Dissertation Defense
Ben Cipollini
UC San Diego Cognitive Science
May 29, 2014
.
!
!
!
!
Asymmetries and communication: It’s time to tell the whole (brain) story.
Dissertation Defense
Ben Cipollini
UC San Diego Cognitive Science
May 29, 2014
.
Hi Mom!
Lateralization
Fundamental to being Human
Manual skill
Face
Processing
Language
The corpus callosum
Fundamental to lateralization?
Hofer et al (2008)
Visual perception
Lateralization and communication
LVF/RH
RVF/LH
Visual perception
Lateralization and communication
LVF/RH
RVF/LH
Visual perception
Lateralization and communication
LVF/RH
RVF/LH
Visual perception
Lateralization and communication
LVF/RH
RVF/LH
Visual perception
Lateralization and communication
LVF/RH
RVF/LH
Visual perception
Lateralization and communication
LVF/RH
RVF/LH
Visual perception
Lateralization and communication
LVF/RH
RVF/LH
Visual perception
Lateralization and communication
LVF/RH
RVF/LH
Visual perception
Lateralization and communication
LVF/RH
RVF/LH
Corpus Callosum
Two types of transfer
Aboitz et al (1992)
Aboitz and Monteil (2003)
Corpus Callosum
Two types of transfer
“Primary”-type
•
•
•
•
Aboitz et al (1992)
Aboitz and Monteil (2003)
Early sensory/motor areas
Large, fast fibers
Mature early
Connect topographically,
only near the midline
Corpus Callosum
Two types of transfer
“Primary”-type
•
•
•
•
Aboitz et al (1992)
Aboitz and Monteil (2003)
Early sensory/motor areas
Large, fast fibers
Mature early
Connect topographically,
only near the midline
Corpus Callosum
Two types of transfer
“Association”-type
•
•
•
•
Aboitz et al (1992)
Aboitz and Monteil (2003)
Late sensory / association areas
Small, slow fibers
Protracted maturation
Connect diffusely
Corpus Callosum
Two types of transfer
“Association”-type
•
•
•
•
Aboitz et al (1992)
Aboitz and Monteil (2003)
Late sensory / association areas
Small, slow fibers
Protracted maturation
Connect diffusely
Corpus Callosum
Two types of transfer
“Association”-type
•
•
•
•
Aboitz et al (1992)
Aboitz and Monteil (2003)
Late sensory / association areas
Small, slow fibers
Protracted maturation
Connect diffusely
Talk outline
Part 1: !
An integrated hypothesis
on lateralization of visual
processing
Part 2: !
Conduction delay
magnitude vs. variability
Part 3: !
Cross-species scaling of
callosal fibers
LH
!
!
small
!
RH
!
PART I: A brief introduction to
visual asymmetries
lateralization in vision
Behrmann & Plaut (2013)
Stimuli:!
Faces and words
RH
LH
Behrmann & Plaut (2013)
lateralization in vision
Behrmann & Plaut (2013)
Stimuli:!
Faces and words
RH
LH
Theory: !
Lateralization is due to competition from !
word learning; words localize with language.
LH
RH
Behrmann & Plaut (2013)
lateralization in vision
Behrmann & Plaut (2013)
Stimuli:!
Faces and words
RH
LH
Theory: !
Lateralization is due to competition from !
word learning; words localize with language.
LH
RH
Behrmann & Plaut (2013)
lateralization in vision
Behrmann & Plaut (2013)
Stimuli:!
Faces and words
RH
LH
Theory: !
Lateralization is due to competition from !
word learning; words localize with language.
LH
RH
Behrmann & Plaut (2013)
lateralization in vision
Behrmann & Plaut (2013)
Stimuli:!
Faces and words
RH
LH
Theory: !
Lateralization is due to competition from !
word learning; words localize with language.
LH
RH
Behrmann & Plaut (2013)
lateralization in vision
“Double filtering by frequency”
Stimuli:!
Navon figures (local/global)!
& spatial frequency gratings
LH
RH
Ivry & Robertson (1998)
lateralization in vision
“Double filtering by frequency”
Stimuli:!
Navon figures (local/global)!
& spatial frequency gratings
LH
RH
Ivry & Robertson (1998)
lateralization in vision
“Double filtering by frequency”
Stimuli:!
Navon figures (local/global)!
& spatial frequency gratings
LH
RH
Ivry & Robertson (1998)
lateralization in vision
“Double filtering by frequency”
Stimuli:!
Navon figures (local/global)!
& spatial frequency gratings
LH
Theory:!
Lateralization is due to asymmetric selection!
of task-related spatial frequency information.
RH
Ivry & Robertson (1998)
lateralization in vision
Our hypothesis
Stimuli:!
RH: contours, faces, low frequencies!
LH: words
LH
!
