►
From YouTube: DevoWorm (2020, Meeting 29): GSoC updates, Chaos and Diffusion-limited Aggregation, DevoLearn
Description
Attendees: Bradly Alicea, Mayukh Deb, Ujjwal Singh, Susan Crawford-Young, Jesse Parent, and Krishna Katyal.
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A
Google,
okay,
renee,
says
he'll
be
limited
to
texting
only
for
today.
So
thank
you.
So
the
first
thing
I
wanted
to
talk
about
was
we
have
a
couple
things
I
wanted
to
get
to
google
summer
of
code
stuff
in
a
minute,
but
I
wanted
to
cover
some
other
things.
First,
I'm
going
to
switch
the
order
a
little
bit
here.
A
Correct
all
right,
so
the
first
thing
I
wanted
to
mention
was
that
I
was
at
the
a
life
2020
conference
and
I
presented
on
some
work
that
I
did
with
my
other
group
and
we
I
was
in
the
workshop,
and
we
had
a
discussion
about
critical
periods
in
development
and
it
kind
of
was
a
side
discussion,
and
so
a
couple
people
from
the
group
just
continue
that
discussion.
So
we
started
a
discord
channel
for
this
topic,
and
so
this
is
discord
if
you're
not
familiar
with
it.
A
It's
a
it's
kind
of
like
a
platform
like
slack,
but
it's
a
different
platform,
a
little
bit
different
setup.
You
have
channels,
and
you
have,
I
think
you
can
also
do
voice
channels
and
video
channels,
but
this
is
where
I'm
gonna
I'll
send
the
link
to
this
out
to
people
who
are
might
be
interested
in
this
group.
You
just
join
via
this
discord
link.
You
click
on
the
link
you
can
go
in
in
discord.
Is
you
can
install
it
as
an
app
on
your
device,
your
laptop
or
your
phone
or
you?
A
I
think
you
can
just
do
it
directly
through
the
web
browser.
So
this
is
the
critical
periods
in
development
discord.
We
have
one
person,
we've
been
I've,
invited
a
couple
people
one
person
to
join
by
yesterday,
besides
myself,
so
I'm
gonna
be
kind
of
organizing
that
we'll
be
putting
things
in
that
topical
area
in
that
section
and
we'll
see
where
the
conversation
goes,
we're
not
really
up
to
any
sort
of
formal
meeting
yet,
but
we'll
see
where
that
leads.
A
So
that's
the
critical
periods
thing
next
thing
is.
Last
week
I
talked
about
the
psycho
bacillary
psychophysics
paper,
so
I
put
together
a
twitter
thread
on
the
meeting
last
week
and
I
put
a
link
to
the
repository
and
there's
this
rambling
codsbot,
which
is
a
bot
that
I
guess
I
I
don't
know.
If
it's
a
bottle,
a
real
person
looks
kind
of
like
a
real
person.
Almost
thinking
of
psychophysics
now
orthogonal
web
seems
to
be
thinking
of
it
too.
A
So
you
know
that
there's
some,
I
always
like
it
when
they,
you
know
when
someone
picks
up
something.
That's
been
tweeted
out
from
the
group,
one
of
the
groups
I
get
like
to
like
try
to
follow
up
on
it.
So
that's
just
just
thinking
about
that.
Now.
These
people,
I
think,
are
thinking
about
psychophysics
more
in
the
human
sense,
but
you
know
they're
interested
in
the
topic.
So
so
then,
actually.
E
E
B
A
E
D
E
A
I
guess
the
discord
what
I'd
say
about
that
is,
I
don't
really
know
yet
and
I'm
just
I'm
using
it
as
an
experiment.
The
reason
I'm
keeping
it
separate
from
the
other
side
channels
is
because
I'm
not
really
sure
where
it's
going
to
go
yet.
So
I
kind
of
wanted
to
have
a
separate
medium
for
that
I've
played
around
a
little
bit
with
this
chord
and
it's
basically
like
slack.
You
can
do
a
lot
of
the
same
things,
but
it's
I
guess
it's
nominally.
A
Maybe
a
public
thing
like
you
just
send
out
the
link
and
someone
can
join
whereas
a
slack
you
have
to
invite
them
in
and
but
I
don't
know
like,
I
don't
know
that
much
more
about
it.
