À l’origine du vivant, la différence sans hiérarchie(s)
médecine/sciences,
Journal Year:
2025,
Volume and Issue:
41(3), P. 282 - 285
Published: March 1, 2025
Toward a probabilistic definition of neural cell types
Current Opinion in Neurobiology,
Journal Year:
2025,
Volume and Issue:
92, P. 103035 - 103035
Published: May 6, 2025
A
classical
view
of
cell
type
relies
on
a
definite
set
stable
properties
that
are
critical
for
brain
functions.
Single-cell
technologies
led
to
an
extensive
multimodal
characterization
nervous
systems
and
perhaps
achieved
one
Santiago
Ramón
y
Cajal's
dreams:
unveil
comprehensive
the
composition.
While
global
analyses
structures
highlight
degree
mesoscale
stereotypy,
finer-scale
resolution
composition
shows
significant
variance
in
essential
neural
cellular
phenotypes,
including
morphology,
gene
expression,
electrophysiology,
connectivity.
This
highlights
need
novel
conceptualization
definition
"cell
type."
The
challenge
modern
classification
is
thus
integrate
various
distinct
into
unifying
descriptor.
Language: Английский
Tile by tile: capturing the evolutionary mosaic of human conditions
Current Opinion in Genetics & Development,
Journal Year:
2024,
Volume and Issue:
90, P. 102297 - 102297
Published: Dec. 19, 2024
Language: Английский
Stochastic Wiring of Cell Types Enhances Fitness by Generating Phenotypic Variability
Divyansha Lachi,
No information about this author
Ann Huang,
No information about this author
Augustine N. Mavor-Parker
No information about this author
et al.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Aug. 8, 2024
Abstract
The
development
of
neural
connectivity
is
a
crucial
biological
process
that
gives
rise
to
diverse
brain
circuits
and
behaviors.
Neural
stochastic
process,
but
this
stochasticity
often
treated
as
nuisance
overcome
rather
than
functional
advantage.
Here
we
use
computational
model,
in
which
connection
probabilities
between
discrete
cell
types
are
genetically
specified,
investigate
the
benefits
wiring.
We
show
model
can
be
viewed
generalization
powerful
class
artificial
networks—Bayesian
networks—where
each
network
parameter
sample
from
distribution.
Our
results
reveal
confers
greater
benefit
large
networks
variable
environments,
may
explain
its
role
organisms
with
larger
brains.
Surprisingly,
find
average
fitness
over
population
agents
higher
single
agent
defined
by
probability.
reveals
how
developmental
stochasticity,
inducing
form
non-heritable
phenotypic
variability,
increase
probability
at
least
some
individuals
will
survive
rapidly
changing,
unpredictable
environments.
suggest
an
important
feature
bug
development.
Language: Английский