!
small
!
!
RH
lateralization in vision
Today’s hypothesis
Stimuli:!
RH: contours, faces, low frequencies!
LH: words
LH
!
!
small
!
!
RH
Theory:!
Lateralization is due to a connectivity asymmetry,!
mediated by developmental changes to!
interhemispheric competition.
LH (wide)
RH (narrow)
Part I: The Differential Encoding model
!
Tool: neural networks
Cortical connectivity
Long-range lateral connections?
feed-forward!
& feedback!
(inter-area)
Cortical connectivity
Long-range lateral connections?
feed-forwar
& feedback!
(
inflated
Cortical connectivity
Long-range lateral connections?
feed-forwar
& feedback!
(
inflated
flattened
Cortical connectivity
Long-range lateral connections?
feed-forwar
& feedback!
(
inflated
flattened
Cortical connectivity
Long-range lateral connections?
feed-forwar
& feedback!
(
inflated
flattened
“patchy”!
(intra-area)
Cortical connectivity
Long-range lateral connections?
feed-forwar
& feedback!
(
“patchy”!
(intra-area)
flattened
Cortical connectivity
Long-range lateral connections?
feed-forwar
& feedback!
(
“patchy”!
(intra-area)
flattened
Cortical connectivity
Long-range lateral connections?
feed-forwar
& feedback!
(
“patchy”!
(intra-area)
flattened
Differential encoding model
Mapping to patchy connectivity
(Retinontopic visual cortex)
Differential encoding model
Mapping to patchy connectivity
(Retinontopic visual cortex)
Differential encoding model
Mapping to patchy connectivity
(Retinontopic visual cortex)
Differential encoding model
Mapping to patchy connectivity
Hidden!
(Feature)
Input
(Retinontopic visual cortex)
Differential encoding model
Mapping to patchy connectivity
Output
Hidden!
(Feature)
Input
(Retinontopic visual cortex)
Differential encoding model
Mapping to patchy connectivity
(Retinontopic visual cortex)
Model parameters; !
width of Gaussian to sample from!
# connections / hidden unit
•
•
Connectivity Asymmetry
A Hypothesis
LH: Wide
RH: Narrow
Connectivity Asymmetry
A Hypothesis
LH: Wide
RH: Narrow
Connectivity Asymmetry
A Hypothesis
LH: Wide
RH: Narrow
Differential encoding model
Training methods
•
Create the network (850 input / output
pixels, 850 hidden units. Choose 𝞂, #conns)!
•
Train the network on a set of images!
!
!
!
Differential encoding model
Training methods
•
Create the network (850 input / output
pixels, 850 hidden units. Choose 𝞂, #conns)!
•
Train the network on a set of images!
!
!
!
Differential encoding model
Training methods
•
Create the network (850 input / output
pixels, 850 hidden units. Choose 𝞂, #conns)!
•
Train the network on a set of images!
!
!
!
Differential encoding model
Training methods
•
Create the network (850 input / output
pixels, 850 hidden units. Choose 𝞂, #conns)!
•
Train the network on a set of images!
!
!
!
Differential encoding model
Training methods
•
Create the network (850 input / output
pixels, 850 hidden units. Choose 𝞂, #conns)!
•
Train the network on a set of images!
!
!
!
Differential encoding model
Analysis methods
Differential encoding model
Analysis methods
•
Create the network (850 input / output
•
Train the network on a set of images
•
Present an image and compute:
pixels, 850 hidden units. Choose 𝞂, #conns)
Differential encoding model
Analysis methods
•
Create the network (850 input / output
•
Train the network on a set of images
•
Present an image and compute:
pixels, 850 hidden units. Choose 𝞂, #conns)
•
Output image (spatial frequency analysis)
Differential encoding model
Analysis methods
•
Create the network (850 input / output
•
Train the network on a set of images
•
Present an image and compute:
pixels, 850 hidden units. Choose 𝞂, #conns)
•
Output image (spatial frequency analysis)
•
Hidden unit activations (behavioral
comparison)
Behavioral test
lateralized presentation
LVF/RH
RVF/LH
Behavioral test
lateralized presentation
LVF/RH
RVF/LH
Behavioral test
lateralized presentation
LVF/RH
RVF/LH
Behavioral test
lateralized presentation
LVF/RH
RVF/LH
Behavioral test
lateralized presentation
LVF/RH
RVF/LH
Behavioral test
lateralized presentation
LVF/RH
RVF/LH
Behavioral asymmetry
RH/global, LH/local advantages
RH CVF LH
(LVF) (BH) (RVF)
Task: Did you see a target?!
Targets: T, H
Global
Target
Local
Target
Adapted from Sergent (1982)
Behavioral asymmetry
RH/global, LH/local advantages
RH (LVF)
LH (RVF)
RH CVF LH
(LVF) (BH) (RVF)
Task: Did you see a target?!