I
don't
think
it's
that
much
different
than
slack,
but
I
think
it's
a
good
way
to
manage
a
group
like
that,
because
you
know
you
can
post
things
to
the
group.
You
can
post
things
privately,
you
can
put
up
papers
and
you
know
manage
all
that
so.
A
B
B
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B
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G
A
A
A
A
If
people
were
interested
but
for
purposes
of
this,
you
know
people
they're
looking
at
things
like
you
know,
scale
time,
scale,
length
scale
and
things
like
that
they're
also
looking
at
entropy,
so
we've
talked
about
entropy
as
well
information,
entropy
and
then,
let's
see
so
they
give
some
information
about
the
frequency
domain.
Frequency
domain
measures
look
at
the
power
law
relationship
for
different
ages,
a
participant
here,
so
we
have
seven-year-olds
29
year
olds
and
76
year
olds,
and
the
idea
is
that
the
signal
is
variable
enough
that
you
can
scale
this.
A
A
Let's
see
oh
yeah,
determined
fluctuation
analysis,
so
determined
fluctuation
analysis
is
where
you
take
a
time
series
and
you
take
all
of
the
trends
out
of
it,
so
you
actually
are
left
just
with
the
variability
component.
There's
a
way
to
do
this
in
these
in
economics
a
lot,
but
they
also
use
it
in
complexity.
Theory,
it's
just
a
way
to
deal
with
it.
A
These
measures
of
complexity
and
fractal
scaling.
So
this
is
the
the
trended
fluctuation
analysis
down
here.
This
is
the
power
law
slope.
This
is
another
version
of
the
detriment,
fluctuation
analysis
and
then
a-p-e-n,
which
I'm
not
really
sure
what
that
is.
Let's
see
what
that
is
approximate,
entropy,
okay.
So
this
is
the
the
idea
of
using
another
measure
of
from
information
theory,
another
measure
of
entropy,
to
look
at
the
same
data.
A
Okay.
So
then
there
there's
that
so
you
you
know
people
you
can
analyze.
You
know
cardiac
information
using
these
different
types
of
measurements
and
it
gives
you
different
results
than
you
might
get
if
you
just
looked
at
the
standard
measurement
that
that
should
just
give
you
a
little
bit
of
an
idea
of
this
there's
another
paper:
application
of
chaos,
theory
to
biology
and
medicine,
which
is
this
paper.
A
So
this
is
just
putting
numbers
on
things
that
we
see
in
in
nature,
very
complex
processes
and
you're,
putting
a
number
on
it,
you're
using
a
parameter
to
sort
of
understand
variability
in
that
as
well,
because
if
you
have
a
parameter
and
you'll
see
in
our
the
next
thing,
I'm
going
to
show
you
how
changes
in
parameter
can
make
difference
in
what
you're,
observing
or
the
process
that
you're
observing.
So
quantification
of
a
chaotic
system,
such
as
a
nervous
system,
can
occur
by
calculating
the
correlation
dimension.
A
So
this
is
another
measure
correlation
dimension
of
a
sample
of
the
data
that
the
system
generates
for
biological
systems.
The
point
correlation
dimension,
which
is
another
measure,
has
an
advantage
in
that
it
does
not
presume
stationarity
of
the
data
and
stationarity.
Is
this
idea
that
it's
there's
some
predictable
fluctuation
in
the
data
as
the
d2
algorithm
must
and
then
and
thus
can
track
the
transient?
A
They
often
have
a
reduced
sensitivity
and
specificity
compared
to
the
dimensional
measurements,
so
they're
arguing
the
dimensional
measurements
that
they're
introducing
here.
These
correlation
dimensions
are
actually
better
than
your
something
like
a
standard
deviation
or
a
power
spectrum
analysis,
because
they
have
a
better
statistical
quality
to
them,
and
so
they
don't.
Oh.
This
is
just
the
abstract
to
this
paper,
but
again
we
have
a
paper
where
people
are
proposing
a
set
of
measures
and
they're
arguing
why
it's
bet
superior
to
like
something
that
came
before
it,
which
is
more
stan.
A
So
they
investigate
the
conditions
under
which
cortical
activity
alone
makes
spontaneous
activity,
self-reproducing
and
stable
against
fluctuations
of
spike
trains.
