►
From YouTube: DevoWorm (2021, Meeting 26): GSoC #7, Proprioceptive Environment, Thermal Optima, Cognitive Models
Description
Update on Summer of Code activities (Mainak Deb, Improving DevoLearn), Review of submissions document and group-wide task board. The role of temperature in development, ecology, and evolution. Popular articles on the process of metamorphosis in insects. Papers on Cephalopod Cognition and Neuromechanical modeling in Drosophila larvae. Attendees: Akshay Nair, Bradly Alicea, Ujjwal Singh, Sanjay from Amrita University, and Mainak Deb.
B
C
C
A
A
A
That's
good
glad
to
hear
that
everything
is
going
well
for
you.
Your
internship
sounds
like
it
was
pretty
successful,
and
so
you
know
we'll
be,
I
guess,
we're
written
kind
of
in
the
throes
of
summer
of
code,
so
we're
getting.
You
know
to
the
part
where
we're
really
kind
of
getting
some
tangible
results.
So
look
forward
to
you,
get
providing
your
input
and
joining
in
the
group
meetings
and
that.
E
A
All
right
thanks
so
yeah
that
brings
us
to
my
knock
and
he's.
I've
got
gotta,
so
that's
his
blog
post
or
his
blog
that
he
posted
in
the
chat.
So
that's
his
google
summer
code
update
he's
doing
weekly
update
on
his
blog,
so
my
neck,
I
checked
out
your
github.
Pushes
they
look
good.
Why
don't
you
give
a
presentation
on
your
progress
for
this
week?
B
A
E
A
A
A
A
A
B
A
B
A
B
Yeah,
so
this
is
the
week
six
update
of
google's
code,
so
the
first
update,
as
you
can
read
like
the
first
update,
was
to
basically
swap
out
the
cell
membrane
segmentation
model.
That
was
which
was
already
there
in
the
in
the
repository,
and
I
I
actually
had
trained
a
new
model,
so
I
had
to
swap
it
out
and
just
exchange
the
pth
file.
That's
all
there
was
to
it,
but
then
I
had
to
run
the
tests
locally,
because
travis
has
been
like
they
have
stopped.
A
B
So
the
second
point
is
that
I
actually
have
to
rename
the
embryo
segmented
package
to
the
cell
membrane
segmented,
because
first
of
all
the
name
is
a
bit
more
specific.
Now,
because
the
name
itself
talk
about
because
the
name
itself
it
talks
about
the
function
of
the
package,
and
the
second
reason
is
that
is
that
to
avoid
naming
conflicts
when
we
add
new
segmentation
models
to
the
library
so
on
on
the
end
of
the
user,
there
are
only
us
like
it's
like
the
on
the
end
of
the
user.
There
is
only.
B
B
Or
like
to
make
it
work
with
the
library
as
a
whole,
I
wrote
all
that,
and
then
I
made
some
additions
to
the
testing
script
too,
to
accommodate
for
this
additional
model,
and
I've
actually
elaborated
the
steps
which
would
be
required
to
run
inference
on
one
single
input,
image
here
and
later
I'll
be
working
on
a
function
which
could
basically
run
inference
on
an
entire
file
of
an
entire
folder
of
images
which
have
like
an
entire
folder
that
contain
input
images.
So
that's
something
that's
coming
and
really
can
you
scroll
down
a
bit?
B
They
are
basically
taken
that
these
outputs
are
basically
taken
from
that
model
itself,
so
the
first
cliff,
as
you
can
see
the
input
image,
which
is
basically
a
2d
image
which
is
varying
through
the
z-axis,
so
the
inputs
are
waiting
through
the
z-axis
and
the
segmentation
maps
are
also
like
the
segmentation
maps
that
you
see
to
the
right,
they're
actually
corresponding
to
the
input
image
which
came
out
of
the
model,
which
is
basically
the
model
output.
So
this
is
one
way
the.
B
B
B
B
I
guess
that
would
be
a
better
thing
for
a
lot
of
the
non-polars
out
there,
so
then
yeah
so
that
was
then.
The
next
plan
is
to
move
from
privacy
a
to
get
your
actions
because
running
the
tests
online
for
every
running
the
test
offline
for
every
pr
or
every
single
little
change
that
I
make
it's
actually
a
bit
tedious
because
it
takes
nearly
300
seconds
or
so
to
run
the
tests
and
like
it's.
Actually
it
actually.
A
E
C
E
B
B
A
B
A
E
E
A
A
You
know
fair
results
and
they
want
to
know
kind
of
how
the
model
is
performing,
because
we
have
all
these
different
models.