Targets: T, H
Global
Target
Local
Target
Adapted from Sergent (1982)
Behavioral asymmetry
RH/global, LH/local advantages
RH (LVF)
LH (RVF)
RH CVF LH
(LVF) (BH) (RVF)
Task: Did you see a target?!
Targets: T, H
Global
Target
Local
Target
Adapted from Sergent (1982)
Behavioral asymmetry
RH/global, LH/local advantages
RH (LVF)
LH (RVF)
RH CVF LH
(LVF) (BH) (RVF)
Task: Did you see a target?!
Targets: T, H
Global
Target
Local
Target
Adapted from Sergent (1982)
Behavioral asymmetry
RH/global, LH/local advantages
RH (LVF)
LH (RVF)
RH CVF LH
(LVF) (BH) (RVF)
Task: Did you see a target?!
Targets: T, H
Global
Target
Local
Target
Adapted from Sergent (1982)
Behavioral asymmetry
RH/global, LH/local advantages
RH (LVF)
LH (RVF)
RH CVF LH
(LVF) (BH) (RVF)
Task: Did you see a target?!
Targets: T, H
Global
Target
Local
Target
Adapted from Sergent (1982)
Differential encoding model
Accounting for human behavior (Sergent, 1982)
Methods:
•
•
•
Sample connections for LH and RH.
•
Train separate neural networks on
Sergent’s behavioral task.
Train on Navon stimuli. Record hidden unit activities for
each image.
!
Asymmetry:
•
Width of Gaussian that connections
are sampled from.
LH (wide)
RH (narrow)
Differential encoding model
Accounting for human behavior (Sergent, 1982)
RH
(LVF)
Global
Target
LH
(RVF)
Local
Target
Human Data
Adapted from Sergent (1982)
Hsiao et al. (2013)
Hsiao et al (submitted)
Differential encoding model
Accounting for human behavior (Sergent, 1982)
RH
(LVF)
Global
Target
LH
(RVF)
Local
Target
Human Data
Model Data
Adapted from Sergent (1982)
Hsiao et al. (2013)
Hsiao et al (submitted)
lateralization in vision
Differential encoding model
Expt 1: !
behavioral effects
Expt 2: !
spatial frequency processing
Expt 3: !
developmental pruning
RH: global
RH
RH
LH: local
LH
LH
Differential encoding model
Accounting for human behavior (Sergent, 1982)
Methods:
•
•
•
•
Sample connections for LH and RH.
Train on Navon stimuli. Record output images.
Analyze how well each model
hemisphere reproduces the image
at each spatial frequency, then
compare (RH - LH) !
Asymmetry:
•
Width of Gaussian that connections
are sampled from.
LH (wide)
RH (narrow)
Differential encoding model
Spatial frequency biases
-𝚫 log(power)
RH - LH (vs. original)
Lower
Higher
Hsiao et al. (2013)
Differential encoding model
Spatial frequency biases
RH - LH (vs. original)
-𝚫 log(power)
RH
Lower
Higher
Hsiao et al. (2013)
Differential encoding model
Spatial frequency biases
RH - LH (vs. original)
-𝚫 log(power)
RH
LH
Lower
Higher
Hsiao et al. (2013)
Differential encoding model
Spatial frequency biases
LH/
HSF
RH - LH (vs. original)
RH/
LSF
-𝚫 log(power)
RH
LH
Lower
Higher
Hsiao et al. (2013)
Differential encoding model
Spatial frequency biases
Lower
Higher
Hsiao et al. (2013)
Lower
Higher
Cipollini & Cottrell (COGSCI 2014)
lateralization in vision
Differential encoding model
Expt 1: !
behavioral effects
Expt 2: !
spatial frequency processing
Expt 3: !
developmental pruning
RH: global
RH: LSF
RH
LH: local
LH: HSF
LH
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
During development:
•
Visual acuity / contrast sensitivity
starts out poor, improves over time.
•
RH begins maturing earlier than the
LH (Geschwind & Galaburda, 1985; Hellige 1993;
Chiron et al., 1997)
•
Patchy connectivity matures via
pruning & strengthening connections
due to visual experience (Katz & Callaway,
1992; Burkhalter et al., 1993).
Katz and Callaway (1992)
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
During development:
•
Visual acuity / contrast sensitivity
starts out poor, improves over time.
•
RH begins maturing earlier than the
LH (Geschwind & Galaburda, 1985; Hellige 1993;
Chiron et al., 1997)
•
Patchy connectivity matures via
pruning & strengthening connections
due to visual experience (Katz & Callaway,
1992; Burkhalter et al., 1993).
RH will prune connections under
blurrier, lower spatial frequency input
Katz and Callaway (1992)
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
Methods:
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
Methods:
Start RH and LH networks with
equivalent connections.