Invoking
simple
assumptions
of
properties
of
neurons,
it
has
shown
that
the
stochastic
background
activity
cannot
be
stabilized
when
all
neurons
are
excitatory.
A
If
you
look
that
up
on
google
you'll
see
that
there's
a
lot
of
there's
a
there's,
a
fair
amount
of
stuff
on
that
and
so
they're,
basically
looking
at
measuring
the
activity
of
single
neurons
and
populations
of
neurons
and
looking
at
the
sort
of
complex
dynamics
that
are
going
on
here,
so
spontaneous
activity
becomes
self-stabilizing
in
the
presence
of
local
inhibition
given
in
reasonable
values
with
parameters
of
the
network.
Spontaneous
activity,
reproduces
itself
and
small
fluctuations
in
the
rate
are
suppressed.
A
If
the
time
integra
integrate
integration.
Time,
constants
of
excitatory
and
inhibitory
neurons
at
the
selma
are
equal.
Local
excitatory
inhibitor
inputs
to
a
neuron
must
balance
to
provide
local
stability
so
they're,
giving
all
these
conditions
for
sort
of
stability
in
the
network,
stability,
locally
or
stability
across
the
wide
range
of
neurons,
and
so
they
talk
about
a
number
of
these
types
of
results,
and
so
again,
in
this
case
you're
using
different
measures
you're
using
different.
A
You
know
ways
to
measure
a
time
series
to
look
at
the
activity
of
single
neurons
in
the
activity
of
populations,
and
this
is
something
that
is,
you
know,
very,
a
very
hard
problem.
It's
not
an
easy
problem
to
attack,
and
so
you
know
using
this
sort
of
you
know.
Looking
at
the
fluctuations
in
signals
is
actually
quite
useful
in
this
case,
and
so
they
talked
about
using
this
sort
of
approach,
as
opposed
to
like
something
that
concentrates
on
sort
of
the
mean
field
approach,
which
is
where
you
know.
A
A
So
this
is
a
heavy
mathematical
modeling
paper,
but
the
id
that's
basically
the
same
idea:
they're
modeling,
neurons,
they're,
modeling,
the
fluctuation
in
activity
and
then
they're
looking
at
the
individual,
neurons
versus
the
population,
and
so
that's
all
possible
using
like
a
lot
of
dynamical
system
and
systems
and
mathematics
that
you
know
I'm
not
going
to
get
into
here,
but
that's
basically
the
idea
behind
biological
chaos,
and
so
I
think
that's
something
to
think
about
further.
I
you
know
it's.
A
I
can't
really
do
it
justice
in
like
10
or
15
minutes,
but
I
can
tell
you
that
there's
much
more
to
read
out
there
on
this
topic,
but
I
did
want
to
show
you
a
couple
of
visualizations.
Maybe
make
this
a
little
clearer.
What
we're
talking
about
so
susan
last
week
mentioned
diffusion,
limited
aggregation,
and
so
I
went
out
and
I
found
in
netlogo,
which
is
a
agent-based
modeling
program.
I
found
some
simulations
of
diffusion,
limited
aggregation,
so
this
first
one
is
a
video,
and
I
recorded
this
of
the
simulation.
A
I
don't
know
if
I
can
make
it
bigger,
but
so
this
video
is
going
to
be
of
this
simulation.
It's
going
to
initialize
with
a
single
point
and
you're
going
to
see
a
bunch
of
particles,
as
you
can
see
in
the
field
of
view
there
and
they're
kind
of
coming
across
the
field
of
view
and
they're
kind
of
wandering
around
doing
a
random
walk,
and
then
you
see
in
the
middle,
where
they're
starting
to
aggregate
when
they
hit
that
middle
particle,
that's
sort
of
the
initial
particle
they
start
to
attach
to
it.
A
A
A
Let
me
assure
you
that
that's
what
it
looks
like
very
similar.
You
know
if
you
look
at
if
you've
ever
seen,
frost
on
a
window
or
something
you
can
look
up,
what
a
snowflake
looks
like
online
here,
but
you
know
this
is
how
this
process
aggregates
you
just
have
individual
particles
and
they
aggregate
in
this
way,
and
they
form
this
complex
structure.
A
A
But
this
this
video
was
on
for
another
two
minutes
I
mean
you
know
it
kind
of
goes
on
here.
