►
Description
Attendees: Bradly Alicea, Mayukh Deb, Mainak Deb, Ujjwal Singh, and Richard Gordon.
A
A
A
B
A
B
B
B
D
E
B
B
C
B
C
B
Yeah,
that
sounds
good,
so
I
don't
think
anyone
else
is
going
to
show
up.
I
had
a
couple
well,
I
have
a
couple
things
for
today.
I
guess
I
was
gonna
talk
about
open
projects,
it's
hoping
a
couple
more
people
would
show
up,
but
that's
okay.
We
can
do
it
a
recording
on
on
the
internet,
and
you
know
people
will
see
it.
I
just
wanted
to
go
over
what
was
open.
B
B
B
So
this
is
the
follow-up
on
diva
proposed
projects.
We
have
a
couple
of
projects
that
I
listed
there,
probably
more,
but
these
are
the
ones
that
I
kind
of
came
up
at
the
top
of
my
head.
That
we've
talked
about
maybe
recently
or
over
the
summer
and
just
kind
of
like
doing
like
you
know
getting
the
stage
where
we
talk
about
it
and
then
we
maybe
put
a
little
bit
of
you
know
time
into
it,
and
then
it's
still
open.
B
So
I
mean
you
know
one
way
to
make
sure
you
complete
projects
is
to
keep
putting
them.
You
know
in
your
face
and
making
reminding
yourself
that
they're
there
and
so
that's
a
good
strategy.
So
that's
what
we're
gonna
do
today.
We're
gonna
talk
a
little
bit
about
each
of
these,
and
maybe
you
know
just
keep
reminding
us
that
they're
there.
So,
let's
see
I
have
six
that
I've
counted
and
they're
more.
I
think,
but
I
don't
know,
I'd
have
to
go
back
and
look
at
our
conversations
from
the
summer
to
remember
so.
B
The
first
one
is
the
lagrangian
embryo,
which
we
talked
about
maybe
like
two
weeks
ago,
and
then
I
have
this
column
called
applications,
which
means
that
we
have
some
other
initiative
that
were
that
we've
sort
of
it
follows
up
on
a
theme
that
we've-
maybe
I
should
have
put
themes
in
there,
but
it
follows
up
in
a
theme
that
we've
discussed
in
the
group
so
there's
this
complexity.
Measures
theme
that
the
lagrangian
embryo
fits
into,
and
there
are
probably
other
themes
as
well.
B
Then
we
have
bacillaria
psychophysics
and
collective
pattern,
generators
which
are
both
in
the
basilaria
theme
periodicity
of
the
embryo,
which
is
complexity
measures,
and
this
is
the
one
I
mentioned,
axolotl
virtual
embryo.
This
is
more
of
a
modeling
theme,
which
is
the
thing
that
I've
been
talking
about
with
susan,
and
we
talked
about
a
couple
times
in
the
meeting.
We've
shown
the
data
a
little
bit
and
then
evo
learn
build
out,
which,
of
course,
we
know
what
that
is.
B
We've
been
successfully
complete,
we've
successfully
completed
sort
of
the
built.
You
know
the
built
up
of
diva
learn
the
repository
the
organization-
everything
like
that,
but
now
we're
built.
We
want
to
build
it
out.
We
want,
to
maybe
add
some
more
features
and
actually
get
people
to
use
it,
and
so
that's
you
know,
that's
its
own
sort
of
project.
B
B
So
I
I
think
we
had
another
message
in
the
chat
and
it
was
rojoella
said
I
have
started
working
on
the
axolotl
3d
image
formation.
Maybe
I
can
talk
about
my
progress
in
coming
weeks.
Yeah.
That
would
be
great
just
you
know
we
can
talk
about
it
in
slack.
We
can
preview
it
and
then
we
can
put
it
on
the
schedule
for
one
of
the
coming
meetings.
E
B
B
B
So
we
can
look
at
things
like
divergence
of
spatio-temporal
flows,
fluid
flows
and
then
meso
patterns,
which
is
something
that
we've
talked
about
in
the
divo.
