80 100 120

• 150
• 100
• 50

50 100 150 200 20 40 60 80 100 120

• 200
• 100

100 200 B

Position in the target Position in the eye A

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Ocular dominance patterns

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Ocular Dominance Pattern

Horton and Hocking, 1996 Left eye Right eye

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3-eyed frog

Cline, Debski, Constantine-Paton (1987)

1 2 1 2 3

Normal frog 3-eyed frog Each eye completely crosses over to the other side

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Two eyes projecting to the same target

Eye 1 Eye 3 q q Eye 1 & 3

• Eye 3

Eye 1

 

 

2

col col i i ij i j i ij col

H q r C U r r     

 

   1 ˆ 1 C       

E1 E3 E1 E3

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Conclusions:

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“Nothing in biology makes sense except in the light of evolution”

• Theodosius Dobzhansky

Nothing in intelligent behavior makes sense except in the light of biology Genomic bottleneck principle #1: information about brain’s capacity for general intelligence is compressed into < 1GB of mammalian genome Genomic bottleneck principle #2: The need to compress information about brain architecture into a small volume (<1GB) endowed mammalian brain with general intelligence.

• Tony Zador, Nature Communications (2019)
• Wei, Tsigankov, Koulakov, Annals of NYAS (2013)

Neurotheory Lab is hiring!

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koulakov@cshl.edu darkstar.cshl.edu