Before
•
LH
RH
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
•
Start RH and LH networks with
equivalent connections.
•
Train on natural images, with RH
receiving more blurred versions of
the images than the LH.
Before
Methods:
LH
RH
full-fidelity
low-frequency
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
•
Start RH and LH networks with
equivalent connections.
•
Train on natural images, with RH
receiving more blurred versions of
the images than the LH.
•
While training, remove the weakest
connections.
Before
Methods:
LH
RH
full-fidelity
low-frequency
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
Start RH and LH networks with
equivalent connections.
•
Train on natural images, with RH
receiving more blurred versions of
the images than the LH.
•
While training, remove the weakest
connections.
After
•
Before
Methods:
LH
RH
full-fidelity
low-frequency
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
Start RH and LH networks with
equivalent connections.
•
Train on natural images, with RH
receiving more blurred versions of
the images than the LH.
•
While training, remove the weakest
connections.
Asymmetry:
•
RH trained on blurrier images than
the LH network.
After
•
Before
Methods:
LH
RH
full-fidelity
low-frequency
Differential Encoding Model
LH
RH
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
Batch 1
Batch 2
Batch 3
Batch 4
Batch 5
Batch 6
(train to !
error criterion)
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
RH: Blurred
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
Compile
connection distribution
RH: Blurred
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
RH: LH: Blurred
Full-Fidelity
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
-
=
RH: LH: Blurred
Full-Fidelity
RH - LH
Differential Encoding Model
Developmental pruning (Cipollini & Cottrell, COGSCI 2014)
-
!
=
Shorter connections encode !
LSF information better.
!
RH: LH: Blurred
Full-Fidelity
RH - LH
lateralization in vision
Differential encoding model
Expt 1: !
behavioral effects
Expt 2: !
spatial frequency processing
Expt 3: !
developmental pruning
RH: global
RH: LSF
RH: short
LH: local
LH: HSF
LH: long
Connectivity Asymmetry
Summary
•
RH is specialized for encoding local spatial relationships.!
•
This is due to shorter long-range lateral connections in RH
retinotopic visual areas.!
•
The connectivity asymmetry comes from development.!
•
Visual acuity / contrast sensitivity changes interact with
asymmetry in developmental timing.!
•
Due to developmental connection pruning
Connectivity Asymmetry
May capture different relationships
Connectivity Asymmetry
May capture different relationships
Natural images
Faces
Locally highly predictive,!
decreases with distance
Connectivity Asymmetry
May capture different relationships
Natural images
Faces
Words
Not highly predictive,!
more spread,!
highly anisotropic
Locally highly predictive,!
decreases with distance
Connectivity Asymmetry
May capture different relationships
Natural images
Faces
Words
Not highly predictive,!
more spread,!
highly anisotropic
Locally highly predictive,!
decreases with distance
Connectivity Asymmetry
May capture different relationships
Natural images
Faces
Words
Not highly predictive,!
more spread,!
highly anisotropic
Locally highly predictive,!
decreases with distance
Connectivity Asymmetry
May capture different relationships
Natural images
Faces
Words
Not highly predictive,!
more spread,!
highly anisotropic
Locally highly predictive,!
decreases with distance
Connectivity Asymmetry
May capture different relationships
Natural images
Faces
Words
Not highly predictive,!
more spread,!
highly anisotropic
Locally highly predictive,!
decreases with distance
Role of corpus callosum?
Development is the key.
Role of corpus callosum?
Development is the key.
A problem?
• Our developmental model shows asymmetry, but weaker lateralization.
• Connection asymmetry develops early (by 15 months of age) without
hemispheric competition.
Role of corpus callosum?
Development is the key.
A problem?
• Our developmental model shows asymmetry, but weaker lateralization.
• Connection asymmetry develops early (by 15 months of age) without
hemispheric competition.
• Developmental data suggest lateralization through competition,
increasing from ~3-5 years through ~14 years old.
Role of corpus callosum?
Development is the key.
A problem?
• Our developmental model shows asymmetry, but weaker lateralization.
• Connection asymmetry develops early (by 15 months of age) without
hemispheric competition.
• Developmental data suggest lateralization through competition,
increasing from ~3-5 years through ~14 years old.
A solution?
Role of corpus callosum?
Development is the key.
A problem?
• Our developmental model shows asymmetry, but weaker lateralization.
• Connection asymmetry develops early (by 15 months of age) without
hemispheric competition.
• Developmental data suggest lateralization through competition,
increasing from ~3-5 years through ~14 years old.
A solution?
• Could make it stronger by making the inputs stronger, or it could be
enhanced with interhemispheric competition (Dailey & Cottrell, 1999; Reggia &
Shulz, 2002)
Role of corpus callosum?