Learn
software
in
terms
of
meta
features
so
being
able
to
extract
those
features,
would
help
in
terms
of
not
just
the
cell
tracking
aspect,
but
the
extraction
of
meta
features
would
help
in
this
initiative,
and
this
these
are
just
some
diagrams.
I
drew
of
like
some
flows,
and
so
that's
that's
one
project,
that's
on
sort
of
just
kind
of
starting
out.
B
B
B
E
B
B
So
psychophysics
is
a
form
of
looking
at
like
how
they've
done
a
lot
of
tests
in
humans,
but
also
other
animals
and
nervous
systems
looking
at
how
they
can
perceive
things
like
differences
in
the
number
of
items
in
a
spatial
array.
So
in
this
case
you
have
what
they
call
just
noticeable
difference.
If
I
give
you
this
array
of
10
dots
versus
20
dots,
can
you
detect
the
difference
between
the
two
if
I
flash
them
quickly
in
front
of
you?
If
I
I
show
you
this
array
versus
this
array?
B
Well,
it's
easy
to
see
that
there
are
more
dots
in
this
array
than
this
array.
However,
if
you
look
at
this
array
versus
this
array,
110
to
120.,
the
order
of
magnitude
is
less.
The
increase
between
110
120
is
less
than
10
to
20,
because
10
to
20
is
a
doubling
and
110
to
120
is
not
you
know
it's
just
a
little
bit
more,
and
so
I
can
show
you
these
two
arrays
and
it's
much
harder
for
you
to
detect
the
difference.
B
If
I
show
it
to
you,
maybe
in
a
couple
seconds,
and
so
those
are
types
of
things
that
you
look
at
in
psychophysics
as
their
you
know,
physical
attributes
to
the
stimulus
and
then
there's
some
characteristic
response,
and
so
this
may
seem
very
far
removed
from
basil
area.
But
there
are
things
we
can
do
with
look.
You
know
using
some
of
these
measurement
techniques
to
understand.
B
Maybe
what's
going
on
with
the
movement
of
the
colony
and
the
coordination
of
the
colony
and
so
forth,
and
then
the
question
is:
is
do
these
behaviors
represent
autonomous
intelligence,
or
does
it
simply
mimic
intelligent
behavior?
And
so
that
would
be
another
thing
like
to
discuss
in
the
paper
which
would
be
you
know.
Does
this
represent
like
something
like
a
brain,
or
is
it
something
that
just
mimics
what
a
brain
does,
and
it's
just
kind
of
this
you
know
you
can
think
of
like
a
pendulum
being
something
that
mimics
say:
human
walking
you.
B
B
E
D
B
B
C
C
C
B
B
You
know
we'd
like
to
have
more
data
generated
and
as
for
like
formal
experiments,
I'm
not
really
sure.
If
that's
what
we
can
get
not
really
sure
if
you
can
do
a
like
an
experimental
intervention,
that's
suitable
for
what
we
want,
maybe
just
like
recordings
of
behavior,
maybe
more
data
in
terms
of
the
variety
of
behaviors
or
you
know,
just
more
data
in
general
will
give
us
some
indication
because.
D
B
B
And
so
yeah,
this
is
this
paper
that
thomas
harvick
is
preparing
for
this.
This
is
the
same
book
chapter
that
the
machine
learning
paper
was
going
into,
but
this
is
this.
I
don't
know
if
we
have
access
to
this
or
not
so
we
can
maybe
get
a
handle,
get
access
to
this
paper
and
look
at
the
data
that
are
that
he's
collected
so
far
and
he'll
collect
more
data
because
he's
been
culturing
his
okay.
We
can
ask
thomas
directly.
B
B
B
B
We
have
this
like.
What's
the
measurement
techniques
right
now,
this
is
just
an
outline,
but
you
know
as
we
go
through,
we
can
so
the
idea
would
be
that
we
would
hypothesize
that
the
bacillary
has
this
internal
model
with
inputs,
outputs
processing
units.