Just
wanted
to
get
kind
of
a
bit
more
to
the
end
yeah.
So
it's
growing
a
little
bit
more
now,
if
you
notice
on
the
right,
and
what
about
I
mean
you
have
a
couple
of
these
sliders
and
I
know
I
talked
we
in
our
group
last
saturday,
the
other
group
I'm
in
and
jesse
was
there.
We
talked
about
this
platform
that
breezing
net
logo
and
how
they
use
these
sliders.
A
A
The
wiggle
angle
is
just
like
the
angle
of
the
random
walk
like
how
variable
is
the
random
walk.
Does
it
go
all
over
the
place,
or
does
it
stay
in
a
very
focused
area?
This
is
set
rather
low
again,
so
this
process
might
be
a
lot
faster
if
you
have
like
the
set
at
a
higher
level.
So
again,
these
parameters
really
do
determine
what
happens
in
the
simulation.
A
We
can
also
control
the
speeds.
So
that's
you
know.
Just
to
try
to
control
everything
there,
okay,
so
I'll
go
back
to
a
quick
recap
of
doa
one.
So
this
is
the
the
video
I
was
showing.
So
this
is
the
simulation.
If
you
watch
it,
you'll
see
that
these
particles
attach
to
this
central
particle
as
the
simula
after
the
simulation
is
initialized,
and
you
have
these
sliders
on
the
left
and
on
the
top
that
control
the
parameter
values,
and
so
sometimes
these
parameter
values.
A
If
you
set
them
high
enough,
they
result
in
something
called
phase
transitions
which
are,
you
know,
allow
it
to
become.
You
know,
go
to
a
new
state,
so
if
you
have
a
lane
of
traffic
and
you
crowd
it
to
a
certain
degree
with
with
vehicles,
then
they
there's
spontaneous
jamming,
and
so
this
this
is
the
sort
of
thing
you
would
might
see
in
this
simulation
as
well.
So
let
me
go
to
doa2.
A
So
doa2
is
a
different
version
of
this.
This
is
where
you
have
not
just
a
random
set
of
particles
that
are
moving
around
and
bumping
into
one
another,
but
you
have
this:
it's
like
snow
falling
down
from
the
sky,
so
you
have
all
these
particles
falling
down
like
this
and
as
they
fall,
they
start
to
aggregate
on
the
bottom,
but
notice
that
the
aggregation
isn't
even
so.
A
A
A
But
that
leads
to
these
patterns.
And
again
this
is
your.
You
don't
have
too
many
parameters
in
this
simulation,
but
you
can't
increase
and
decrease
the
speed.
So
you
can
make
this.
You
know
you
can
change.
You
can
play
around
with
the
speed
and
it
may
give
you
you
know
different
results
in
terms
of
not
just
what
not
necessarily
different
results
for
the
outcome,
but
to
you
know
to
enhance
your
understanding
of
what's
going
on.
A
A
A
D
G
G
G
B
G
G
G
G
A
Well,
from
the
the
main
website,
we
have
a
list
of
contributors
and
we
have
the
links
on
that
site.
I
think
I'm
gonna
I'll
look
and
make
sure
it's
updated
and
then
send
you
the
link,
and
so
you
can
just
take
that
and
take
the
links
and
affiliations
from
that
from
that
page.
But
it
has
yeah
and
I
think
that's
there
are
a
couple.
A
G
G
G
G
G
G
G
A
Okay,
yeah,
I
mean
there,
you
know
it's
like
I.
I
don't
know
what
the
benefit
of
people
signing
up
it
would
be
if
you'd
have
like
they'd
have
their
own.
If
they
had
their
own
secure
account
like
where
they
could
store
things.
It
would
be
one
thing,
but
if
they're
just
gonna
use
it,
I
mean
it'd,
be
nice
to
know
who
they
are.
But
you
know
it's
like
they
make
that
voluntary.
G
G
A
A
H
Yeah,
so
the
last
week
was
like
some
of
the
time,
went
into
improving
the
documentation
for
d1
itself
and
the
other
time
I
spent
in,
like
writing.
Example
notebooks
like
so
that
people
can
get
started
like
I
wrote
another
one
and
I
think
I
sent
you
the
gif
on
slide.
I
don't
exactly
remember
so.