I
know
that
usually
was
working
on
this
last
summer.
We
have
a
number
of
different
models
that
we
we
have
available,
that
we
can
use.
So
you
know
their
open.
Devo
cell
was
one
from
two
years
ago.
This
is
another
one.
A
If
you
don't
know
much
about
machine
learning,
then
people
are
just
going
to
use
their
eyes
to
look
at
it
and
say
that
that
looks
good.
Where
that
doesn't
look
good,
so
I
mean
you
know,
that's
that's
something
that
we
can
have
just
you
know
as
depending
on
who's
using
the
interface
that
might
be
useful
or
not
so
yeah.
Thank
you.
That
was
a
nice
mock-up
that
you
made
here
now
like
it's
it's
a
fairly
clean
mock-up.
You
have,
you
know
the
well.
I
would
imagine.
B
B
A
If
you
watch
it.
Yes,
so
that's
nice,
that's
good!
Now
this
the
z-axis
is
like
you're
looking
at
the
different
stacks
so
like
they
have
a
z,
coordinate
in
the
data
set,
they
have
a
z-axis
which
is
like
going
through
the
stack
and
it's
like
the
bursal
ventral
axis.
So
this
is
just
going
down
that
axis
and
looking
at
how
the
images
or
how
the
values
vary
on
that
axis,
correct.
A
A
Right
and
so
yeah,
you
kind
of
see
a
different
pattern
of
like
how
things
are
popping
up,
like
you
know,
like
the
z-axis.
If
you
go
through
a
stack
with
a
microscope,
you'll
see
like
things
kind
of
come
into,
focus,
come
out
of
focus,
and
then
you
know
as
you
go
through,
and
that's
you
kind
of
see
that
here
in
in
the
top
image
and
the
bottom
image
you
see
things
kind
of
emerge
in
time
like
their
cells,
are
dividing
or
popping
into
view
yeah.
So
that's.
A
A
B
A
A
B
A
Okay,
good
all
right,
so
this
is
our
submissions
document.
So
we
update
this
periodically.
We
were
there's
a
period
where
we
were
updating
things
every
week,
but
we're
not
submitting
every
couple
days
like
we
were
earlier
this
year,
but
anyways,
let's
go
through
this,
so
we
have
a
number
of
things.
We
want
to
kind
of
go
over
a
lot
of
things
completed.
A
The
diva
learned
paper
is
going
to
be
later,
maybe
in
the
fall
after
the
end
of
gsoc,
the
bacillary
non-renal
cognition
paper
is
still
going,
I'm
working
on
it,
I'm
getting
it
to
sort
of
a
critical
mass.
Now
you
know
kind
of
you
don't
want
to
push
these
things
too
hard,
because
you
know
you
have
to
do
a
lot
of
thinking
and
getting
things
in
order.
So
this
will
be
worth
coming.
A
I
don't
know
exactly
when,
but
it's
coming
along
pretty
well,
it's
going
to
be
like
it's
not
going
to
be
too
much
based
on
data
as
it
turns
out,
but
that's
okay,
this
this
particular
paper,
so
we'll
probably
maybe
have
another
derivative
paper
with
more
data,
but
I
didn't
want
to
make
this
dependent
on.
You
know
a
data
analysis
right
now,
so
this
is
something
that's
been
solicited
for
us
and
I'm
trying
to
get
the
basic
ideas
out
there.
A
A
A
A
This
is
just
a
collection
of
you
know,
sort
of
mathematical
tools
that
we
use,
and
some
of
that
was
in
the
open
arm
poster
that
I
showed
a
couple
weeks
ago,
but
this
is
still
ongoing.
We
want
to
make
this
into
something:
that's
like
a
short
paper
and
not
just
a
poster,
so
the
test
of
williamson
symbiosis,
that's
where
we
take
this,
these
ideas
of
williamson
and
use
genetic
data
to
see
to
test
certain
hypotheses
and
we'll
talk
about
williamson
in
a
little
bit,
but
we
have
this.
A
A
A
Then
you
get
up
to
higher
level
derivatives
which
are
jerkiness
and
other
types
of
processes,
and
so
these
are
like
things
that
are
very
high
frequency
movements,
and
so
that's
what
this
is
about
this
game
theory
and
developmental
processes.
This
has
been
accepted
at
dynamics
days,
europe.
This
is
something
that
I'm
going
to
be
kind
of,
putting
slides
together
on
soon,
because
the
dynamics
days
is
in
late
august,
and
we'll
talk
more
about
that
when
I
put
the
slides
together,
this
is
a
one
of
the
open
papers
that
we
have.
