►
From YouTube: DevoWorm (2021, Meeting 23): GSoC Update #3 (Segmentation Maps), Steve McGrew, Cephalod Brains.
Description
Update on Summer of Code activities (Mainak Deb, Improving DevoLearn), memorial for Steve McGrew (holography, microscopy, genetic algorithms), discussion on scaling in the ball microscope, and papers on C. elegans Metabolic Networks (WormPaths) and Neural Progenitor migration in Octopus brain development. Attendees: Susan Crawford-Young, Richard Gordon, Bradly Alicea, and Mainak Deb.
A
B
C
B
B
C
B
C
C
B
C
B
C
C
C
B
C
C
B
D
B
D
Yeah,
okay,
so
I'll
start
with
my
update
for
last
week.
So
last
week's
goal
was
to
actually
upgrade
the
cell
nutrient
segmentation
model
and
like
at
the
start,
I
basically
like.
I
basically
tried
to
see
what's
like
if,
if
like,
if
there
was
something
wrong
with
the
data
which
was
being
used
for
the
training
so
like
the
first
issue
that
I
faced
is
that
there
was
a
slight
offset
in
there
in
the
file
names
of
the
images.
D
D
D
D
D
D
D
D
B
D
D
B
D
D
D
D
D
The
original
mask
it
actually
contains
a
little
mistake
over
here
in
between
these
two
surfaces,
clearly
a
boundary
but
in
the
origin
of
that
in
the
in
this
in
this
mask,
there's
no
boundary
here.
It's
it's
actually
a
partial
line
here,
that's
all,
but
the
model
it
it
actually
captures
the
boundary.
Even
though
the
original
mask.
B
D
D
This
part
is,
it
should
not
be
like
this,
and
it
should
be
like
this.
So
that's
something
interesting
that
that's
something
interesting
that
I
saw
and
the
next
photo
is
this
like
this
photo
is
very
dim
like,
as
you
can
see,
this
photo
is
very
dim,
and
the
original
mask
looks
something
like
this
and
the.
D
But
I
really
did
not
get
time
to
deploy
this
into
a
gui
and
check
it
out,
but
I
don't
think
it
will
take
much
time
to
replace
the
model
and
in
fact
I'll
be
replacing
the
model
in
the
library
itself
very
soon
by
today
itself.
Yes,
so
I
think
that's
it
for
this
week's
update
and
if
anyone
has
any
comments.
C
B
B
D
D
A
I
make
a
suggestion
if
you
look
lower
right
hand,
corner
picture
that
you
just
had
on
the
screen.
Is
it
this
one
yeah
yeah
the
right
hand
the
right
hand,
one
yeah?
Okay,
you
see
three
lines
that
don't
they're.
Okay,
three,
three,
yes,
no
yeah!
Is
there
any
reason
not
to
detect
such
lines
and
just
extend
them
as
straight.
A
A
A
D
D
B
D
C
Well,
just
there
was
that
very
nice
programming,
and
I
put
that
in
the
chat.
I
maybe
would
like
to
use
this
depending
on
what
I'm
doing,
I'm
after
all,
compressing
a
tissue.
So
it's
going
to
have
distorted
boundaries
between
the
cells.
Then,
maybe
I'd
like
to
see
that
reduce
to
lines
and
instead
of
instead
of
the
whole
thing,
if
you
could
just
see
where
it
where
the
boundary
guards
it
would
be
useful,
yeah.
D
C
B
B
D
D
D
D
D
D
B
B
Unfortunately,
he
lost
his
battle
with
cancer
this
last
week
or
so,
and
it's
a
sad
time,
but
we
have
a
little
bit
of.
I
wanted
to
go
back
over
some
of
the
things
that
he's
done.
He
was
a
very
interesting
person.
He
was
a
polymath,
what
they
call
a
polymath,
which
means
he
was
involved
in
a
lot
of
different
things.
B
So
you're
like
why
what
was
his
specialty
for
this
group?
Well,
he
was
interested
in,
I
think
biological
complexity.
He
was
also
interested
in
computer
science.
He
had
a
number
of
different
algorithms
that
he
had
worked
on,
especially
in
genetic
programming,
and
he
also
did
some
microscopy,
and
so
I'm
going
to
show
some
things
here
if
you
want
to
catch
up
on
them
after
the
meeting
or
you
know,
it'll
give
you
some
sort
of
background
into
his
work.
