PLoS Computational Biology,
Год журнала:
2021,
Номер
17(9), С. e1009439 - e1009439
Опубликована: Сен. 22, 2021
Recent
neuroscience
studies
demonstrate
that
a
deeper
understanding
of
brain
function
requires
behavior.
Detailed
behavioral
measurements
are
now
often
collected
using
video
cameras,
resulting
in
an
increased
need
for
computer
vision
algorithms
extract
useful
information
from
data.
Here
we
introduce
new
analysis
tool
combines
the
output
supervised
pose
estimation
(e.g.
DeepLabCut)
with
unsupervised
dimensionality
reduction
methods
to
produce
interpretable,
low-dimensional
representations
videos
more
than
estimates
alone.
We
this
by
extracting
interpretable
features
three
different
head-fixed
mouse
preparations,
as
well
freely
moving
open
field
arena,
and
show
how
these
can
facilitate
downstream
neural
analyses.
also
produced
our
model
improve
precision
interpretation
analyses
compared
outputs
either
fully
or
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Июль 4, 2023
The
neural
representations
of
prior
information
about
the
state
world
are
poorly
understood.
To
investigate
them,
we
examined
brain-wide
Neuropixels
recordings
and
widefield
calcium
imaging
collected
by
International
Brain
Laboratory.
Mice
were
trained
to
indicate
location
a
visual
grating
stimulus,
which
appeared
on
left
or
right
with
probability
alternating
between
0.2
0.8
in
blocks
variable
length.
We
found
that
mice
estimate
this
thereby
improve
their
decision
accuracy.
Furthermore,
report
subjective
is
encoded
at
least
20%
30%
brain
regions
which,
remarkably,
span
all
levels
processing,
from
early
sensory
areas
(LGd,
VISp)
motor
(MOs,
MOp,
GRN)
high
level
cortical
(ACAd,
ORBvl).
This
widespread
representation
consistent
model
Bayesian
inference
involving
loops
areas,
as
opposed
incorporated
only
decision-making
areas.
study
offers
first
perspective
encoding
cellular
resolution,
underscoring
importance
using
large
scale
single
standardized
task.
Neuron,
Год журнала:
2022,
Номер
110(18), С. 2961 - 2969.e5
Опубликована: Авг. 12, 2022
Parietal
cortex
is
implicated
in
a
variety
of
behavioral
processes,
but
it
unknown
whether
and
how
its
individual
neurons
participate
multiple
tasks.
We
trained
head-fixed
mice
to
perform
two
visual
decision
tasks
involving
steering
wheel
or
virtual
T-maze
recorded
from
the
same
parietal
during
these
Neurons
that
were
active
task
typically
inactive
steering-wheel
vice
versa.
Recording
apparatus
without
stimuli
yielded
specificity
as
task,
suggesting
depends
on
physical
context.
To
confirm
this,
we
some
third
combining
context
with
environment
T-maze.
This
hybrid
engaged
those
task.
Thus,
participation
by
mouse
specific,
this
determined
Head-fixed
behavioral
experiments
in
rodents
permit
unparalleled
experimental
control,
precise
measurement
of
behavior,
and
concurrent
modulation
neural
activity.
Here,
we
present
OHRBETS
(Open-Source
Rodent
Behavioral
Experimental
Training
System;
pronounced
'Orbitz'),
a
low-cost,
open-source
platform
hardware
software
to
flexibly
pursue
the
basis
variety
motivated
behaviors.
mice
tested
with
displayed
operant
conditioning
for
caloric
reward
that
replicates
core
phenotypes
observed
during
freely
moving
conditions.
also
permits
optogenetic
intracranial
self-stimulation
under
positive
or
negative
procedures
real-time
place
preference
like
assays.
In
multi-spout
brief-access
consumption
task,
licking
as
function
concentration
sucrose,
quinine,
sodium
chloride,
modulated
by
homeostatic
circadian
influences.
Finally,
highlight
functionality
OHRBETS,
measured
mesolimbic
dopamine
signals
task
display
strong
correlations
relative
solution
value
magnitude
consumption.
All
designs,
programs,
instructions
are
provided
online.
This
customizable
enables
replicable
consummatory
behaviors
can
be
incorporated
methods
perturb
record
dynamics
vivo.
Nature Communications,
Год журнала:
2024,
Номер
15(1)
Опубликована: Авг. 1, 2024
Abstract
Animals
likely
use
a
variety
of
strategies
to
solve
laboratory
tasks.
Traditionally,
combined
analysis
behavioral
and
neural
recording
data
across
subjects
employing
different
may
obscure
important
signals
give
confusing
results.
Hence,
it
is
essential
develop
techniques
that
can
infer
strategy
at
the
single-subject
level.
We
analyzed
an
experiment
in
which
two
male
monkeys
performed
visually
cued
rule-based
task.
The
their
performance
shows
no
indication
they
used
strategy.
However,
when
we
examined
geometry
stimulus
representations
state
space
activities
recorded
dorsolateral
prefrontal
cortex,
found
striking
differences
between
monkeys.
Our
purely
results
induced
us
reanalyze
behavior.
new
showed
representational
are
associated
with
reaction
times,
revealing
were
unaware
of.
All
these
analyses
suggest
using
strategies.
Finally,
recurrent
network
models
trained
perform
same
task,
show
correlate
amount
training,
suggesting
possible
explanation
for
observed
differences.