A
A
A
They
can
be
triangles,
they
can
be
different
shapes
and
then
you
can
compare
them
to
shape
droplets
or
as
as
was
done
in
this
euler
pads
for
life
presentation,
you
can
analyze
them
using
a
network
approach,
and
so
that's
that's
kind
of
what
we're
going
to
do
for
this.
But
this
is
you
know.
This
is
something
that
needs
maybe
some
initiative.
I
I'm,
I
think,
we'll
get
there.
A
A
So
I
talked
about
steve
mcgrew
two
weeks
ago
and
he
was
what
they
call
polymath
and
he
you
know,
did
a
lot
of
things
in
his
life
and
one
of
the
things
is.
He
wrote
this
book
that
was
unpublished.
It
was
called
eye
of
nature
and
I
actually
have
a
copy
of
the
manuscript
I
was
reading
through
in
the
last
couple
weeks
and
it's
really
interesting.
A
It's
got
a
lot
of
interesting
ideas,
it's
about
evolution
and
you
know
about
sort
of
biology
and-
and
you
know
there
there's
this
non-neuronal
cognition
component
to
it
as
well.
So
I
mean
these
are
things
that
fit
well
under
the
theme
of
our
group,
we're
thinking
of
maybe
publishing
this
as
a
some
sort
of
you
know,
self-published
thing
or
maybe
publishing
it
in
pieces.
A
It's
really
hard
to
say
what
will
happen,
but
I
think
well,
maybe
it's
maybe
closer
to
the
fall,
we'll
start
going
through
the
chapters
and
looking
at
what's
there
and
finally
there's
this
differentiation
tree
of
the
brain
which
I've
been
I.
That
was
mainly
a
note
for
me
to
bring
that
up
in
a
meeting,
and
I
wanted
to
bring
it
up
when
dick
was
here.
But
I'll
wait
for
him.
I
I
keep
putting
it
off
because
I
I'll
bring
it
up
in
a
couple
weeks,
but
this
is
basically
some
work.
A
Some
stuff-
I
was
doing
with
my
other
group
on
aid,
computational
agents
and
how
to
develop
them
and
evolve
them,
and
so
there
are
some
insights
here.
A
differentiation
tree
of
course,
is
where
you
look
at
cells
as
they
differentiate,
and
they
form
these
tree
structures
where
you
know,
when
one
group
of
cells
differentiates
into
a
tissue,
then
that
gives
rise
to
other
tissues
and
so
forth,
and
it
builds
this
tree
or
this
sort
of
hierarchy
and
time
of
of
tissues
that
form.
A
So
I'm
going
to
talk
about
that
in
a
in
a
couple
weeks,
so
these
are
all
different
things
that
you
can,
if
you're
interested.
Let
me
know-
and
I
can
give
you
a
deeper
dive
into
the
topic-
we
have
links
that
well,
we
have
deliverable
links.
This
hasn't
really
been
updated
very
systematically,
but
I
can
give
you
the
latest
updates
on
that.
If
you're
interested,
oh
hello,.
B
C
B
C
A
Well,
we'll
be
updating
it
and
that's
a
fast-moving
area.
It's
something
that
you
know.
I
don't
know
what
we'll
be
doing
with
it,
but
we
have
you
know
in
an
archive
paper.
You
can
update
it
and
then
put
up
new
versions
and
then
eventually,
if
you
get
it
published,
then
you
know
that's
a
publication,
but
then
even
then
you
can,
you
know,
make
new
version.
You
know
make
new
things
from
it
in
terms
of
research,
so
yeah
just
yeah
yeah.
I
mean,
if
you
think,
there's
things
that
you
know.
A
D
A
Does
that
correspond
with
the
top
levels
of
an
alex
net
and
so
forth,
and
so
they're
different
analyses?
You
can
use
to
look
at
like
the
way
those
things
compare
whether
they're,
I
think
you're
using
a
correlation
coefficient,
which
is
you
know,
not
maybe
not
the
best
tool,
but
it's
what
we
have
now
today
to
look
at
the
similarities
and
there
the
similarities
aren't
very
strong
overall,
but
you
do
see
patterns,
so
some
of
the
levels
are
doing
things
more
like
the
brain
than
others,
and
it's
really
interesting.
A
So
we
have
you
know
things
that
are
finished
for
the
year
makes
you
feel
accomplished
to
have
a
long
finished
list.
But
you
know
these
are
things
that
we
put
on
the
board
and
they're
finished.
We
also
have
these
things
that
are
off
the
radar.
So
there's
a
lot
of
stuff.