So
let
me
share
my
screen
here.
B
So,
okay,
I
think
this
is
so.
This
is
steve's
company
new
light
industries
limited,
so
he
founded.
This
company
was
founded
in
1992
in
spokane,
washington
and
that's
in
the
u.s.
He
founded
this.
They
did
a
lot
of
things
with
engineering
and
with
start
emerging
computer
science
and
engineering
doing
a
lot
of
holography.
B
So
this
is
where
you
create
holograms
doing
some
uv
imaging
electro
forming
vacuum
coating
optics
robotics.
So
there
is
a
very
wide
range
of
things
that
they
were
doing
in
this
company,
and
so
this
was
a
range
of
things.
He
had
a
number
of
patents
as
well,
so
his
patents,
here's
a
list
of
his
patents.
He
had
all
sorts
of
different
patents
in,
like
physics
and
engineering
and
quantum
dot
security,
device
and
method,
for
example,
or
a
method
of
apparatus
for
reading
and
verifying
holograms.
B
Here's
one
more
holography
down
here,
so
he
was
very
much
at
that
interface
of
like
imaging
and
technology
and
programming.
He
did
a
lot
of
stuff
with
genetic
algorithms,
not
sure
if
that's
reflected
in
his
patents,
but
I
think
you
can
see
that
he
was
doing
a
lot
of
different
things
and
he
was
getting
patents
on
them.
So
so
that's
one
way
that
he
made
a
contribution
to
the
world.
It
was
very
interesting
work,
there's
an
article
here
so
he's
from
spokane
washington.
B
He
was
his
business
is
based
there
and
this
is
from
the
local
paper
an
article
from
1997
on
his
work.
So
this
was
quite
a
while
ago,
but
this
is
so
he
did
a
lot
of.
He
was
also
an
artist
and
he
did
a
lot
of
things
with
metal
working
and
so
his
holography
fits
into
the
metal
working
aspect
of
it
as
well.
It's
sort
of
like
the
you
know
that
that
sort
of
general
arts-
you
know
craftsman
type
thing
so
steve
mcgrew.
B
His
three-dimensional
work
shows
up
on
credit
cards
licenses,
and
so
counterfeiting
is
a
good
business
for
new
light
industries.
They
weren't
counterfeiting
money.
They
were
trying
to
build
holographic
security
technologies
for
money
paper
money.
So
if
you
look
at,
I
don't
know
how
how
it
is
in
other
countries,
but
in
the
united
states-
and
I
think
in
canada
as
well.
The
money
has
special
security
devices
built
in
they
have
holographic
verification.
B
They
have
different
things
that
they
put
in
they
print
on
the
money
that
help
people
detect
real
bills
from
forged
bills,
and
so
he
was
some
of
his
work
went
into
that
sort
of
thing.
It
was
very
practical
and
he's
been
been
doing
this
sort
of
stuff
with
holography
for
decades,
and
so
this
really
has
its
roots
in
physics.
B
B
B
B
He
founded
this
company
called
light
impressions
and
the
company
was
doing
five
million
dollars
a
year
business.
They
were
located
in
santa
cruz,
california,
and
this
was
in
the
80s
and
then
in
1989.
There
was
an
earthquake
in
the
san
francisco
bay
area,
and
the
problem
was:
is
that
his
home,
the
earthquakes,
put
his
house
in
two
because
it
was
right
near
the
epicenter
of
the
earthquake.
B
So
then
he
ended
up
leaving
california
moving
back
to
washington
state
and
that's
where
he
found
new
light
industries
and
that's
where
he
was
for
most
the
rest
of
his
life.
I
I
assume,
but
this
was
you
know,
so
he
had
some
visions
for
this
beyond.
Just
the
practical,
though
mcgregor
city
had
also
tinkered
with
virtual
reality,
eyeglasses
my
vision
for
that
is
a
wearable
computer,
but
don't
see
a
lot
of
time
to
develop
the
idea.
B
B
But
there
is
this:
he
did
do
a
lot
of
stuff
with
algorithms
and
genetic
algorithms
and
he
was
doing
some
stock
market
prediction
as
well.
So
he's
he
went
into
a
lot
of
different
areas.