This
one
is
basically
I
work
more
on
those
like
embryo
empire
networks
that
bradley
talked
about.
So
I
kind
of
made
this.
H
H
H
H
H
A
A
So
it's
the
project
ends
the
31st.
That's
actually,
I
think
the
deadline
for
submissions
and
so
project
submission
and
we'll
go
over
this
in
more
detail
next
week
is
going
to
involve
you
filling
out
their
form
by
kind
of
describing
what
you
did
and
then
submitting
a
link
where
it
has
to
execute.
So
I
think
both
of
you
have
that
down
where
you
can,
I
think
it's
well.
It's
asked
has
to
be
like
a
repository.
A
So
in
years
past
and
vinay
can
confirm
this.
You
know
you
can
submit
a
gist
repository
or
you
can
submit
a
github
repository
just
a
place
where
everything
resides
and
I'm
not
sure.
If,
if
you
just
submit
diva
learn,
you
know
you
might
want
to
submit
something,
basically
where
they
can
run
it
and
execute
it
and
understand.
What's
going
on
under
the
hood,
so
you
know
I
don't
know
if.
H
A
Yeah
so
yeah
you're
gonna.
Well,
I
guess
you
have
to
submit
it
in
the
file
like
in
the
final
form,
what
the
link
is,
but
that's
up
to
you
but
yeah,
then.
The
other
thing
you
want
is
to
have
a
readme
where
you
can
go
through
and
have
the
people
you're
submitting
it
to
understand
what
you
did
and
in
some
cases
people
have
been
very
elaborate
on
this,
but
I
don't
think
you
need
to
be
that
elaborate
just,
and
I
think
you
guys
probably
have
that
down,
especially
my
hook.
A
I've
seen
some
of
his
things
that
he
has
on
github
and
it's
like
it's
very
complete,
but
I
would
just
like
try
to
create
a
readme
for
the
submission
that
goes
through.
Everything
provides
a
link
and
then
the
link
would
be
to
the
repo
and
then
it
should
be
very
straightforward
on
how
to
execute
it,
and
I
guess
their
rule
is
that
they
have
to
be
able
to
execute
it
for
you
to
pass.
A
A
A
F
A
Yeah,
so
let's
see
I'm
going
to
share
my
screen,
so
jesse
asked
earlier
about
evil
learn,
so
let's
go
back
over
evil
learn.
Actually
I
wanted
to
cover
a
couple
things.
So
the
first
thing
is
diva
learn
itself.
Let's
just
kind
of
review
that
to
see
where
that
is-
and
I
know
maybe
jesse
and
krishna
didn't
see
it
last
week,
but
we
have
now.
We
have
this
organization,
divo
learn
and
we've
added
a
couple
things
into
this.
A
So
we've
got
evil
learn
here
with
the
sort
of
so
it's
sort
of
this
d
learn
standalone
software.
That's
version
0.9,
something
now,
and
so
this
is
the
thing
that
maya
created
for
this
g-sock.
As
you
know,
in
addition
to
other
things
in
this
organization,
but
this
is
the
main
kernel
here
of
what's
going
on
with
divalern.
A
H
A
You
might
you
know
you
might
submit
that
to
this
repository
as
well.
Just
you
know,
okay
yeah,
and
then
we
have,
of
course,
the
networks.
This
is
myok's
work
on
networks,
so
this
is
the
stuff
that
we
had.
He
committed
the
csv
file,
so
this
is
these:
are
data
for
the
cs
in
csv
format,
so
this
is
the
table.
A
We
can
get
some
besides
question
and
we
might
get
some
more
commits
to
this,
and
then
we
can
make
a
directory
here
and
then
so
that's
the
data
science
demo.
This
is
c
elegans
divalern.
So
this
is
stuff
here
so
yeah.
This
is
the
he
has
the
repository
tree
in
here
and
I
use
it.
So
this
is
all
sort
of
documentation
for
that.
A
Then
we
have.
The
final
piece
of
this
is
contribution
guideline.
The
media
folder
is
for
just
random
pictures
and
files,
and
then
the
contribution
guidelines
are
how
to
contribute
to
this.
So
we
have
evil.
Learn.
Now
is
a
standalone
thing.
We
have
you
know,
here's
how
you
contribute
and
we
wanted
to
put
those
guidelines
in
because
that's
a
good
practice
for
open
projects,
and
so
now
we
have
three
people
you
can
contact.
