bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2022,
Номер
unknown
Опубликована: Март 29, 2022
Abstract
Synaptic
changes
underlie
learning
and
memory
formation
in
the
brain.
But
synaptic
plasticity
of
excitatory
synapses
on
its
own
is
unstable,
leading
to
unlimited
growth
strengths
without
additional
homeostatic
mechanisms.
To
control
we
propose
a
novel
form
at
inhibitory
synapses.
We
identify
two
key
features
plasticity,
dominance
inhibition
over
excitation
nonlinear
dependence
firing
rate
postsynaptic
neurons
whereby
change
same
direction
as
strengths.
demonstrate
that
stable
realized
by
this
achieve
fixed
excitatory/inhibitory
set-point
agreement
with
experimental
results.
Applying
disinhibitory
signal
can
gate
lead
generation
receptive
fields
strong
bidirectional
connectivity
recurrent
network.
Hence,
simultaneously
stabilize
enable
upon
disinhibition.
PLoS Computational Biology,
Год журнала:
2022,
Номер
18(12), С. e1010682 - e1010682
Опубликована: Дек. 2, 2022
Synaptic
changes
are
hypothesized
to
underlie
learning
and
memory
formation
in
the
brain.
But
Hebbian
synaptic
plasticity
of
excitatory
synapses
on
its
own
is
unstable,
leading
either
unlimited
growth
strengths
or
silencing
neuronal
activity
without
additional
homeostatic
mechanisms.
To
control
strengths,
we
propose
a
novel
form
at
inhibitory
synapses.
Using
computational
modeling,
suggest
two
key
features
plasticity,
dominance
inhibition
over
excitation
nonlinear
dependence
firing
rate
postsynaptic
neurons
whereby
change
with
same
sign
(potentiate
depress)
as
strengths.
We
demonstrate
that
stable
realized
by
this
model
affects
excitatory/inhibitory
weight
ratios
agreement
experimental
results.
Applying
disinhibitory
signal
can
gate
lead
generation
receptive
fields
strong
bidirectional
connectivity
recurrent
network.
Hence,
simultaneously
stabilize
enable
upon
disinhibition.
Journal of Neuroscience,
Год журнала:
2024,
Номер
44(14), С. e1894232024 - e1894232024
Опубликована: Фев. 13, 2024
Human
listeners
possess
an
innate
capacity
to
discern
patterns
within
rapidly
unfolding
sensory
input.
Core
questions,
guiding
ongoing
research,
focus
on
the
mechanisms
through
which
these
representations
are
acquired
and
whether
brain
prioritizes
or
suppresses
predictable
signals.
Previous
work,
using
fast
auditory
sequences
(tone-pips
presented
at
a
rate
of
20
Hz),
revealed
sustained
response
effects
that
appear
track
dynamic
predictability
sequence.
Here,
we
extend
investigation
slower
(4
permitting
isolation
responses
individual
tones.
Stimuli
were
50
ms
tone-pips,
ordered
into
random
(RND)
regular
(REG;
repeating
pattern
10
frequencies)
sequences;
Two
timing
profiles
created:
in
“fast”
sequences,
tone-pips
direct
succession
(20
Hz);
“slow”
separated
by
200
silent
gap
Hz).
Naive
participants
(
N
=
22;
both
sexes)
passively
listened
while
recorded
magnetoencephalography
(MEG).
Results
unveiled
heightened
magnitude
REG
when
compared
RND
patterns.
This
manifested
from
three
tones
after
onset
repetition,
even
context
characterized
extended
durations
(2,500
ms).
observation
underscores
remarkable
implicit
sensitivity
acoustic
regularities.
Importantly,
evoked
single
exhibited
opposite
pattern—stronger
than
sequences.
The
demonstration
simultaneous
but
opposing
reveals
concurrent
processes
shape
representation
Frontiers in Computational Neuroscience,
Год журнала:
2022,
Номер
16
Опубликована: Июль 4, 2022
While
it
is
universally
accepted
that
the
brain
makes
predictions,
there
little
agreement
about
how
this
accomplished
and
under
which
conditions.
Accurate
prediction
requires
neural
circuits
to
learn
store
spatiotemporal
patterns
observed
in
natural
environment,
but
not
obvious
such
information
should
be
stored,
or
encoded.
Information
theory
provides
a
mathematical
formalism
can
used
measure
efficiency
utility
of
different
coding
schemes
for
data
transfer
storage.
This
shows
codes
become
efficient
when
they
remove
predictable,
redundant
spatial
temporal
information.
Efficient
has
been
understand
retinal
computations
may
also
relevant
understanding
more
complicated
processing
visual
cortex.
However,
literature
on
cortex
varied
confusing
since
same
terms
are
mean
things
experimental
theoretical
contexts.
In
work,
we
attempt
provide
clear
summary
relationship
between
prediction,
review
evidence
principles
explain
retina.
We
then
apply
framework
occurring
early
visuocortical
areas,
arguing
from
rodents
largely
consistent
with
predictions
model.
Finally,
respond
criticisms
suggest
ways
might
design
future
experiments,
particular
focus
extent
make
representations
environmental
statistics.
Communications Biology,
Год журнала:
2023,
Номер
6(1)
Опубликована: Окт. 19, 2023
The
relative
importance
or
saliency
of
sensory
inputs
depend
on
the
animal's
environmental
context
and
behavioural
responses
to
these
same
can
vary
over
time.
Here
we
show
how
freely
moving
rats,
trained
discriminate
between
deviant
tones
embedded
in
a
regular
pattern
repeating
stimuli
different
variations
classic
oddball
paradigm,
detect
tones,
this
discriminability
resembles
properties
that
are
typical
neuronal
adaptation
described
previous
studies.