Definitely
think
that's
so
I
I
can
make
a
list
of
things
for
people.
I
can
send
it
out
by
email
if
people
are
interested
in
checking
them
out.
Although
I
covered
a
lot
of
that
in
the
in
this
little
presentation,
so
did
anyone
have
anything
to
add
on
that.
B
A
C
A
A
A
B
A
A
B
A
B
B
A
B
A
B
Okay,
well
yeah.
Maybe
we'll
pull
that
stuff
up,
maybe
for
next
week
too,
to
talk
more
about
it.
Okay,
yeah,
let's
see
so
my
screen's
already
being
shared.
So
I
also
wanted
to
point
this
out,
and
this
is
if
you're
interested
in
learning
more
about
open
worm
and
about
sort
of
the
thinking
behind
open
worm.
There's
this
carbon
copies
foundation
workshop
they're
doing
the
carbon
copies
foundation.
I
think
they're
doing
some
sort
of
whole
brain
emulation
of
like
their
their.
B
I
think
their
interest
is
ultimately
consciousness,
but
their
interest
is,
in
this
whole
brain
emulation
idea
where
you
can
emulate
whole
brains
to
see
if
you
can
build.
You
know
intelligent,
it's
sort
of
like
an
artificial,
general
intelligence
approach.
Why
I
bring
this
up?
Is
that
stephen
larson?
Who
is
one
of
the
co-founders
of
open
worm?
He
actually
talked
at
this
workshop.
He
was
the
first
speaker,
so
this
was
yesterday,
but
it's
on
youtube,
there's
a
youtube
link
in
the
in
this
information
here
and
this
live
stream.
B
If
you
go
to
the
live
stream,
you
don't
have
to
sign
up,
they
talked
about,
but
they
had
a
talk
with
steve
larsen.
I
think
it
was
about
30
to
45
minutes
long,
and
he
talked
a
lot
about
his
motivations
for
starting
open
worm.
His
educational
background
why
he
got
interested
in
what
he
did.
Essentially
it
was
he
was
steve.
B
So
he
went
from
sort
of
an
agi
lab
to
a
lab
where
they
did
about
a
neuroinformatics,
and
then
he
moved
on
to
open
worm
which
was
to
take
you
know,
to
find
a
practical
model
organism
and
to
build
the
anatomy
to
build
what
was
going
on
there
and
then
produce
with
that
something
that
produced
simple
behaviors
and
then
work
from
there
and
see.
You
know
if
we
can
understand
that,
and
we
can
simulate
that.
B
Then
we
get
closer
to
this
sort
of
in
artificial
intelligence.
So
it's
worth
watching
the
whole
talk.
You
know
he's
talking
to
these
people
kind
of
informally,
but
at
the
same
time
there's
this.
You
know
you
get
a
lot
of
good
ideas
of
what's
going
on
there
and
you're,
you
know
able
to
see
kind
of
the
path
that
he
took
and
maybe
how
that's
really
important-
and
you
know
it
gives
you
some
background
as
to
sort
of
what
open
worm
is
doing.
Why
they're
doing
it.
B
B
The
paper
on
the
bacillary
and
on
renal
cognition
as
a
somewhat
hard
deadline,
I'm
trying
to
pull
that
together
into
a
more
formal
draft
in
the
next
couple
weeks.
But
some
of
the
thing
is
that's
number
seven,
but
some
of
the
things
I
can
put
red
on
that
when
I
use
red
it
means
that
I
want
to
like
get
it
to
completion
fairly
soon,
but
other
than
that
we
have.
B
You
know
things
like
the
diva
and
paper,
which
is
this
pre-print,
which
will
probably
wait
till
the
end
of
summer
here
once
we
can
incorporate
minox
work
into
it,
then
we
have
the
boring
billion
in
the
kindle
book,
so
the
boring
billion
is
this
stuff
about.
You
know
embryogenesis
from
way
back
now,
early
early
days
of
embryogenesis
said
even
before
that
the
kindle
book
was,
I
think,
an
idea
that
krishna
had
about
getting
all
the
diva
warm
ml
materials
together.
So
that's
also
outstanding.