Development is the key.
A problem?
• Our developmental model shows asymmetry, but weaker lateralization.
• Connection asymmetry develops early (by 15 months of age) without
hemispheric competition.
• Developmental data suggest lateralization through competition,
increasing from ~3-5 years through ~14 years old.
A solution?
• Could make it stronger by making the inputs stronger, or it could be
enhanced with interhemispheric competition (Dailey & Cottrell, 1999; Reggia &
Shulz, 2002)
lateralization in vision
Our hypothesis
• [3-15 months] A connection asymmetry emerges via connection pruning
with relatively weak lateralization (De Schonen and Deruelle, 1991).!
• [15 months+] Pruning ends; further visual experience of relatively
independent hemispheres suppresses asymmetries.!
• [5-14 years] Late visual areas connect with thin fibers that mature late.
Maturation enables competition => lateralization.!
• The latent connection difference leads to a RH bias to learn faces,
contours, low frequency information; LH to learn words faster.
lateralization in vision
Our hypothesis
• [3-15 months] A connection asymmetry emerges via connection pruning
with relatively weak lateralization (De Schonen and Deruelle, 1991).!
• [15 months+] Pruning ends; further visual experience of relatively
independent hemispheres suppresses asymmetries.!
• [5-14 years] Late visual areas connect with thin fibers that mature late.
Maturation enables competition => lateralization.!
• The latent connection difference leads to a RH bias to learn faces,
contours, low frequency information; LH to learn words faster.
!
What mechanisms might cause a developmental shift
from weak to robust interhemispheric communication?
Part II:
Brain size, delays, & Independence
!
Tools: Neural network modeling
inter-dependent or
independent?
Wada test
•
•
“Turn off" one hemisphere!
Split brain
•
!
No report of half the world
missing
•
Alan Alda: Do you feel any
differently when you think?!
!
JW: No… just got a backup
brain, is all…
Does brain size cause
lateralization?
!
Well… it must.!
Does brain size cause
lateralization?
Conduction Delay Magnitude
!
Well… it must.!
Ringo et al. (1994)
Does brain size cause
lateralization?
Conduction Delay Magnitude
Proportion of Fibers
!
Well… it must.!
Ringo et al. (1994)
Rilling & Insel (1999)
Hemispheric independence
Exists? Leads to lateralization?
Hemispheric independence
Exists? Leads to lateralization?
• Anatomical and functional asymmetries
exist in large brains... and small brains (e.g.
Rogers, 2009).
Hemispheric independence
Exists? Leads to lateralization?
• Anatomical and functional asymmetries
exist in large brains... and small brains (e.g.
Rogers, 2009).
• Humans without a corpus callosum (i.e.
very few / slow connections) don’t show
greater lateralization (e.g. Gazzaniga, 2000; Paul
et al, 2007).
Paul (2011)
Hemispheric independence
Exists? Leads to lateralization?
• Anatomical and functional asymmetries
exist in large brains... and small brains (e.g.
Rogers, 2009).
• Humans without a corpus callosum (i.e.
very few / slow connections) don’t show
greater lateralization (e.g. Gazzaniga, 2000; Paul
et al, 2007).
• Delays decrease with development, but
lateralization increases (e.g. Dundas et al., 2012;
Petitto et al., 2012).
Petitto et al. (2012)
Hemispheric independence
Exists? Leads to lateralization?
• Anatomical and functional asymmetries
exist in large brains... and small brains (e.g.
Rogers, 2009).
• Humans without a corpus callosum (i.e.
very few / slow connections) don’t show
greater lateralization (e.g. Gazzaniga, 2000; Paul
et al, 2007).
• Delays decrease with development, but
lateralization increases (e.g. Dundas et al., 2012;
Petitto et al., 2012).
• Interhemispheric functional connectivity
very strong in development and
adulthood (e.g. Stark et al., 2008; Uddin et al., 2010).
Stark et al. (2008)
Conduction delay magnitude
Effect is overstated, maybe irrelevant
Ringo et al. (1994)
Cipollini & Cottrell (COGSCI 2013)
Conduction delay magnitude
Effect is overstated, maybe irrelevant
• Model shows a short delay to onset of
interhemispheric communication
(otherwise communication is identical).
Ringo et al. (1994)
Cipollini & Cottrell (COGSCI 2013)
Conduction delay magnitude
Effect is overstated, maybe irrelevant
• Model shows a short delay to onset of
interhemispheric communication
(otherwise communication is identical).
! ! ! ! ! AND
Ringo et al. (1994)
Cipollini & Cottrell (COGSCI 2013)
Conduction delay magnitude
Effect is overstated, maybe irrelevant
• Model shows a short delay to onset of
interhemispheric communication
(otherwise communication is identical).