You
know
within
the
colony
and
interactions
between
the
different
cells,
and
so
then
certain
behaviors
should
conform
to
a
statistical
regularity,
some
sort
of
output
function
that
we
can
characterize
and
that's
basic
and
then
we'd
use
one
of
these
methods.
B
We
could
look
at
like
the
detection
of
stimulus
or
we
could
even
look
at
like
its
own
movement,
and
that's
that's,
I
think,
the
place
where
you
know
maybe
there's
a
little
bit
of
people
would
throw
up
a
challenge
and
say
well,
you're,
not
really
looking
at
stemily
directly,
and
so
you
could
say
well,
you
know
we
there.
You
know
we're
there
ways.
You
know
we
can
just
measure
the
output
data
and
say
that
this
behavior
is.
You
know
that
there's
some
regularity
in
behavior
or
something
that's
that's.
B
B
B
C
E
B
B
B
D
B
B
Require
a
little
bit
more
simulation
work,
so
this
is
the
repo
for
this.
This
is
the
same
repo
I
didn't
put
the
dot
in
here,
but
it's
the
same
place.
It's
just
a
different
folder,
and
this
is
so.
This
is
a
cell
colony
again
a
non-neuronal
system
as
an
information
processing
entity,
and
this
is
an
example
of
a
collected
or
a
central
pattern.
B
Which
is
where
you
have
an
organism
that
has
like
a
set
of
excitable
cells
or
neurons,
and
they
generate
this
movement
and
like
this
tail
flicking
behavior
and
it's
rhythmic
in
the
sense
that
it
has
this
regular
irregularity
to
it.
So
in
these
I
think
this
is
a
tadpole
and
then
in
the
in
the
adult
or
later
in
development.
They
have
this
hind
limb,
central
pattern,
generator
that's
synchronized
across
muscles,
and
so
this
is
the
way
you
get
a
lot
of
movements
in
a
wide
range
of
organisms.
B
C
elegans
has
central
pattern:
generators
for
generating
their
movements.
It's
just
a
collection.
It's
just
like
a
small
network
of
neurons
that
generate
something.
That's
a
regular
pulse,
your
heart
rate
or
your
heartbeat
is
actually
managed
by
a
central,
a
set
of
central
pattern
generators
so
that
the
the
rhythm
is
always
there.
It's
not
lost,
and
it's
not
something
that
happens
as
an
emergent
phenomenon.
B
So
that's
the
idea
of
a
central
pattern
generator,
but
a
collective
pattern.
Generator,
then,
is
something
that
happens
amongst
a
bunch
of
cells
that,
if
you're
in
a
nervous
system,
but
they
still
generate
this
rhythmic
pattern
and
that's
the
sort
of
thing
basically,
the
question
is:
does
basilary
demonstrate
a
non-neuronal
analog
to
this
central
pattern?
B
Generator,
and
so
actually
this
should
be
copgs,
but
the
the
yeah
so
cpgs
are
central
pattern,
generators,
collective
pattern,
generators
or
copgs,
and
so
that's
the
the
idea
behind
that,
and
so
the
question
is:
is
that
do
copgs
as
something
that's?
Not
a
cpg
but
based
loosely
on
that
same
analogy,
reflect
adaptive
control
or
are
they
simply
an
output
of
the
behavior?
B
B
B
Okay,
oh
and
then
this
so
this
is
the
document,
and
this
is
actually
even
in
less
complete
shape
than
the
other
one.
This
is
an
outline
mainly
so
central
pattern.
Generators
generate
oscillations
from
a
small
neural
circuit,
but
in
these
in
a
vessel
where
we
observe
something,
we're
proposing
is
called
collective
pattern.
Generators
which
are
generated
from
collective
and
coordinated
behavior,
the
central
nervous
system
of
brain
is
involved,
and
then
this
kind
of
gives
you
this
a
neural
architecture.
B
B
I
have
data
on
cpgs
from
a
stick
insect.
These
are
what
they
call
synergistic
cpgs,
and
so
this
paper
has
a
data
set,
that's
open
that
we
can
analyze
and
you
know
maybe
compare
with
the
simulation
of
basil
area
and
then
maybe
there's
some
analysis.