B
We
have
this
test
of
williamson
symbiosis,
which
was
this
idea
of
where
it's
it's
a
heavy
sort
of
genomics
bioinformatics
work
where
you're
comparing
genomes
to
see
if
they're
a
thing
you
know
in
organisms
that
there
some
organisms
have
what
we
might
call
multiple
developmental
programs,
and
we
want
to
see
if
they
have
genes
that
enable
these
two
programs
to
coexist
in
the
same
organism.
So
it's
it's.
This
idea
of
testing
some
of
these
ideas,
but
but
looking
at
the
genome
to
see
if
we
can
validate
it
in
some
way.
B
B
B
in
28
we
have
this
quantitative
comparison
of
archaea
and
shaped
droplets.
That's
something
that
is
again
something
that
dick
is
working
on.
We
might
tie
in
the
topo
nets.
Stuff
topo
nuts
happens
in
a
couple
weeks.
So
once
that's
finished
I'll
have
some
feedback
on
that
we
can
go
from
there
in
29.
We
will
put
evolution
book,
and
this
is
steve.
Mcgrew
we'll
put
this
in
the
list,
and
so
I
guess
it's
like
a.
B
Published
book
I
don't
know,
you
know
who
the
publisher
would
be
but
and
then
the
deadline
would
be.
I
guess
tba
so
we'll
put
that
on
the
list
to
see
you
know,
keep
track
tabs
on
it.
Other
than
that.
I
think
the
that's
pretty
much
it.
We
have.
There
isn't
really
anything
to
follow
up
on
right
now
from
this
list,
so
people
have
things
they
want
to
put
on
the
list.
B
B
Let's
see
my
knack
will
work
with
us
me:
stoyan,
smokov
and
kevin
clark,
starting
in
august.
So
that's
number
28.
So
that's
oh
number,
28!
Is
this?
Oh,
so
this
quantitative
comparison
are
key
and
shape
droplets.
Okay,
yeah
I'll,
put
a
note
in
there.
Yeah
I'll
just
make
it
I'll.
Just
put
this
in
the
notes,
section
28.!
B
That
update
okay,
so
that's
that's
all
for
the
submissions
page,
a
reminder
that
we
have
this
network
neuroscience
poster.
That's
due.
I
think
it's
due
wednesday,
so
I've
been
going
through
and
I
haven't
put
it
in
the
slack.
Yet
that's
not
that's
on
my
that's
my
fault,
but
it's
pretty
close
to
being
done.
It's
got
a
lot
of
images
like
I
said
just
they're
not
like
you
know,
specialized
images,
they're,
just
kind
of
like
community
visually
communicating
some
of
the
ideas
here.
B
So
I'll
put
this
in
the
slack,
and
if
people
want
to
take
a
look
at
it
and
see,
if
you
know
they
want
to
change
anything
or
add
anything,
that's
you
know
have
any
better
ideas
for
images
or
whatever.
Let
me
know
I'll,
just
put
it
in
the
slack
channel
and
then
we'll
work
on
it
there,
and
so
I
think
this
is
pretty
much
just
you
know
I
mean
we
have.
B
B
So
that's
what
that
looks
like
okay
and
so
then,
finally,
we'll
get
to
our
papers
here
for
the
week.
So
we
have
a
couple
papers
that
some
of
these
are
newer.
Some
of
these
are
older,
so
this
is
the
first
one.
This
is
a
new
paper,
it's
called
worm
paths,
and
this
is
c
elegans
metabolic
pathway,
annotation
visualization,
and
so
I
don't
know
any
of
these
authors-
I'm
not
familiar
with
them,
but
this
is
something
from
my
system's
biology
perspective.
B
So
this
is
metabolic
pathway
visualization.
So
it's
going
to
be
a
little
bit
intense,
I
think
for
a
lot
of
people,
but
this
that's
okay.
We
need
to
understand
metabolic
pathways.
So
in
our
group
we
aim
to
understand
metabolism
in
the
nematode
c
elegans
and
its
relationships
with
gene
expression
physiology
in
the
response
to
therapeutic
drugs.
B
B
A
lot
of
neurological
disorders
and
or
you
know,
neurological
functions
like
sleep
and
other
things,
and
so
the
way
that
they
can
make
a
comparison
between
worms
and
humans
is
that
their
shared
derived
genes
or
their
paralogs
or
homologs
of
human
genes,
and
so
they
can
understand
it
at
the
genetic
level.