! ! ! ! ! AND
• Callosal and interhemispheric delay
magnitude differences are exaggerated.
(model: 10x difference; real system 1-3x? [5-20ms?])
Ringo et al. (1994)
Cipollini & Cottrell (COGSCI 2013)
Conduction delay magnitude
Effect is overstated, maybe irrelevant
• Model shows a short delay to onset of
interhemispheric communication
(otherwise communication is identical).
Corpus Callosum#
! ! ! ! ! AND
Delays to midline
• Callosal and interhemispheric delay
magnitude differences are exaggerated.
(model: 10x difference; real system 1-3x? [5-20ms?])
• Callosum contains a distribution of fibers,
including a number of very fast fibers.
1
5
10
...
Delay (µs)
Adapted from Caminiti et al. (2009)
Cipollini & Cottrell (COGSCI 2013)
Conduction delay magnitude
Effect is overstated, maybe irrelevant
• Model shows a short delay to onset of
interhemispheric communication
(otherwise communication is identical).
Corpus Callosum#
! ! ! ! ! AND
Delays to midline
• Callosal and interhemispheric delay
magnitude differences are exaggerated.
(model: 10x difference; real system 1-3x? [5-20ms?])
• Callosum contains a distribution of fibers,
including a number of very fast fibers.
• Prediction can overcome any these delays
1
5
10
...
Delay (µs)
Adapted from Caminiti et al. (2009)
(only need temporal correlations within 5-20 ms)
Cipollini & Cottrell (COGSCI 2013)
Does brain size cause
lateralization?
Proportion of Fibers
N
O
Conduction Delay Magnitude
Ringo et al. (1994)
Rilling & Insel (1999)
Conduction delay magnitude
Effect is overstated, maybe irrelevant
Cipollini & Cottrell (COGSCI 2013)
Conduction delay magnitude
Effect is overstated, maybe irrelevant
•
Conduction delay correlates with
things that matter
• White matter volume
Cipollini & Cottrell (COGSCI 2013)
Conduction delay magnitude
Effect is overstated, maybe irrelevant
•
Conduction delay correlates with
things that matter
• White matter volume
• Metabolism
Cipollini & Cottrell (COGSCI 2013)
Conduction delay magnitude
Effect is overstated, maybe irrelevant
•
Conduction delay correlates with
things that matter
• White matter volume
• Metabolism
• Variability in conduction delays
Cipollini & Cottrell (COGSCI 2013)
Conduction delay magnitude
Effect is overstated, maybe irrelevant
•
Conduction delay correlates with
things that matter
• White matter volume
• Metabolism
• Variability in conduction delays
Variability in conduction delays exist,
but not in the cognitive literature.
Cipollini & Cottrell (COGSCI 2013)
Development & delays
Thin fibers ⇒ unreliable delays
Unmyelinated fibers
Post-natal day 150
Post-natal day 26
Prenatal
adapted from Berbel & Innocenti (1988)
Cipollini & Cottrell (COGSCI 2013)
Development & delays
Thin fibers ⇒ unreliable delays
Unmyelinated fibers
Post-natal day 150
Small, unmyelinated axons
have unreliable delays (up to 20%)
(Wang et al, 2008; Faisal et al. 2008)
Post-natal day 26
Prenatal
adapted from Berbel & Innocenti (1988)
Cipollini & Cottrell (COGSCI 2013)
Development & delays
Thin fibers ⇒ unreliable delays
Unmyelinated fibers
Post-natal day 150
Small, unmyelinated axons
have unreliable delays (up to 20%)
(Wang et al, 2008; Faisal et al. 2008)
Post-natal day 26
Degree of effect may be
unique to humans
Prenatal
adapted from Berbel & Innocenti (1988)
Cipollini & Cottrell (COGSCI 2013)
(long fibers, highly compressed brains)
Development & delays
Thin fibers ⇒ unreliable delays
Unmyelinated fibers
Post-natal day 150
Small, unmyelinated axons
have unreliable delays (up to 20%)
(Wang et al, 2008; Faisal et al. 2008)
Post-natal day 26
Degree of effect may be
unique to humans
Prenatal
adapted from Berbel & Innocenti (1988)
Cipollini & Cottrell (COGSCI 2013)
(long fibers, highly compressed brains)
What are the effects
of delay-dependent
variability?
Unreliable delays:
Cause independence, delay lateralization?
•
Reliable delays do not interrupt longdistance communication. $
•
Unreliable delays cause a bias for local
circuits, more independent hemispheres.$
•
Reduction in variability during
development leads to greater
hemispheric collaboration with age.
image from Lewis & Elman (2008)
Model params: noise = 1% (mean)$
all delays=1, cc delays=10, Tend=35
Cipollini & Cottrell (COGSCI 2013)
Unreliable delays:
Cause independence, delay lateralization?