That's
like
you
know
stuff
that
we
were
gonna.
A
Do
it
didn't
happen
for
whatever
reason
this
actually
might
be
back
on
the
radar
here,
because
we
have
this
talk
so
see
things
fall
off
the
radar
and
then
come
back
on,
and
then
things
fall
off
the
radar
and
you
get
things
that
are
held
because
you
don't
know
how
to
implement
them
so
that
you
know
this
is
how
it's
organized
it's
like.
We
have
these
things
to
do
and
we
kind
of
propose
them
and
then
maybe
we
move
this
into
hold
because
it's
you
know
something.
A
A
A
A
A
A
That's
what
they
call
non-neuronal,
meaning
that
there's
no
brain.
It's
just
a
you
know
some
sort
of
model
of
movement,
some
sort
of
model
of
like
information,
processing
and
I'll
cover
that
in
a
couple
weeks,
but
suffice
it
to
say
that
this
is.
You
know
something
that
is
it's
not
an
easy
thing
to
kind
of
convince
people
of,
so
we
need
to
have
some
solid
computational
models.
We
need
to
have
some
and
you
know,
data
analysis.
A
So
that's
you
know
that's
coming
together,
slowly
but
surely-
and
so
that's
the
couple
issues
around
that
here.
So
again,
that's
that's
what
we
have
for
people
if
they're
interested
in
contributing
and
now
I
think
I'll,
go
to
the
papers
folder
and
in
the
papers
folder.
We
have
a
lot
of
interesting
things
to
talk
about
today,
so
we
didn't
have
any
more
comments.
Well,
where
we
have
comments
in
a
little
bit,
you
can
put
them
in
the
chat.
A
A
So
metamorphosis
is
where
your
organism
takes.
You
know
is
like
one
in
one
state
and
then
there's
this
period
where
it
goes
into
a
cocoon
or
it
goes
into
some
sort
of
hibernation,
and
then
it
comes
out
in
a
totally
different
state.
So,
for
example,
the
caterpillar
turning
into
a
butterfly
the
caterpillar.
Is
this
long
worm-like
creature
and
it's
crawling
around
and
then
it
forms
a
cocoon
at
some
point
in
its
life,
and
then
it
turns
into
this
butterfly
as
it's.
A
A
First,
the
caterpillar
digests
itself?
I'm
sorry
about
this.
I
had
printed
it
from
the
wabinets
there's
text
over
it,
but
releasing
enzymes
to
dissolve
all
its
tissues.
If
you
were
to
cut
open
a
cocoon
at
the
right
time,
caterpillar,
basically
caterpillar
or
soup
would
ooze
out.
So
it
would
be
like
this.
Just
the
soup
of
whatever's
in
the
you
know,
that's
going
to
become
the
new
cells
and
tissue,
but
contents
of
the
pupil
are
not
an
amorphous
mess.
Certain
highly
organized
groups
of
cells
aid
in
the
digestive
process.
A
Before
hatching
when
a
caterpillar
is
still
developing
inside
the
egg,
it
grows
something
called
an
imaginal
disk
so
for
each
of
its
body
parts.
So
this
is
something
we
see
in
drosophila,
which
is
the
fruit
fly
model
it
will
need
it
will
need
as
a
mature,
butterfly
or
moth.
So
it
has
these
like
developmental
sort
of
things
that
happen
in
the
egg
before
it
becomes
a
caterpillar,
even
that
are
going
to
be
important
for
the
butterfly
phenotype.
A
A
A
This
imaginal
disc,
which
is
the
so
on
the
front,
fly
the
imaginal
disc,
might
begin
with
only
50
cells
and
increase
to
more
than
50
000
cells
by
the
end
of
metamorphosis,
depending
on
the
species.
Certain
caterpillar
muscles
and
sections
of
the
nervous
system
are
largely
preserved
in
the
adult
butterfly.
A
One
study
even
suggests
that
moths
remember
what
they
learned
in
later
stages
of
their
lives
as
caterpillars,
and
I
don't
know
if
I
think
we've
talked
about
this
in
previous
meetings,
but
the
flatworm,
which
is
a
model
for
regeneration.
It's
a
different
type
of
worm
than
c
elegans,
but
the
flatworm
actually
has
this
incredible
regenerative
capacity
where
you
can
take
a
single
cell
and
you
can
create
a
whole
new
organism,
and
these
cells
are
basically
totipotent,
meaning
that
a
single
cell
can
reproduce
the
entire
phenotype
what's
interesting
about
flatworms.