Nature Neuroscience,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 16, 2024
Abstract
Neurophysiology
has
long
progressed
through
exploratory
experiments
and
chance
discoveries.
Anecdotes
abound
of
researchers
listening
to
spikes
in
real
time
noticing
patterns
activity
related
ongoing
stimuli
or
behaviors.
With
the
advent
large-scale
recordings,
such
close
observation
data
become
difficult.
To
find
neural
data,
we
developed
‘Rastermap’,
a
visualization
method
that
displays
neurons
as
raster
plot
after
sorting
them
along
one-dimensional
axis
based
on
their
patterns.
We
benchmarked
Rastermap
realistic
simulations
then
used
it
explore
recordings
tens
thousands
from
mouse
cortex
during
spontaneous,
stimulus-evoked
task-evoked
epochs.
also
applied
whole-brain
zebrafish
recordings;
wide-field
imaging
data;
electrophysiological
rat
hippocampus,
monkey
frontal
various
cortical
subcortical
regions
mice;
artificial
networks.
Finally,
illustrate
high-dimensional
scenarios
where
similar
algorithms
cannot
be
effectively.
Nature Neuroscience,
Год журнала:
2024,
Номер
27(6), С. 1199 - 1210
Опубликована: Май 6, 2024
Abstract
Recent
work
has
argued
that
large-scale
neural
recordings
are
often
well
described
by
patterns
of
coactivation
across
neurons.
Yet
the
view
variability
is
constrained
to
a
fixed,
low-dimensional
subspace
may
overlook
higher-dimensional
structure,
including
stereotyped
sequences
or
slowly
evolving
latent
spaces.
Here
we
argue
task-relevant
in
data
can
also
cofluctuate
over
trials
time,
defining
distinct
‘covariability
classes’
co-occur
within
same
dataset.
To
demix
these
covariability
classes,
develop
sliceTCA
(slice
tensor
component
analysis),
new
unsupervised
dimensionality
reduction
method
for
tensors.
In
three
example
datasets,
motor
cortical
activity
during
classic
reaching
task
primates
and
recent
multiregion
mice,
show
capture
more
structure
using
fewer
components
than
traditional
methods.
Overall,
our
theoretical
framework
extends
population
incorporating
additional
classes
variables
capturing
structure.
Individual
choices
shape
life
course
trajectories
of
brain
structure
and
function
beyond
genes
environment.
We
hypothesized
that
individual
task
engagement
in
response
to
a
learning
program
results
individualized
biographies
connectomics.
Genetically
identical
female
mice
living
one
large
shared
enclosure
freely
engaged
self-paced,
automatically
administered
monitored
tasks.
discovered
growing
increasingly
stable
interindividual
differences
trajectories.
Adult
hippocampal
neurogenesis
connectivity
as
assessed
by
high-density
multielectrode
array
positively
correlated
with
the
variation
exploration
efficiency.
During
some
tasks,
divergence
transiently
collapsed,
highlighting
sustained
significance
context
for
individualization.
Thus,
equal
environments
do
not
result
because
confronts
individuals
lead
divergent
paths.
Electrophysiology
has
proven
invaluable
to
record
neural
activity,
and
the
development
of
Neuropixels
probes
dramatically
increased
number
recorded
neurons.
These
are
often
implanted
acutely,
but
acute
recordings
cannot
be
performed
in
freely
moving
animals
neurons
tracked
across
days.
To
study
key
behaviors
such
as
navigation,
learning,
memory
formation,
must
chronically.
An
ideal
chronic
implant
should
(1)
allow
stable
for
weeks;
(2)
reuse
after
explantation;
(3)
light
enough
use
mice.
Here,
we
present
“Apollo
Implant”,
an
open-source
editable
device
that
meets
these
criteria
accommodates
up
two
1.0
or
2.0
probes.
The
comprises
a
“payload”
module
which
is
attached
probe
recoverable,
“docking”
cemented
skull.
design
adjustable,
making
it
easy
change
distance
between
probes,
angle
insertion,
depth
insertion.
We
tested
eight
labs
head-fixed
mice,
rats.
days
was
stable,
even
repeated
implantations
same
probe.
Apollo
provides
inexpensive,
lightweight,
flexible
solution
reusable
recordings.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2022,
Номер
unknown
Опубликована: Май 9, 2022
Abstract
Understanding
brain
function
relies
on
the
collective
work
of
many
labs
generating
reproducible
results.
However,
reproducibility
has
not
been
systematically
assessed
within
context
electrophysiological
recordings
during
cognitive
behaviors.
To
address
this,
we
formed
a
multi-lab
collaboration
using
shared,
open-source
behavioral
task
and
experimental
apparatus.
Experimenters
in
ten
laboratories
repeatedly
targeted
Neuropixels
probes
to
same
location
(spanning
secondary
visual
areas,
hippocampus,
thalamus)
mice
making
decisions;
this
generated
total
121
replicates,
unique
dataset
for
evaluating
electrophysiology
experiments.
Despite
standardizing
both
procedures,
some
outcomes
were
highly
variable.
A
closer
analysis
uncovered
that
variability
electrode
targeting
hindered
reproducibility,
as
did
limited
statistical
power
routinely
used
analyses,
such
single-neuron
tests
modulation
by
parameters.
Reproducibility
was
enhanced
histological
quality-control
criteria.
Our
observations
suggest
data
from
systems
neuroscience
is
vulnerable
lack
but
across-lab
standardization,
including
metrics
propose,
can
serve
mitigate
this.