C
elegans
is
easy
to
manipulate
at
the
genetic
level
and
then
see
something
in
the
phenotype.
B
So
but
of
course
the
metabolism
is
somewhat
similar
as
well,
so
you
can
also
make
that
comparison,
but
our
understanding
of
the
metabolism
is
much
less
clear
than
our
understanding
of
c
elegans
genetics.
So
this
is
a
an
important
advance
for
this
reason,
and
many
other
reasons,
visualization
of
the
metabolic
pathways
that
comprom
comprise
the
metabolic
network.
So
there's
a
network
of
metabolic
components
that
drive
and
some
drive
metabolism.
So
metabolism
is
highly
what
we
might
call
regulated
in
a
network
as
opposed
to
something
that
you
just
you
know
it's
like.
B
B
D
B
Nothing
that's
like
specific
to
c
elegans.
It's
just
largely
like
things
that
you
might
find
in
any
standard
organism
and
apparently
c
elegans,
like
most
organisms,
have
specializations
in
that
domain.
So
you
know
we
want
to
understand
those
things.
Many
of
these
pan-organismal
maps
come
with
complete
or
inaccurate
pathway
and
enzyme
annotations.
B
So
this
is
something
actually
stephen
mentions
in
his
talk
is
that
you
know
when
you're
working
at
the
cell
level
and
c
elegans
a
lot
of
the
cell
data
that
we
get,
we
get
a
lot,
we
can
segment
cells
and
understand
the
names
and
generally
the
functions,
but
then
you
know
groups
will
annotate
the
cells
and
they'll
pass
that
information
onto
the
broader
community.
The
problem
is
that
annotations
are
often
quite
incomplete.
B
B
Or
you
know,
verbal
descriptions
are
not
very,
you
know
easily
sort
of
circumscribed
like
you
can
just
use
a
term
and
it's
very
vague
in
general
and
there's
no
real
detail
and
then
annotations.
So
you
know
we
want
to
understand
those
annotations,
or
at
least
the
functions
and
attaching
sort
of
a
functional
label
to
a
cell
more
easily.
B
B
B
We
show
that
worm
paths
provides
easy
to
navigate
maps
and
that
the
different
levels
of
worm
pads
can
be
used
for
metabolic
pathway,
enrichment
analysis
of
transcriptomic
data
in
the
future.
We
envision
further
developing
these
maps
to
be
more
interactive
with
an
analogy
of
road
maps
that
are
available
on
mobile
devices.
B
So
this
is
like
some
you
know
like
if
you
pull
up
a
map
on
your
phone,
what
that
looks
like
so
that's
that's
their
vision,
but
they
really
gonna
go
into
so
a
lot
of
these
metabolic
maps.
They're
really
kind
of
you
know,
they're
pretty
complex.
If
you
look
at
them,
they
look
like
this
massive
subway
network
and
it's
a
you
know
it's
kind
of
a
mess
to
understand.
What's
going
on,
you
can
follow
the
pathways
through,
but
then
you
know
what
is
going
on
in
in
that
map.
B
So
it's
very
important
to
sort
of
visualize
it
in
a
an
effective
way
and
understand.
What's
going
on
so
metabolic
reactions
function
in
metabolic
pathways
that
are
interconnected
to
form
the
metabolic
network.
In
these
networks
the
nodes
are
metabolites
which
are
the
little
dots
and
the
edges
which
are
the
lines
between
them
are
conversion
and
transport
reactions
carried
out
by
metabolic
enzymes
and
transporters.
B
So
that's
the
way
the
the
metabolic
networks
are
structured
genome
scale.
Metabolic
network
models,
which
are
things
that
are
controlling
gene
expression,
provide
mathematical
tools
that
are
invaluable
for
the
systems,
level,
analysis
and
metabolism.
Some
such
models
have
been
constructed
for
numerous
organisms,
including
bacteria,
yeast
and
c
elegans
and
humans.
So
they've
got
these
different
models
for
different
organisms,
but
they're
improving
upon
the
c
elegans
model
and
all
these
bacteria
yeast.
You
know
humans,
obviously
is
important
for
human
health.
Yeast
is
actually
a
common
model
for
looking
at
animal
or
metazoan
origins
of
things.
B
So
yeast
is
actually
quite
an
important
model
organism,
and
then
bacteria
is
easy
to
understand.