A
Is
that
if
you
take
a
single
cell
and
you
regenerate
a
phenotype,
that
new
phenotype
will
actually
sort
of
reproduce
the
memories
of
the
worm
than
which
was
taken
from
so
there's
this
flatworm,
you
take
a
cell
out,
you
make
a
new
worm
and
then
that
worm
retains
the
memories
of
the
old
worm.
So
that's
it's
interesting
that
that
mechanism
sort
of
may
exist
in
both
of
these
systems,
so.
D
A
A
look
at
this
metamorphosis.
Metamorphosis
as
it
happens,
is
difficult.
Disturbing
a
caterpillar
inside
its
cocoon
or
chrysalis
risks
botching
the
transformation,
so
they
actually
have
some
incredible
photos
of
this
silk
moth
that
failed
to
spin
a
cocoon.
So
this
is
the
link
to
the
incredible
photos,
and
so
we
can
see
here
that,
like
these
are
eggs,
this
is
the
caterpillar
as
it's
crawling
around
after
about
five
days,
the
hatchlings
begin
to
shed
their
first
skins.
A
A
A
A
So
it's
very
soft.
It's
very
like
brittle.
It's
not
soft,
but
it's!
I
guess.
If
you
could
squeeze
it,
you
know
as
a
human.
You
could
squeeze
it
apart,
but
and
then
so
this
is
actually.
This
was
previously
the
caterpillar
of
a
tusa
moth,
which
is
a
certain
type
of
transformation.
Here
the
caterpillar
has
already
shortened
up
becoming
a
pre-pupa
inside
the
caterpillar
shell.
It
was
developing
the
proto-versions
of
its
moth
organs,
antenna
wings
and
legs.
A
The
pre-pupa
is
incapable
of
walking,
you
know,
can
only
wiggle
in
midsection
like
a
hula
dancer,
so
these
caterpillars
that
would
walk
around
and
now
have
lost
their
legs
and
they
can't
walk
at
this
stage.
They're
just
kind
of
wriggling
around
it
began.
It
begins
by
sharking
its
shoulders
in
this
area
back
and
up
spitting
splitting
the
caterpillar
skin.
Unfortunately,
these
shots
didn't
turn
out,
so
it
was
a
little
bit
blurry
in
this
one.
A
This
is
where
it's
sort
of
mid
pipa.
It's
it's
like
coming
out.
It's
moving
around
a
lot
when
it's
in
this
stage
and
the
antenna
and
wings
are
actually
preformed
but
they're
not
complete.
They
are
separate,
and
then
this
is
there
it's
very
hard
to
imagine
in
between
moments.
So
you
get,
you
know
they
get
this
transformation,
but
I
I
don't
think
anyone's
been
able
to
really
capture
sort
of
very
precise
images
of
this
process.
A
It's
just
that
they
have
this
sort
of
hardware
on
board
and
then
it
eventually
becomes
these
things.
But
it's
very
hard
to
see
inside
the
people
to
see
this
this
you
know
like
a
time
lapse.
We
can't
really
acquire
that
here
and
so
then
we
can
see
the
shapes
of
the
legs
antenna
and
wings
distinctly
here.
The
entire
thing
looks
like
it's
been
carved
out
of
delic
green
jade,
but
this
is
the
sort
of
the
skin
here.
A
A
The
people
would
darken
over
a
matter
of
hours
and
then
you
get
this
emergence
of
a
moth
in
this
case
which
has
wings,
and
it
has
this
basic
phenotype
from
the
caterpillar.
But
it
has
wings
and
has
different
parts
that
were
already
kind
of
there
as
as
precursor
organs,
and
there
were
developmental
organs
that
just
kind
of
differentiated.
A
Through
this
process,
so
that's
this
article
and
then
there's
another
article
where
they
another
scientific
american
article
where
they
kind
of
talk
about
you
know
what
people
are
doing
to
find
the
origins
they
do
bring
up
donald
williamson
and
people
kind
of
ridicule,
some
of
the
ideas
he
had.
They
have
this.
A
Little
bit,
you
know
it
doesn't
really
fit
the
data
that
we
have.
So
what
is
you
know?
How
do
we
study
this?
Well,
we
could
use
like
molecular
data,
which
is
something
we've
proposed
doing,
but
you
know
it's
maybe
more
important
to
understand
the
actual
process,
which
is
why
I
showed
you
all
those
pictures
to
give
you
an
idea
of
what
this
process
looks
like,
and
so
you
know,
maybe
it
isn't
that
you
have
two
different
developmental
programs.
A
Maybe
it's
that
you
have
a
single
developmental
program
that
has
many
parts,
so
we
don't
really
know
how
this
works.
