Sound elicits stereotyped facial movements that provide a sensitive index of hearing abilities in mice
Current Biology,
Год журнала:
2024,
Номер
34(8), С. 1605 - 1620.e5
Опубликована: Март 15, 2024
Язык: Английский
Analysis methods for large-scale neuronal recordings
Science,
Год журнала:
2024,
Номер
386(6722)
Опубликована: Ноя. 7, 2024
Simultaneous
recordings
from
hundreds
or
thousands
of
neurons
are
becoming
routine
because
innovations
in
instrumentation,
molecular
tools,
and
data
processing
software.
Such
can
be
analyzed
with
science
methods,
but
it
is
not
immediately
clear
what
methods
to
use
how
adapt
them
for
neuroscience
applications.
We
review,
categorize,
illustrate
diverse
analysis
neural
population
describe
these
have
been
used
make
progress
on
longstanding
questions
neuroscience.
review
a
variety
approaches,
ranging
the
mathematically
simple
complex,
exploratory
hypothesis-driven,
recently
developed
more
established
methods.
also
some
common
statistical
pitfalls
analyzing
large-scale
data.
Язык: Английский
HIPPIE: A Multimodal Deep Learning Model for Electrophysiological Classification of Neurons
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2025,
Номер
unknown
Опубликована: Март 15, 2025
Abstract
Extracellular
electrophysiological
recordings
present
unique
computational
challenges
for
neuronal
classification
due
to
noise,
technical
variability,
and
batch
effects
across
experimental
systems.
We
introduce
HIPPIE
(High-dimensional
Interpretation
of
Physiological
Patterns
In
recordings),
a
deep
learning
framework
that
combines
self-supervised
pretraining
on
unlabeled
datasets
with
supervised
fine-tuning
classify
neurons
from
extracellular
recordings.
Using
conditional
convolutional
joint
autoencoders,
learns
robust,
technology-adjusted
representations
waveforms
spiking
dynamics.
This
model
can
be
applied
clustering
diverse
biological
cultures
technologies.
validated
both
in
vivo
mouse
vitro
brain
slices,
where
it
demonstrated
superior
performance
over
other
unsupervised
methods
cell-type
discrimination
aligned
closely
anatomically
defined
classes.
Its
latent
space
organizes
along
gradients,
while
enabling
individual
corrected
alignment
experiments.
establishes
general
systematically
decoding
diversity
native
engineered
Язык: Английский
Modeling neural coding in the auditory brain with high resolution and accuracy
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 19, 2024
Computational
models
of
auditory
processing
can
be
valuable
tools
for
research
and
technology
development.
Models
the
cochlea
are
highly
accurate
widely
used,
but
brain
lag
far
behind
in
both
performance
penetration.
Here,
we
present
ICNet,
a
model
that
provides
simulation
neural
dynamics
inferior
colliculus
across
wide
range
sounds,
including
near-perfect
responses
to
speech.
We
developed
ICNet
using
deep
learning
large-scale
intracranial
recordings
from
gerbils,
addressing
three
key
modeling
challenges
common
all
sensory
systems:
capturing
full
statistical
complexity
neuronal
response
patterns;
accounting
physiological
experimental
non-stationarity;
extracting
features
different
brains.
used
simulate
activity
thousands
units
or
provide
compact
representation
central
through
its
latent
dynamics,
facilitating
hearing
audio
applications.
Язык: Английский