•
Reliable delays do not interrupt longdistance communication. $
•
Unreliable delays cause a bias for local
circuits, more independent hemispheres.$
•
Reduction in variability during
development leads to greater
hemispheric collaboration with age.
image from Lewis & Elman (2008)
Lateralization of language, handedness,
Modelage
params:
visual processing all increase with
noise = 1% (mean)$
all delays=1, cc delays=10, Tend=35
Cipollini & Cottrell (COGSCI 2013)
Does brain size cause
lateralization?
Proportion of Fibers
N
O
Conduction Delay Magnitude
Ringo et al. (1994)
Rilling & Insel (1999)
Does brain size cause
lateralization?
Proportion of Fibers
N
O
Conduction Delay Magnitude
Ringo et al. (1994)
Rilling & Insel (1999)
Collaboration is strong
and may enhance lateralization
Part II:
Brain size, connections, & Independence
!
Tools: Photoshop/MATLAB & linear regression
Allometry:
[Big brain] <> big [little brain]
Quantities scale as power law of brain volume
Allometry:
[Big brain] <> big [little brain]
Quantities scale as power law of brain volume
Surface area / cortical folding
should be: SA ∝ BV2/3!
actual: SA ∝ BV8/9
Allometry Basics
Three potential relationships
Euclidean axes
log-log axes
This is how
mammalian
brains
work!
Changizi (2009)
This is how
mammalian
brains
work!
Changizi (2009)
This is how
mammalian
brains
work!
Changizi (2009)
This is how
mammalian
brains
work!
Changizi (2009)
Slower scaling of the corpus callosum?
MRI volumes for !
11 primate species
Rilling & Insel (1999)
MRI volumes for !
11 primate species
Desired
Slower scaling of the corpus callosum?
vs.
Total fibers
Rilling & Insel (1999)
Callosal fibers
MRI volumes for !
11 primate species
Desired
Slower scaling of the corpus callosum?
vs.
Estimated
Total fibers
Rilling & Insel (1999)
Callosal fibers
vs.
Grey matter!
surface area
Callosal !
surface area
Slower scaling of the corpus callosum?
Euclidean
Rilling & Insel (1999)
Log-log
Corroborate results
1.
Missing quantities (neuron
Must compute:
density, CC axon density, etc.)
vs.
also scale with brain size.!
2.
Scaling of inter-area
connectivity differs:!
• Per area: connects to a
greater # intrahemispheric
areas in larger brains!
• Per area: connects to the
same # interhemispheric
area in larger brains
Callosal fibers
Total fibers
Must compare to:
vs.
Inter-area connections
1
Corroborate results
Corroborate results
Small brain:
•
•
# areas: 16
# inter-area cxns: 4/area
Large brain:
•
•
# areas: 32
# inter-area cxns: 8/area
Corroborate results
Small brain:
•
•
•
# areas: 16
# inter-area cxns: 4/area
% CC cxns: 1/4
Large brain:
•
•
•
# areas: 32
# inter-area cxns: 8/area
% CC cxns: 1/8
Corroborate results
Small brain:
•
•
•
•
# areas: 16
# inter-area cxns: 4/area
% CC cxns: 1/4
% V1=>V2 cxns: 1/4
Large brain:
•
•
•
•
# areas: 32
# inter-area cxns: 8/area
% CC cxns: 1/8
% V1=> V2 cxns: 1/8
Corroborate results
Small brain:
•
•
•
•
# areas: 16
# inter-area cxns: 4/area
% CC cxns: 1/4
% V1=>V2 cxns: 1/4
Large brain:
•
•
•
•
❖
# areas: 32
# inter-area cxns: 8/area
% CC cxns: 1/8
% V1=> V2 cxns: 1/8
Comparing total connections != comparing functional connectivity
Corroborate results
Small brain:
•
•
•
•
# areas: 16
# inter-area cxns: 4/area
% CC cxns: 1/4
% V1=>V2 cxns: 1/4
Large brain:
•
•
•
•
# areas: 32
# inter-area cxns: 8/area
% CC cxns: 1/8
% V1=> V2 cxns: 1/8
❖
Comparing total connections != comparing functional connectivity
❖
Instead: compare # fibers per inter-area connection (a la DTI)
Corroborate results
1. Must compute:
vs.
Callosal fibers
Total fibers
2. Must compare to:
vs.
Inter-area connections
1
Computing desired
values
Formulas:!
•
•
•
[# callosal connections] = [callosal area] * [axon density /mm2]!
[# white matter connections] = [#cortical neurons] * [% projecting]!
[#cortical neurons] = [grey matter volume] * [neuron density / mm3]
The Compromise:
Grey Matter Area vs. CC Area
Formulas:!
•
•
[# callosal connections]
: CCcxns ∝ TBV0.662 * Densfibers in CC !