Has
a
very
small
genome
relative
to
you
know
I
mean
there
are
only
a
couple
hundred
genes
in
a
lot
of
bacterial
genomes,
so
they
have
this.
You
know
simple
model
that
you
can
understand
and
it's
usually
a
single
cell.
So
those
are
you
know
that
sort
of
low
hanging
fruit
for
these
kind
of
networks,
metabolic
network
models
are
extremely
useful
because
they
can
be
used
with
using.
B
They
can
be
analyzed
using
flux,
balance
analysis,
which
is
a
specific
type
of
a
mathematical
analysis.
They
use
to
show
the
flow
of
things
through
the
network,
so
you
you
evaluate
each
node
and
you
look
at
the
things
that
are
moving
between
the
nodes
and
then
there's
this
formal
and
that
mathematical
analysis
you
can
do
to
get
a
sense
of
you
know,
movement
in
the
network,
so
this
allows
us
to
derive
specific
insights
and
hypotheses.
B
So
you
can
see
the
pathway
here,
and
this
is
what
the
pathway
looks
like
you
have
all
these
different
metabolites
and
then
you
can
see
that
it's,
it's
almost
like
a
tree
more
than
a
a
really
dense
network.
But
when
you
think
about
this
is
just
one
of
maybe
60
or
70
different
things
you
can
plug
into
this
network,
so
it
actually
ends
up.
Looking
very
you
know
busy.
C
B
B
This
shows
like
some
of
these
pathways,
which
we
just
saw
so
that
you
know
you
have
a
single
pathway
and
it's
connected
to
another
pathway,
and
so
you
have
like
you
can
model
it
in
terms
of
different
parts
of
the
cell,
like
mitochondria
exterior
cellular
space.
That's
what's
going
on
the
cytosol,
you
can
partition
the
networks
in
that
way
and
then
any
good
visualization.
B
You
have
to
have
a
legend,
so
this
is
these:
are
your
restrictions
or
your
reactions
here,
different
colors
for
different
types
of
reactions
and
and
movement
between
these
nodes
and
then
yeah?
So
this
is
how
they're
visualizing
this,
and
then
they
have
this
figure
here,
where
they're
comparing
sort
of
worm
pads,
which
is
curated
metabolism
genes
with
worm
cat,
which
is
going
on
at
the
genome
scale
and
so
they're.
Making
this
comparison
between
these
two
models.
B
The
next
paper
is
this
paper
on
identification
of
neural
progenitor
cells
in
their
progeny
reveals
long-distance
migration
in
the
developing
octopus
brain,
so
we're
going
from
c
elegans
to
octopus
and
we're
moving
from
metabolism.
Up
to
these,
you
know
differentiating
cells
and
the
progenitor
cells,
which
are
not
stem
cells
necessarily
but
they're
the
progenitors
to
what
will
become
neural
cells.
So
I
guess
there's
they
would
be
classified
as
pluripotent
cells.
That
are,
you
know,
early
forms
of
a
neural
cell.
B
So
this
is
an
octopus,
and
so
octopus
has
a
fairly
large
brain
and
it's
a
very
intelligent
animal
and
there's
been
a
lot
of
behavioral
studies
on.
You
know
how
intelligent
octopus
are,
and
so
people
are
working
on
the
octopus
brain
and
it's
you
know
it's
it's
probably
far
different
from
a
human
brain
in
a
lot
of
ways.
So
it's
a
cephalopod
brain.
B
There
have
been
some
people
have
like
explored
that,
but
that's
that's
a
story
for
another
time,
so
how
the
cephalopod
brain
develops
has
only
been
described
at
the
morphological
level
and
it
remains
unclear
where
the
progenitor
cells
are
located
and
what
molecular
factors
drove
neurogenesis
using
histological
techniques.
We
located
dividing
cells,
neuroprogenitors
and
post
mitotic,
neurons
and
octopus
vulgaris
embryos,
so
they're
looking
in
the
embryo
they're
looking
at
these
progenitor
cells,
and
they
want
to
see
what
they're
doing
in
the
brain
as
in
during
development.
B
Our
results
indicate
that
progenitors
are
located
outside
the
central
ring
cords
and
the
lateral
ellipse
adjacent
to
the
eyes,
which
is
a
different.