Moreover,
auditory
brainstem
response
(ABR)
latency
decreases
after
training,
finding
consistent
with
notion
animals
develop
type
plasticity
stimuli.
Our
study
suggests
existence
form
long-term
memory
may
modulate
level
according
its
relevance,
sets
ground
for
future
experiments
will
help
disentangle
functional
mechanisms
govern
habituation
relation
adaptation.
Biological
memory
networks
are
thought
to
store
information
by
experience-dependent
changes
in
the
synaptic
connectivity
between
assemblies
of
neurons.
Recent
models
suggest
that
these
contain
both
excitatory
and
inhibitory
neurons
(E/I
assemblies),
resulting
co-tuning
precise
balance
excitation
inhibition.
To
understand
computational
consequences
E/I
under
biologically
realistic
constraints
we
built
a
spiking
network
model
based
on
experimental
data
from
telencephalic
area
Dp
adult
zebrafish,
precisely
balanced
recurrent
homologous
piriform
cortex.
We
found
stabilized
firing
rate
distributions
compared
with
global
Unlike
classical
models,
did
not
show
discrete
attractor
dynamics.
Rather,
responses
learned
inputs
were
locally
constrained
onto
manifolds
‘focused’
activity
into
neuronal
subspaces.
The
covariance
structure
supported
pattern
classification
when
was
retrieved
selected
subsets.
Networks
therefore
transformed
geometry
coding
space,
continuous
representations
reflected
relatedness
an
individual’s
experience.
Such
enable
fast
classification,
can
support
continual
learning,
may
provide
basis
for
higher-order
learning
cognitive
computations.
Biomedicines,
Год журнала:
2025,
Номер
13(2), С. 460 - 460
Опубликована: Фев. 13, 2025
Cerebral
plasticity
is
the
ability
of
brain
to
change
and
adapt
in
response
experience
or
learning.
Its
hallmarks
are
developmental
flexibility,
complex
interactions
between
genetic
environmental
influences,
structural-functional
changes
comprising
neurogenesis,
axonal
sprouting,
synaptic
remodeling.
Studies
on
have
important
practical
implications.
The
molecular
characteristics
may
reveal
disease
course
rehabilitative
potential
patient.
Neurological
disorders
linked
with
numerous
cerebral
non-coding
RNAs
(ncRNAs),
particular,
microRNAs;
discovery
their
essential
role
gene
regulation
was
recently
recognized
awarded
a
Nobel
Prize
Physiology
Medicine
2024.
Herein,
we
review
association
its
homeostasis
ncRNAs,
which
make
them
putative
targets
for
RNA-based
diagnostics
therapeutics.
New
insight
into
concept
provide
additional
perspectives
functional
recovery
following
damage.
Knowledge
this
phenomenon
will
enable
physicians
exploit
regulate
eloquent
networks
timely
interventions.
Future
studies
pathophysiological
mechanisms
at
macro-
microscopic
levels
advance
rehabilitation
strategies
improve
quality
life
patients
neurological
diseases.
PLoS Computational Biology,
Год журнала:
2025,
Номер
21(4), С. e1012910 - e1012910
Опубликована: Апрель 24, 2025
Synaptic
plasticity
is
a
key
player
in
the
brain’s
life-long
learning
abilities.
However,
due
to
experimental
limitations,
mechanistic
link
between
synaptic
rules
and
network-level
computations
they
enable
remain
opaque.
Here
we
use
evolutionary
strategies
(ES)
meta
learn
local
co-active
large
recurrent
spiking
networks
with
excitatory
(E)
inhibitory
(I)
neurons,
using
parameterizations
of
increasing
complexity.
We
discover
that
robustly
stabilize
network
dynamics
for
all
four
synapse
types
acting
isolation
(E-to-E,
E-to-I,
I-to-E
I-to-I).
More
complex
functions
such
as
familiarity
detection
can
also
be
included
search
constraints.
our
strategy
begins
fail
complexity,
it
challenging
devise
loss
effectively
constrain
plausible
solutions
priori
.
Moreover,
line
previous
work,
find
multiple
degenerate
identical
behaviour.
As
optimization
strategy,
ES
provides
one
solution
at
time
makes
exploration
this
degeneracy
cumbersome.
Regardless,
glean
interdependecies
various
parameters
by
considering
covariance
matrix
learned
alongside
optimal
rule
ES.
Our
work
proof
principle
success
machine-learning-guided
discovery
networks,
points
necessity
more
elaborate
going
forward.
To
rapidly
process
information,
neural
circuits
have
to
amplify
specific
activity
patterns
transiently.
How
the
brain
performs
this
nonlinear
operation
remains
elusive.
Hebbian
assemblies
are
one
possibility
whereby
strong
recurrent
excitatory
connections
boost
neuronal
activity.
However,
such
amplification
is
often
associated
with
dynamical
slowing
of
network
dynamics,
non-transient
attractor
states,
and
pathological
run-away
Feedback
inhibition
can
alleviate
these
effects
but
typically
linearizes
responses
reduces
gain.
Here,
we
study
transient
(NTA),
a
plausible
alternative
mechanism
that
reconciles
excitation
rapid
while
avoiding
above
issues.
NTA
has
two
distinct
temporal
phases.
Initially,
positive
feedback
selectively
amplifies
inputs
exceed
critical
threshold.
Subsequently,
short-term
plasticity
quenches
dynamics
into
an
inhibition-stabilized
state.
By
characterizing
in
supralinear
models,
establish
resulting
onset
transients
stimulus
selective
well-suited
for
speedy
information
processing.
Further,
find
excitatory-inhibitory
co-tuning
widens
parameter
regime
which
possible
absence
persistent
In
summary,
provides
parsimonious
explanation
how
collaborate
networks
achieve
amplification.