We
can
use
discussion,
and
so
that's
that's.
That
idea.
B
D
B
B
Zebra
fish
embryo.
I
think
I
showed
you
a
three-dimensional
cell
tracking
reconstruction,
where
it
had
kind
of
like
the
cells
on
the
edge,
and
then
there
was
like
this
gap,
so
it
kind
of
looked
like
a
crescent
or
an
open,
ended
sphere,
and
so
that
comes
from
this
body
at
the
bottom.
So
these
cells
butt
at
the
top
of
the
at
the
embryo,
and
they
sort
of
this
is
where
all
the
cells
are,
and
so
it's
a
little
bit
different
than
c
elegans.
C
elegans.
Is
that
one?
B
B
B
That
occur,
we
see
this
in
c
elegans
as
well,
that
they
don't
it's
not.
You
know
uniform
with
respect
to
time.
Like
not,
you
know,
you
don't
get
the
same
number
of
cells
every
minute
you
get
these
these.
You
know
waves
of
cell
division
and
it
breaks
down
a
little
bit
out
here,
but
what
you
can
do
is
you
can
bin
these
division
events
and
you
can
look
at
like
these
little.
B
You
know
you
can
look
at
the
local
maximum
of
the
number
of
division
events
over
time,
and
so
that's
how
we
did
this
graph
here,
where
you
have
that
you
take
all
these
data
where
you
have
these
different,
you
know
peaks
in
cell
division
and
then
you
look
at
the
periodicity
between
the
peaks
so
between
this
peak
and
this
peak
represents
maybe
about
maybe
15
minutes.
I
don't
know,
but
this
is
what
you
end
up
with
you
end
up
with
these
graphs,
where
there
are.
B
These
peaks-
and
so
you
can
see
there
are
a
lot
of
peaks
in
the
lower
end
of
the
spectrum,
and
that
represents
a
lot
of
these
this
area
here
and
then
you
have
a
couple
out
here,
which
are,
you
know,
16
to
22
minutes,
and
that's
these
these
areas
over
here.
So
it's
the
sort
of
frequency
domain
analysis.
B
B
Somewhat
crude,
but
it
actually
gets
at
the
structure
a
bit,
and
so
the
idea
would
be
in
in
this
paper
would
be
to
explore
some
of
these
trends
for
different
species,
and
so
we
have
zebrafish
data.
We
have
c
elegans
data
and
that
that's
a
good
comparison
just
if
you
want
to
do
like
a
cross
species
comparison,
but
maybe
there
are
other
well,
maybe
just
leave
it
at
zebra
fish
to
see
elegance.
B
B
A
B
B
B
B
B
Yeah
legion
was
sick,
yeah
yeah,
one
of
the
collaborators
was
sick,
and
so
this
well,
you
know
we'll
try
to
get
this
into
better
shape
over
again,
this
is
going
to
be.
You
know,
we're
just
going
to
have
to
keep
revisiting
it
and
tightening
it
up
and
figuring
out
like
what
we
have
in
terms
of
an
argument
in
terms
of
like
the
comparisons,
the
comparative
for
some
of
you
who
have
not
done
any
comparative
work
before
this
is.
D
B
Interesting
area
where
you
can
take
two
organisms
that
are,
you
know
quite
different
in
their
say,
development
or
in
other
ways,
and
you
can
find
commonalities
between
them,
and
you
know
with
with
this
ability
to
find
you
know
this
ability
to
do
cell
tracking
and
things
like
that.
It
makes
it
much
easier
to
do
because
you
know
if
you
were
just
doing
this
by
I
or
some
other
method.
It
would
be
very
hard
to
get
to
find.
B
B
I
think
I
did
have
a
folder
here,
one
time
I
put
in
the
url
for
the
call
for
papers.
Okay,
let's
see
all
right,
there's
the
call
for
papers
so
well,
anyways,
there's
a
folder
there's
some
document
that
I
I
created
that
describes
sort
of
the
rules
that
people
have
said
they
might
want
to
play.