[# white matter connections] : WMcxns ∝ TBV0.85 * TBV-0.32
Example method
CC fiber density (new result!)
Wang et al. (2008)
Allometric regression
Example method
CC fiber density (new result!)
Wang et al. (2008)
Allometric regression
Example method
CC fiber density (new result!)
Wang et al. (2008)
Allometric regression
Example method
CC fiber density (new result!)
Wang et al. (2008)
Allometric regression
% CC Connections
Decreases (way) faster than
Rilling estimate:
!
TBV0.88
!
!
!
Our estimate:
!
TBV0.64
% CC Connections
Decreases (way) faster than
Rilling estimate:
!
TBV0.88
Not even close; why?
!
!
!
Our estimate:
!
TBV0.64
Corroborate results
1. Must compute:
vs.
Callosal fibers
Total fibers
2. Must compare to:
vs.
Inter-area connections
1
CC decrease due to more
areas?
CC decrease due to more
areas?
x:
/
ratio of inter-area connections!
that are interhemispheric!
—Changizi & Shimojo (2005)
CC decrease due to more
areas?
ratio of fibers!
that are interhemispheric!
—just computed!
y:
x:
/
ratio of inter-area connections!
that are interhemispheric!
—Changizi & Shimojo (2005)
CC decrease due to more
areas?
ratio of fibers!
that are interhemispheric!
—just computed!
/
ratio of inter-area connections!
that are interhemispheric!
—Changizi & Shimojo (2005)
CC decrease due to more
areas?
ratio of fibers!
that are interhemispheric!
—just computed!
/
ratio of inter-area connections!
that are interhemispheric!
—Changizi & Shimojo (2005)
Strength of CC
connections
• CC connections are
strong: 4.3x larger than
the average intrahemispheric area-area
connection!
• This is true across
species!
• This is true, in spite of
their long distance
Summary
A re-examination of % cc fibers
Rilling & Insel (1999):$
!
❖
The proportion of
interhemispheric fibers mildly
decreases with brain size
(exponent=0.88)$
❖
This suggests a functional
decrease in callosal communication.$
Summary
A re-examination of % cc fibers
Rilling & Insel (1999):$
!
Our claims:$
❖
The proportion of
interhemispheric fibers mildly
decreases with brain size
(exponent=0.88)$
❖
The proportion of
interhemispheric fibers drastically
decreases with brain size
(exponent=0.63)$
❖
This suggests a functional
decrease in callosal communication.$
❖
This suggests no functional
decrease in callosal communication.$
❖
A comparison of fiber counts between
interconnecting areas suggests
callosal fiber bundles may be
special (unexpectedly strong).
Summary
A re-examination of % cc fibers
Rilling & Insel (1999):$
!
Our claims:$
❖
The proportion of
interhemispheric fibers mildly
decreases with brain size
(exponent=0.88)$
❖
The proportion of
interhemispheric fibers drastically
decreases with brain size
(exponent=0.63)$
❖
This suggests a functional
decrease in callosal communication.$
❖
This suggests no functional
decrease in callosal communication.$
❖
A comparison of fiber counts between
interconnecting areas suggests
callosal fiber bundles may be
special (unexpectedly strong).
No evidence of hemispheric independence in humans, !
no relationship between independence and lateralization.
Does brain size cause
lateralization?
Ringo et al. (1994)
Proportion of Fibers
N
O
N
O
Conduction Delay Magnitude
Rilling & Insel (1999)
Does brain size cause
lateralization?
Ringo et al. (1994)
Proportion of Fibers
N
O
N
O
Conduction Delay Magnitude
Rilling & Insel (1999)
Collaboration is strong
and may enhance lateralization
Talk Summary
• I suggested that development and interhemispheric communication drive
connection differences that lead to visual asymmetries. !
• I offered a mechanism for the developmental bias towards more local circuits:
conduction delay variability.!
• I stated that conduction delay magnitude likely does not cause independence,
and that independence is unlikely to cause lateralization anyway.!
• I showed that callosal connectivity is robust across species (including humans),
with evidence of a special role.
Independence = self-reliance
Solve everything yourself!
Independence: fend for yourself.
Collaboration = specialization
Communication mediates specialization
Independence: fend for yourself.
Collaboration = specialization
Communication mediates specialization
Collaboration: specialization emerges.
Independence: fend for yourself.
Thank you!
• Gary Cottrell & GURU!
• Committee!
• CARTA!
• Funding sources!
•
NSF / TDLC & CARTA!
• All you!
• TDLC / PEN!
•
For your support!
• Collaborators (Janet Hsiao)!
•
For listening!
•
For your questions!
• Kari Hanson, Eva Dundas,
Kaja Jasinska, Gabrielle
Mussachia!
• Family & loved ones!