Mechanistically
very
well,
but
this
kind
of
goes
through
the
history
of
like
how
people
have
thought
about
metamorphosis,
and
this
goes
back
to
the
17th
century.
So
people
have
proposed
a
lot
of
ideas
about
this.
Why
you
get
this
transformation?
A
We
know
that
they're,
like
they're
imaginable,
discs
involved,
and
we
know
that,
like
there's
certain
genes
that
get
expressed.
So
we
know
that
there's
certain
things
that
are
going
on,
but
we
still
don't
really
have
a
good
like
time
lapse,
imaging
of
it
and
we
don't
know
much
about
the
genetics
so
yeah.
So
this
is
just
kind
of
goes
through
a
lot
of
the
state-of-the-art
and
we
do
have
evidence,
but
not
really
very
conclusive
evidence.
A
A
One
is
that
environment
is
permissive.
This
means
that
there's
this
general
information
in
in
the
environment
so
changes
over
time
stresses
intensities.
These
gradients
of
temperature
are
transduced
into
the
biological
system,
so,
like
a
change
in
temperature
is
an
input
into
the
biological
system.
It
might
trigger
some
gene
to
be
expressed
or
some
response,
some
stress
response.
A
The
second
is
that
environment
is
instructive,
and
that's
that
specific
information,
such
as
ratios
patterns
and
codes,
are
then
transduced
into
the
biological
system,
so
they
could
be
environmental
patterns
like
if
there's
a
drought
or
if
there's
like
this
ratio
of
light
to
dark-
or
you
know
something
like
you
know
daylight
to
night
time.
So
if
you,
you
know,
as
your
seasons
change
daylight,
might
change
the
amount
of
daylight
that
an
organism
gets
and
so
that
might
have
some
effect
on
the
biological
system
so
like
with
different
types
of
circadian
rhythms
in
that.
A
A
So
these
are
these.
Are
things
to
be?
You
know
to
think
about
so
distribution
limits
of
organisms
are
dependent
on
biotic
and
abiotic
environmental
factors,
organisms
maximize
their
fitness
and
an
optimal
environmental
range.
So
they
kind
of
you
know
they
respond
to
the
changes
in
environment
or
the
variation
of
environment
by
trying
to
maximize
their
fitness,
but
they
try
to
find
this
optimal
environment,
optimal
environmental
range,
so
they,
but
they
can
only
survive
short
periods
in
environmental
conditions
that
exceed
a
threshold
in
their
critical
tolerance
limits.
A
So
here
they
have
a
picture
of
penguins
live
in
antarctica,
and
you
know
imagine
if
they
were
transported
to
the
amazon
rainforest
where
it's
very
hot
and
wet
and
their
physiological
tolerance
in
that
case
would
be
very
limited
because
they're,
not
you,
know,
they're
up
their
fitness
is
sort
of
optimized
to
this
sort
of
you
know
arctic
environment,
and
so
this
is
the
the
where
they've
evolved
their
abilities
to
tolerate
temperature,
and
things
like
that.
If
you
move
it
to
another
part
of
the
world,
they
may
not
do
very
well.
A
A
So
you
have
this
lower
critical
limit
in
this
upper
critical
limit,
and
then
you
have
this
performance
optimum.
So
when
the
temperature
is
very
low
there,
the
performance
is
limited.
You
know
if
it's
like
a
human
and
it's
like
you,
know,
10
degrees
below
zero.
You
know
humans
have
to
be
in
a
warmer
environment.
They
wear
clothes
that
you
know.
Performance
is
limited.
A
A
A
To
show
that
you
have
this
thermal.
You
know
like
in
case
of
temperature.
You
have
this
thermal
gradient
where
there's
an
optimum
point
and
then
there
the
sub-optimal
points
and
then
these
critical
limits
that
define
sort
of
the
edges
of
the
survivability
of
an
individual
organism,
in
this
case
they're
talking
about
fitness.
A
So
in
terms
of,
if
you
know,
you're
below
or
above
the
critical
limits,
you
know
you,
you
have
no
reproductive
fitness,
essentially
in
terms
of
people
living
their
lives,
you
know
they
might
die
or
they
might
get.
You
know
injured
or
something
in
development.
When
you
have
eggs
where
you
have,
you
know
embryos
in
a
womb.
A
A
Now
I'm
going
to
go
through
some,
maybe
two
more
papers
and
I
think
I'll
and
again,
if
you
have
things
in
the
chat,
if
you
want
to
put
them
in
the
chat,
we
can
talk
about
them,
see
I
have
this.