A
different
anatomy,
of
course,
than
the
mammalian
brain,
suggesting
that
newly
formed
neurons
migrate
into
the
cords
lineage
tracing
experiments
ensure
that
progenitors,
depending
on
their
location
in
the
lateral
ellipse,
which
is
a
anatomical
landmark
in
the
in
the
that
we
don't
really
have
an
analog
for
in
mammalian
brains,
generate
neurons
for
the
different
lobes.
D
B
So
we're
making
an
analogy
between
cephalopod
brain,
specifically,
octopus
and
mammalian
brains
and
we're
making
the
statement
that
there's
there
are
similar
processes
going
on
in
these
brains
in
terms
of
development
and
in
developmental
migration
of
cells
and
differentiation
of
cells,
they're,
making
all
sorts
of
different
structures.
They
have
a
different
developmental
trajectory,
but
you
know
they
have
the
same
type
of
cellular
behaviors.
B
So
this
is
so.
Cephalopods
are
invertebrates,
so
they
have
a
very
different
origin
than
mammalian
brains.
Not
that
different.
I
mean
they
come
from
the
same
common
ancestor
at
some
point,
but
the
trajectory
of
their
brain
development
is
different
and
the
actual
brains
look
different.
So
let
me
see
if
I
can
find
an
image.
Actually
let
me
go
through
this
part
here.
Where
we
talk
about
the
they
talk
about
the
adult,
cephalopod,
mollusk,
octopus
vulgaris.
B
It
has
a
highly
centralized
brain
containing
of
about
200
million
nerve
cells
and
the
supra
and
subseophagia
mass
and
two
optic
lobes.
Yet
the
cellular
molecular
mechanisms
driving
brain
development
remain
poorly
understood,
so
at
hatching,
which
is
when
they
are
born.
Essentially,
the
oval
gyros
brain
counts
about
200
000
cells
and
occupies
roughly
one-fourth
of
the
total
body.
So
I
guess
this
is
a
body
mass
indicating
extensive
embryonic
neurogenesis
in
general.
Neuroprogenitor
cells
are
generated
from
ectodermal
cells
and
divides
symmetrically
and
asymmetrically
to
generate
all
neurons
of
the
nervous
system.
B
So
this
ectodermal
cell
is
like
part
of
the
three
germ
layers
that
you
would
see,
and
then
this
is
the
germ
layer
which
all
these
cells
arise.
They
divide
in
these
different
ways
and
they
generate
neurons
and
then,
let's
see,
enclaves,
harboring
species
with
diffuse
nerve
nets,
so
in
in
cephalopod
brains,
they
use
something
called
a
nerve
net,
which
is
a
little
bit
different
than
what
we
have
in
human
brain
and
the
human
nervous
system.
B
They
have
this
sort
of
it's
like
a
network,
it's
it's
a
sort
of
a
network
of
nerves
and
neurons
and,
of
course,
the
central
brain,
but
they
have
this.
You
know
where
different
parts
of
the
body
are
taking
in
information,
so
it's
it's
much
different
than
in
a
good
model,
for
this
is
the
hydra
which
we
haven't
talked
about,
but
I've
I've
seen
people
talk
about
this,
where
the
hydra
has
like
this
nervous
system.
That
is,
you
know
much
different
than
the
way
it
behaves
in
terms
of
like
taking
in
sensory.
B
Information
is
much
different
than
the
mammalian
brain,
so
so
in
these
and
clades
are
like
taxonomic
groups.
So
these
nerve
nuts,
which
are
diffuse
the
proliferating,
neural
progenitor
cells,
are
distributed
throughout
the
ectoderm
generating
local
neurons.
So
in
this
case,
instead
of
having
like
in
a
mammalian
brain
where
you
have
nerves,
nerve,
endings,
and
then
you
have
a
central
nervous
system
and
a
spinal
cord.
B
You
have
these
neurons
that
emerge
throughout
the
body
and
they
generate
local
neurons,
and
then
they
have
this.
Some
phyllo
have
a
centralized
nervous
system,
including
vertebrates
arthropods
and
some
analogues.
The
neuroprogenitor
cells
are
grouped
in
the
neurectoderm,
so
there's
a
difference
in
terms
of
the
developmental
trajectory
of
these
nervous
systems
and
where
these
cells
emerge
in
development.
So
in.