So
I
think
it
might
be
in
here.
No,
it
isn't.
Okay,
so
I'll!
B
B
B
Where
are
we
well,
as
an
organism
starts
from
the
one
cell
zygote
through
cell
division,
embryogenesis
and
maturity?
Well,
that's
what
we
just
saw
while
the
bottom-up
approach
of
a
molecular
developmental
biology
is
yielded
a
richness
of
biochemical
mechanisms.
It
has
not
shown
how
these
are
organized
in
space
and
time.
B
D
B
In
the
fertilization
process
involves
the
interaction
of
the
spermatozoon
with
the
egg
cortex,
resulting
in
various
waves
that
propagate
along
the
cortex
depending
on
the
species.
The
activated
spermatozoan
is
the
ability
to
fertilize
the
oocytes
and
then
indicating
that
the
fertilizable
conditions
are
correlated
with
the
structural
organization
of
the
egg
quartex
at
the
time
of
fertilization.
B
B
Now
we
have
one
paper
here
already:
french
flat
gradients
and
turning
reaction,
diffusion
versus
differentiation,
noises
models
of
morphogenesis,
so
that's
scheduled
for
october,
and
then
there
will
be
more
papers
in
this
issue
as
they
become
submitted
and
available.
So
so
yeah.
We
would
submit
to
this
and
then
we'd
get
some
peer
review
feedback
and
then
we
would
you
know.
B
Hopefully
this
becomes
a
paper
in
this
issue,
and
so
I
mean
that's
that's
I
read
all
that
just
to
make
people
aware
of
kind
of
where
this
is
headed
and
what's
what's
involved
in
this,
and
I
think
like
in
our
computational
approach.
I
think
it's
very
valuable,
like
I
said
you
know,
with
cell
tracking
and
especially
with
the
ability
to
analyze
cell
tracking
data,
it
opens
up
a
lot
of
avenues
for
comparative
stuff.
B
Then
the
axolotl
digital
modeling,
so
this
is
the
thing
that
we've
talked
about
with
susan
a
couple
times
and
for
those
of
you
who
haven't
seen
the
data,
this
is
actually,
if
you
recall,
we
talked
about
this
in
some
of
the
meetings.
This
is
where
we
have
this
microscope
that
takes
these
axolotl
embryos.
So
this
is
an
axolotl
down
here
and
it
takes
the
egg
and
it
flip
it
basically
pulls
it
down,
and
then
it
releases
the
egg
and
it
flips.
B
B
So
you
start
with
this
image
and
then
it
moves.
These
are
the
same
cells.
You
can
kind
of
make
them
out
as
they're
moving
across
the
surface.
This
is
just
the
rotation,
it's
kind
of
like
looking
at
a
a
basketball
or
a
soccer
ball.
As
it's
rotating
you
can
see
like
the
markings
on
it
move.
You
know
as
it's
moving
if
you
took
a
bunch
of
still
shots
of
the
movement
and
you
would
see
that
they
change
position
now.
The
idea
here
is
to
take
this
and
to
project
it
onto
something
like
a
sphere.
B
So
there's
some
map
projection
algorithm
that
needs
to
be
applied
to
these
data.
So
we
can
take
the
image,
and
this
is
a
a
square
image
as
a
raw
image,
and
then
we
can
segment
it
using
a
a
circular
mask
to
pull
that
out
of
the
background
and
it
just
kind
of
makes
sure
that
they're
normalized
so
they're,
mostly
when
you
do
the
actually,
you
can
do
the
segmentation
by
hand
and
just
by
looking
at
the
sort
of
the
edge,
and
you
can
see
that
you
mostly
get
something
that's
equivalent
to
crossed
images.
B
So
it's
about
the
same
size.
So
there
might
be
an
automated
way
to
do
that.
That
would
make
it
a
little
bit
more
efficient,
but
you
do
end
up
with
this
nice
spherical
shape.
You
know,
so
you
don't
have
a
lot
of
post
processing
there.
