The
Drosophila
larva
is
extensively
used
as
model
organism
in
neuroethological
studies
where
precise
behavioral
tracking
enables
the
statistical
analysis
of
individual
and
population-level
metrics
that
can
inform
mathematical
models
larval
behavior.
Here,
we
propose
a
hierarchical
architecture
comprising
three
layers
to
facilitate
modular
construction,
closed-loop
simulations,
direct
comparisons
between
empirical
simulated
data.
At
basic
layer,
autonomous
locomotory
capable
performing
exploration.
Based
on
novel
kinematic
analyses
our
features
intermittent
forward
crawling
phasically
coupled
lateral
bending.
second
navigation
achieved
via
active
sensing
environment
top-down
modulation
locomotion.
top
adaptation
entails
associative
learning.
We
evaluate
virtual
behavior
across
agent-based
simulations
free
exploration,
chemotaxis,
odor
preference
testing.
Our
ideally
suited
for
combination
neuromechanical,
neural
or
mere
components,
facilitating
their
evaluation,
comparison,
extension
integration
into
multifunctional
control
architectures.
Flexible
behaviors
over
long
timescales
are
thought
to
engage
recurrent
neural
networks
in
deep
brain
regions,
which
experimentally
challenging
study.
In
insects,
circuit
dynamics
a
region
called
the
central
complex
(CX)
enable
directed
locomotion,
sleep,
and
context-
experience-dependent
spatial
navigation.
We
describe
first
complete
electron
microscopy-based
connectome
of
How
insects
navigate
complex
odor
plumes,
where
the
location
and
timing
of
packets
are
uncertain,
remains
unclear.
Here
we
imaged
plumes
simultaneously
with
freely-walking
flies,
quantifying
how
behavior
is
shaped
by
encounters
individual
packets.
We
found
that
navigation
was
stochastic
did
not
rely
on
continuous
modulation
speed
or
orientation.
Instead,
flies
turned
stochastically
stereotyped
saccades,
whose
direction
biased
upwind
prior
encounters,
while
magnitude
rate
saccades
remained
constant.
Further,
used
to
modulate
transition
rates
between
walks
stops.
In
more
regular
environments,
continuously
orientation,
even
though
can
still
occur
randomly
due
animal
motion.
find
in
less
predictable
random
both
space
time,
walking
encounter
timing.
Journal of Comparative Physiology A,
Год журнала:
2023,
Номер
209(4), С. 467 - 488
Опубликована: Янв. 20, 2023
Abstract
Using
odors
to
find
food
and
mates
is
one
of
the
most
ancient
highly
conserved
behaviors.
Arthropods
from
flies
moths
crabs
use
broadly
similar
strategies
navigate
toward
odor
sources—such
as
integrating
flow
information
with
information,
comparing
concentration
across
sensors,
over
time.
Because
arthropods
share
many
homologous
brain
structures—antennal
lobes
for
processing
olfactory
mechanosensors
flow,
mushroom
bodies
(or
hemi-ellipsoid
bodies)
associative
learning,
central
complexes
navigation,
it
likely
that
these
closely
related
behaviors
are
mediated
by
neural
circuits.
However,
differences
in
types
they
seek,
physics
dispersal,
locomotion
water,
air,
on
substrates
mean
circuits
must
have
adapted
generate
a
wide
diversity
odor-seeking
In
this
review,
we
discuss
common
specializations
observed
navigation
behavior
arthropods,
review
our
current
knowledge
about
subserving
behavior.
We
propose
comparative
study
arthropod
nervous
systems
may
provide
insight
into
how
set
basic
circuit
structures
has
diversified
different
environments.
Nature Communications,
Год журнала:
2025,
Номер
16(1)
Опубликована: Янв. 28, 2025
To
ensure
their
survival,
animals
must
be
able
to
respond
adaptively
threats
within
environment.
However,
the
precise
neural
circuit
mechanisms
that
underlie
flexible
defensive
behaviors
remain
poorly
understood.
Using
neuronal
manipulations,
machine
learning-based
behavioral
detection,
electron
microscopy
(EM)
connectomics
and
calcium
imaging
in
Drosophila
larvae,
we
map
second-order
interneurons
are
differentially
involved
competition
between
actions
response
competing
aversive
cues.
We
find
mechanosensory
stimulation
inhibits
escape
favor
of
startle
by
influencing
activity
escape-promoting
interneurons.
Stronger
activation
those
neurons
startle-like
behaviors.
This
suggests
occurs
at
level
Finally,
identify
a
pair
descending
promote
could
modulate
sequence.
Taken
together,
these
results
characterize
pathways
competition,
which
is
modulated
sensory
context.
Frontiers in Neural Circuits,
Год журнала:
2020,
Номер
14
Опубликована: Ноя. 9, 2020
The
diversity
and
dense
interconnectivity
of
cells
in
the
nervous
system
present
a
huge
challenge
to
understanding
how
brains
work.
Recent
progress
toward
such
understanding,
however,
has
been
fueled
by
development
techniques
for
selectively
monitoring
manipulating
function
distinct
cell
types—and
even
individual
neurons—in
living
animals.
These
sophisticated
are
fundamentally
genetic
have
found
their
greatest
application
model
organisms,
as
fruit
fly
Drosophila
melanogaster.
combines
tractability
with
compact,
but
cell-type
rich,
incubator
variety
methods
neuronal
targeting.
One
method,
called
Split
Gal4,
is
playing
an
increasingly
important
role
mapping
neural
circuits
fly.
In
conjunction
functional
perturbations
behavioral
screens,
Gal4
used
characterize
governing
activities
grooming,
aggression,
mating.
It
also
leveraged
comprehensively
map
functionally
composing
brain
regions,
central
complex,
lateral
horn,
mushroom
body—the
latter
being
insect
seat
learning
memory.
With
connectomics
data
emerging
both
larval
adult
Drosophila,
poised
play
characterizing
neurons
interest
based
on
connectivity.
We
summarize
history
current
state
method
indicate
promising
areas
further
or
future
application.
Current Opinion in Neurobiology,
Год журнала:
2020,
Номер
65, С. 129 - 137
Опубликована: Ноя. 23, 2020
The
larva
of
Drosophila
melanogaster
is
emerging
as
a
powerful
model
system
for
comprehensive
brain-wide
understanding
the
circuit
implementation
neural
computations.
With
an
unprecedented
amount
tools
in
hand,
including
synaptic-resolution
connectomics,
whole-brain
imaging,
and
genetic
selective
targeting
single
neuron
types,
it
possible
to
dissect
which
circuits
computations
are
at
work
behind
behaviors
that
have
interesting
level
complexity.
Here
we
present
some
recent
advances
regarding
multisensory
integration,
learning,
action
selection
larva.