This
is
from
a
couple
weeks
ago
we
talked
about
cephalopods
or
octopus,
and
so
we
talked
about
how
octopus
have
this
really
unique
brain
and
how
it
develops
so.
A
Octopus
brains
are
different,
a
lot
different
from
human
brains,
but
they
have
a
really
fascinating
developmental
trajectory
and
so,
in
this
paper,
they're
actually
going
they're
talking
about
how
cephalopods
actually
could
serve
as
a
model
of
cognition,
and
so
we
talked
about
non-neuronal
cognition,
which
is
where
you
have
this
model
of
a
sort
of
a
brain
where
there
is
no
brain
things
that
process
information
like
a
brain.
In
this
case,
this
is
a
different
model
system
for
cognition.
A
You
know
we
usually
think
of
the
human
brain
as
a
system
for
cognition,
but
cephalopods
also
are
very
intelligent.
We're
going
to
say,
express
these
benchmarks
for
intelligence,
but
the
brains
are
totally
different
and,
aside
from
being
connected
into
a
network,
they
are
very
you
know,
they're
very
different
and
they
don't
have
the
same
aspects.
So
this
is
something
you
know
you
think
about
when
you
think
about
neural
nets.
Neural
nets
are
generalized
sort
of
human
brains,
they're
generalized
brain.
A
A
A
A
Brains,
but
they
do
have
this,
these
same
properties,
and
so
I
don't
know
if
there
are
any
good
pictures
in
this
paper,
but
so
this
is
a
a
species
of
octopus
here,
another
species
of
octopus.
So
this
is
their
social
organization
cephalopods,
so
there's
a
solitary
day
octopus
at
the
top.
So
these
are
solitary
organisms.
A
You
know
it's
it's
not
absolute
and
we
can
find
exact
exceptions
to
it.
But
we
know
that
social
pressures
can
influence
cognitive
development
and
performance
within
species.
So
there's
this
selection
that
goes
on
for
larger
brains
and
so
in
you
see
this
in
octopus,
but
even
the
solitary
species
seem
to
have
pretty
decent
sized
brains.
So
yes
doesn't
explain
all
the
variation,
but
there
is
this
length
that
they
talk
about
here
between
social
group,
size
and
cognitive
performance.
A
A
They
just
talk
about
some
of
these
aspects,
sort
of
advocating
for
cephalopods
being
a
good
model
for
cognition,
and
so
let's
see
this
is
another
paper
that
this
one
talks
about
the
integrative
neuromechanics
of
crawling
in
d
melanogaster
larvae.
So
these
are
the
fruit
flies
that
we
talked
about
the
drosophila
model
species,
and
so
this
is
a
model
species
that
we've
that
differ
from
c
elegans
in
a
number
of
ways
they
have.
They
have
more
cells
in
their
body.
A
They
have,
you
know,
bigger,
more
complex
brains
relatively
speaking,
and
they
have
these.
These
imaginable
discs
that
are
sort
of
the
basis
for
their
development,
and
so
they
have
the
cellularization
phase,
and
then
they
have.
These
imaginable
discs
that
emerge,
and
so
the
abstract
of
this
paper
is
locomotion
on
an
organism
as
a
consequence
of
the
coupled
interaction
between
brain
body
and
environment,
motivated
by
qualitative
observations
and
quantitative
perturbations
of
crawling
larvae,
which
means
they,
you
know,
looked
under
a
microscope
and
they
did
things
with.
A
Our
model
couples
the
excitation
inhibition
circuits
and
the
nervous
system
to
force
production
in
the
muscles
and
body
movement
in
a
frictional
environment.
So
they're
forcing
this
sort
of
movement
through
stimulating
the
muscles,
so
they're
getting
body
movement,
and
I
guess
they're
modifying
the
environment.
A
Our
results
explain
the
basic
observed
phenomenology
of
crawling
with
and
without
proprioception,
so
proper
reception
is
where
you're
sensing
forces
and
sensing
the
external
world
of
of
physical
sort
of
forces
that
act
upon
the
body.
When
we
walk
you
know,
on
a
different
surface
surfaces,
we
can
tell
that
we're
walking
on
different
types
of
surfaces,
that's
proprioception,
and
so
this
is
something
they
can
modify.
This
is
an
environmental
input.
They
can
modify
and
elucidating
the
stabilizing
role
that
proprioception
plays
in
producing
a
robust
crawling
phenotype.