D
B
But
that's
not
the
typic,
that's
not
typical
of
other
types
of
non-mammalian
brains.
You
also
see
short-range
migratory
events
in
the
drosophila
visual
system,
so
this
is
something
that
we've
seen
in
vertebrates.
It's
long-distance
migration
and
now
we're
also
seeing
it
in
a
cephalopod
nervous
system
as
well.
B
B
B
B
B
Okay,
so
formation
of
the
brain-
this
is
an
in
situ
hybridization
of
the
different
tissues
in
the
in
this
they're
doing
this
embryo
they're,
looking
at
different
sections
of
the
embryo
and
so
they're,
showing
gene
expression,
I
think
over
different
stages
here.
So
these
are
different
developmental
stages,
where
you
start
to
get
the
formation
of
a
brain.
B
You
get
this
outer
layer
here
and
then
you
get
formation
along
the
edges,
and
then
you
start
to
get
these
different
parts
of
the
brain
that
come
into.
They
start
to
get
more
and
more
defined
over
developmental
time
and
you
can
see
different
stages
and
you
can
see
the
structures
being
defined
so
yeah.
So
this
is.
This
is
a
long
again.
This
is
another
very
involved
paper
and
you'd
have
to
really
examine
it
to
get
a
good
sense
of.
What's
going
on,
I
thought
I
would
present
it.
B
So
this
is
actually
trajectory
mapping
where
they're
looking
at
migrating
cells.
So
this
is
where
they
show
they
inject
this
tracer
into
the
brain
into
these
different
cells
and
they're
able
to
watch
how
they
migrate.
So
you
can
see
that
there's
this
migration
pattern
for
this
cell
and
they
migrate.
B
B
B
And
then
yeah
this.
This
shows
like
the
gene
expression
of
different
things
going
on
in
different
species
in
the
cephalopod
clade,
as
they
say
so.
This
is
this:
is
the
ogre
vertebra?
It's
a
cephalopoda
annalids
arthropods
nidarias.
This
is
actually
a
bigger
quaid.
This
is
a
a
clade
that
includes
cnidarian
vertebrates,
so
they
actually
show
the
differential
gene
expression
here.
These
are
genes
that
are
developmental
genes
that
you
would
see
very
generally
speaking,
control
neurogenesis.
B
So
these
things
that
are
expressed
in
these
different
categories
and
you
can
see
there's
variation
across
different
brains.
So
this
is
for
these
are
vertebrate
brains,
for
example,
encephalopod
marines.
They
actually
have
somewhat
similar
gene
expression
patterns,
especially
compared
to
cnidarians
and
arthropods,
but
you
know
they
just
show
that
that
variation
they're
not
saying
anything
more
about
it.
B
B
Let
me
share
my
screen:
we'll
go
through
the
comments.
This
is.
I
put
this
link
to
the
submissions
doc.
Okay,
so
susan
says
octopi
are
smart.
Yes,
they're,
smart
sociopaths,
yeah,
they've,
they've,
taken
pictures
of
octopi
like
in
an
aquarium
at
night,
they've
had
like
cameras
on
the
tank
and
they've,
seen
them
get
out
of
their
tanks
and
walk
around
the
aquarium
and
do
things
like
eat
other
fish
and
things
like
that
so
yeah
it's!
B
B
B
Why
do
we
have
big
greens
and
you
might
say
well
we're
long-lived
we're
actually
not
as
long-lived
to
say,
like
you
know,
whales,
for
example,
but
you
know
they
have
big
brains
too,
but
you
know
that's,
that's,
maybe
not
the
best
explanatory
framework,
because
you
know
octopi
have
pretty
big
brains
and
they
don't
live
that
long.
So
so
there
are
a
lot
of
things
again.
I
just
wanted
to
present
it
to
show
a
lot
of
the
diversity
of
brains
and
especially
in
development,
so
well
all
right.
So
I
think
that's
it.
C
B
B
Can't
hide
from
the
octopus
dick
said
thanks
so
yeah
thanks
for
attending
and
next
week.
If
you
have
anything
to
present,
you
know
we
have
time
during
the
meeting
my
knock
again,
we
look
forward
to
your
next
report
on
your
project
and,
if
you
need
anything
during
the
week,
let
me
know,
on
slack,
have
a
great
week
talk
to
you
later.