A
A
E
B
E
A
Right,
okay,
so
that's
that's
yeah
and
we
want
to
add
on
to
that,
especially
so
yeah
we're
going
to
try
to
make
this
go
publix
like
starting
next
week,
and
I
want
to
make
announcements
and
be
a
little
bit
more
public.
I
mean
we've
kind
of
mentioned
it
a
little
bit
here
and
there,
but
you
know
you
get
to
have
that
initial.
A
You
know
that
sort
of
inauguration
of
the
thing,
so
people
know
that
it's
here
and
it's
available
so
and
then
so
that'll
be
good
and
I
think
people
people
will
be
very
interested.
The
final
thing
I
wanted
to
talk
about
was
the
divorm
ml.
I
wanted
to
revisit
this
for
a
minute,
so
this
is
something
jesse
asked
me
about
in
conjunction
with
something
else
he's
working
on,
and
I
know
vinay
and
jesse,
I
think,
and
maybe
usual
were
there
last
fall
when
we
did
this,
and
this
was
a.
A
It
was
a
course
that
we
did
on
machine
learning
techniques.
So
the
idea
was
to
bring
together
biologists
and
computer
scientists
and
people
in
between
and
basically
interested
in
learning,
more
about
machine
learning
and
learning
more
about
biological
simulation
and
the
like.
So
we
did
this
last
fall.
It
was
a
16
week
course.
I
mean
it
went
on
for
from
about
september
4
to
december
16th,
and
so
each
week
has
a
topic,
and
this
was
all
saved
as
videos
and
slides,
and
so
I
have
it
in
the
diva
or
ml
repo
here
on.
A
It's
been
kind
of
sitting
there.
So
we
had
you
know,
meetings
on
digital
basil
area,
open
devo
cells,
so
those
were
the
things
from
last
year
that
we
actually
produced
over
the
summer.
So
lujan
was
involved
in
the
digital
basil
area.
Vinay
was
involved
in
open
devo
cell
and
they
we
we
got
those
out
there,
and
then
we
talked
about
different
things
like
input,
data
and
pre-trained
models
for
biology,
which
became
this
evil
learn
project.
A
For
my
up
this
year
we
did
covered
a
couple
of
papers
that
were
interesting,
tensorflow
tutorial
from
vinay.
We
did
this
computational
peridolia,
which
is
an
interesting
topic
check
that
out.
If
you're
interested,
we
did
like
things
like
medical,
imaging
developmental
gans,
which
was
something
actually
serendipitously
explored
further
this
summer
and
again
a
lot
of
this
stuff.
A
E
A
A
Susan
said
I'm
interested
in
looking
at
the
course
since
I
missed
it,
I
could
look
at
it
with
regards
to
education
format.
So
yeah,
let
me
give
you
the
link
to
the
repository
here.
This
is
on
github.
You
can
just
I
mean
everything
is
open.
You
just
click
on
the
links
and
a
lot
of
it
is
the
well.
The
readme
is
where
you
want
to
go
for
the
syllabus
and
then
their
videos
and
talks.
So
that's
the
kind
of
the
format
it's
in
now,
but
it
doesn't.
E
E
E
A
Yeah,
so
divalern
is
it's
sort
of
just
emerging,
so
it's
our
like.
You
know
we
have
like
the
the
two
well,
we
have
like
ojuwal's
contributions,
which
are
like
the
different
platforms
and
bringing
that
together.
Then
oaks
pre-trained
model
type
approach,
and
then
we
have
some
other
things
that
were
spin-offs
of
other
things
that
we
did
this
summer
too,
so
the
networks
and
the
and
the
movement
stuff.
Oh,
we
didn't
put
the
movement
stuff
up
here.
Did
we
make?
A
G
G
A
D
A
That
sounds
good,
so
yeah,
okay,
so
thank
you
for
attending
sorry
about
the
glitch
earlier
I'll
make
this
available
glitch
free
on
youtube.
After
so
we,
if
you
want
to
go
back
and
find
anything
and
think
about
it,
more
it'll
be
up
there,
but
otherwise
have
a
good
week.
Next
week,
we'll
talk
about
gsoc
submissions
and
probably
some
other
topics
as
well.