A
A
A
A
So
this
is
the
head,
and
this
is
the
tail
and
there
are
ten
segments
here
and
so
each
of
these
segments-
and
I
think
we've
talked
about
hox
genes
where
there's
like
a
certain
hox
gene,
that's
assigned
to
a
certain
segment
and
that
they
give
rise
to
you
know
you
give
rise
to
these
10
segments
which
are
based
on
the
same
sort
of
template,
but
then
they
can
be
varied
through
evolution
or
through
whatever
hox
gene
is
being
expressed.
So
there's
some
variation
between
these
segments.
A
Although
typic,
you
know
they
all
come
from
sort
of
the
same
place
developmentally,
then
this
is
their
model
here
and
b
of
one
segment.
So
this
is
a
model
of
a
segment
where
there's
a
neural
controller.
There's
excitatory
input.
Then
you
have
this.
So
this
is
the
interior
segment,
which
is
the
the
segment
to
the
to
the
front.
A
A
What
we've
done,
there's
been
a
lot
of
work
done
on
this
in
c
elegans
in
the
open
worm
foundation,
we
have
the
cybernetic
project
and
they
kind
of
think
about
this
model
of
like
body
mechanics
in
terms
of
c
elegans,
and
a
lot
of
the
the
friction
forces
that
cleans
can
experience
and
then
c
is
this.
Larva
body
is
modeled
as
a
linear
chain
of
masses
connected
by
damped
linear
springs.
So
this
is
typical
of
by
a
neuromechanical
model
where
you
have
this
set
of
damp
springs
between
the
segments.
A
A
These
friction
forces
and
then
finally,
d
is
this:
is
the
segment
to
segment
propagation
of
neural
activity
and
they're
trying
to
model
this,
as
it
happens
through
neural
and
proprioceptive
couplings?
So
the
neural
couplings
are
this
w
e
n
here,
so
these
ease
these
circles
with
the
ease
in
them.
There's
a
connection
here,
w
e
p
and
w.
I
p.
These
are
the
proprioceptive
couplings,
so
this
is
coming
from
contraction
detection
from
so
you
have
motion,
you
have
this
contraction
detector,
you
have
this
muscle
drive.
A
The
muscle
drive
is
outputting
from
the
neural
systems
or
from
e
that
gives
you
know
muscles.
Like
some
signals
to
move.
You
have
contraction,
which
you
know
as
the
proprioceptors,
detecting
whether
the
muscle
is
moving
against
the
surface
and
then
that's
inputting
back
into
the
feeding
back
into
the
neurons
and
into
this
eye,
which
is
an
inhibitory
neuron
and
the
excitatory
inhibitory
neurons
in
this
segment
are
working
together.
There's
an
inhibition
going
on
from
the
feedback.
A
There's
excitation
feed
forwarding
from
this
neural
element
to
the
muscle
and
the
inhibitory
neuron,
and
then
there's
this
feed-forward
signal
from
one
segment's
excitatory
neuron
to
the
other,
and
then
there's
this
contraction
detector,
which
goes
to
the
anterior
neuron.
So
this
is
basically
their
model
for
modeling
movement
behavior
and
the
neural
neuron,
what
they
call
the
neuromechanics
of
it.
A
So
that's
their
basic
model
and
they
walk
through
the
the
math.
So
there's
a
lot
of
modeling,
that's
done
here.
They
use
some
pulse
and
cone
equations,
which
is
basically
a
you
know,
a
neural
model
which
accounts
for
excitatory
and
inhibitory
populations
of
neurons
in
a
segment.
They
use
this
over
phase
phase
oscillators.
A
The
right
answer
necessarily,
but
they
make
these
choices
based
on
the
literature
based
on
what
they're
trying
to
achieve
so
and
then
body
mechanics.
So
they
talk
about
passive
tissues
which
are
these
soft
tissues
and
I'm
hoping
that
susan
can
give
a
talk
on
this
soon
she's
she
mentioned
this
a
couple
weeks
ago.
I
don't
know,
but
soft
mechanics
and
soft
materials
are
really
interesting,
really
interesting
biological
implications
for
those
so
yeah.
A
A
I
hope
you've
enjoyed
this.
It
looks
like
everyone
had
to
leave.
Okay,
so
roswell
had
to
go.
Sanji
had
to
go
actually
had
to
go
okay,
so,
let's
see
yeah
so
sanjay
said
being
my
first
divaworm
meeting,
I
got
to
learn
about
interesting
research
works
of
the
community,
hoping
to
work
on
something
to
present
one
day
have
a
great
week,
so
have
a
great
week,
sanjay
and
usual
and
option
in
my
doc,
and
everyone
have
a
good
week
we'll
be
on
slack
and
on
github
and
hope
to
see
you
around.