The
challenge
is
to
project
it
to
some
sort
of
three-dimensional
model
using
a
map
projection
algorithm.
B
So
I
understand
that
usual
said
that
he
was
doing
some
work
on
this,
because
I
sent
that
I
put
the
data
some
sample
data
in
a
folder,
and
I
wanted
my
oak
and
usual
to
take
a
look
and
as
well
said
he
had
some
ideas
about
it
or
I
don't
know.
If
that's
something
you
want
to
follow
up
on
now
I
mean
we
can
talk
about
in
the
coming
weeks.
C
B
D
B
D
B
Yeah,
so
I
mean
this
is
the
idea
here
is
to
get
a
full
view
of
the
embryo
for
all
sides
of
it.
So
like
a
lot
of
the
the
c
elegans
stuff,
you
know
they
take
an
embryo
and
they
put
it
on
a
slide.
And
it's
like
one
like
you
can
see
through
the
embryo.
You
can
do
like
a
series
of
a
stack
of
images,
so
you
can
do
slices
through
the
embryo
through
by
you
know,
changing
the
focal
plane
not
really
because
they're
visually
opaque,
okay
yeah.
D
B
B
There
have
been
a
couple
people
who've
inquired
about
contributing,
but
we'd
really
like
people
to
use
it
for
analysis
and
for
other
things,
and
so
again
this
is,
you
know,
a
series
of
models.
So
we
have
this
divalern
organization
on
github
within
that
we
have
living
the
divalern
program.
So
it's
like
a
pre-trained
model.
B
B
B
You
know
we
might
have
some
3d
digital
modeling
involved
and
we
could
have
that
as
another
part
of
this.
We
also
have
the
education
component,
which
is
the
uvorm
open,
worm,
curriculum
or
things
that
people
we
have
data
science
tutorials
that
people
have
made.
So
those
are
all
things
that
you
know.
We
can
point
people
to
and
say:
look
you
know,
we've
got
all
these
nice
tools
that
you
know.
If
you
want
to
learn
about
this
topic,
it's
like
the
one
stop
shop.
B
You
have
all
these
different
things
that
you
might
need
to
learn
about.
That
can
demonstrate
an
analysis
and
then
or
you
can
just
use
it
as
an
analysis
in
its
own
right.
If
you
want
to
analyze
some
data
and
you
already
kind
of
know
what
you're
doing,
then
you
can
just
do
the
analysis.
So
it's
really.
B
I
think
it's
going
to
be
like
a
nice
component
of
all
this
yeah,
so
I
mean
the
the
only
thing
is
you
know
we
just
have
to
find
people
to
contribute,
maybe
like
as
users
or
not
just
as
like
people
who
make
things,
but
also
as
users
and
we'll
see
how
that
goes
so
yeah,
that's
that's
pretty
much
it.
I
know
I'm
probably
forgetting
a
lot.
Maybe
a
couple
things
on
here.
There
are
things
under
like
say:
divo,
learn
that
are
sort
of
maybe
deserve
their
own
line,
but
we
don't.
D
B
B
B
B
I
don't
know
if
it
was
stasis,
but
definitely
like
compared
to
what's
happened
since
that
explosion?
What
you
know
why?
Why
was
there
that
huge
length
of
time-
and
so
that's
I
mean
that's
something
that
isn't
directly
related
to
what
we're
kind
of
doing
in
this
group?
But
it's
you
know
it's
something
that
you
might
be
interested
in
yeah
well,
this
is
already
sounds
interesting.
B
B
So
yes,
so
for
a
long
time,
life
existed
as
these
one
celled
eukaryotes
we
had
eukaryotes
and
prokaryotes,
but
then
you
don't
have
multicellularity
until
much
later
and
then
that's
kind
of
the
question
and
there
have
been
a
lot
of
people-
have
explored
sort
of
multicellular
transitions
or
they
call
them
the
major
transitions
in
evolution.
But
you
know
that's,
you
know
there
are
a
lot
there's
a
lot
to
explore
there.
So
it's
that's.
Basically
what
you're
related
to.