Synchronization in spiking neural networks with short and long connections and time delays
Chaos An Interdisciplinary Journal of Nonlinear Science,
Год журнала:
2025,
Номер
35(1)
Опубликована: Янв. 1, 2025
Synchronization
is
fundamental
for
information
processing
in
oscillatory
brain
networks
and
strongly
affected
by
time
delays
via
signal
propagation
along
long
fibers.
Their
effect,
however,
less
evident
spiking
neural
given
the
discrete
nature
of
spikes.
To
bridge
gap
between
these
different
modeling
approaches,
we
study
synchronization
conditions,
dynamics
underlying
synchronization,
role
delay
a
two-dimensional
network
model
composed
adaptive
exponential
integrate-and-fire
neurons.
Through
parameter
exploration
neuronal
properties,
map
behavior
as
function
unidirectional
long-range
connection
microscopic
properties
demonstrate
that
principal
behaviors
comprise
standing
or
traveling
waves
activity
depend
on
noise
strength,
E/I
balance,
voltage
adaptation,
which
are
modulated
connection.
Our
results
show
interplay
micro-
(single
neuron
properties),
meso-
(connectivity
composition
network),
macroscopic
(long-range
connectivity)
parameters
emergent
spatiotemporal
brain.
Язык: Английский
Enhancing cognitive abilities through transcutaneous auricular vagus nerve stimulation: Findings from prefrontal functional connectivity analysis and virtual brain simulation
NeuroImage,
Год журнала:
2025,
Номер
unknown, С. 121179 - 121179
Опубликована: Март 1, 2025
Язык: Английский
Symmetry breaking organizes the brain’s resting state manifold
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Дек. 30, 2024
Abstract
Spontaneously
fluctuating
brain
activity
patterns
that
emerge
at
rest
have
been
linked
to
the
brain’s
health
and
cognition.
Despite
detailed
descriptions
of
spatio-temporal
patterns,
our
understanding
their
generative
mechanism
is
still
incomplete.
Using
a
combination
computational
modeling
dynamical
systems
analysis
we
provide
mechanistic
description
formation
resting
state
manifold
via
network
connectivity.
We
demonstrate
symmetry
breaking
by
connectivity
creates
characteristic
flow
on
manifold,
which
produces
major
data
features
across
scales
imaging
modalities.
These
include
spontaneous
high-amplitude
co-activations,
neuronal
cascades,
spectral
cortical
gradients,
multistability,
functional
dynamics.
When
aggregated
hierarchies,
these
match
profiles
from
empirical
data.
The
fundamental
for
construction
task-specific
flows
manifolds
used
in
theories
function.
In
addition,
it
shifts
focus
single
recordings
towards
capacity
generate
certain
dynamics
pathology.
Язык: Английский
Analyzing the Brain’s Dynamic Response to Targeted Stimulation using Generative Modeling
Network Neuroscience,
Год журнала:
2024,
Номер
9(1), С. 237 - 258
Опубликована: Дек. 2, 2024
Generative
models
of
brain
activity
have
been
instrumental
in
testing
hypothesized
mechanisms
underlying
dynamics
against
experimental
datasets.
Beyond
capturing
the
key
spontaneous
dynamics,
these
hold
an
exciting
potential
for
understanding
evoked
by
targeted
stimulation
techniques.
This
paper
delves
into
this
emerging
application,
using
concepts
from
dynamical
systems
theory
to
argue
that
stimulus-evoked
such
experiments
may
be
shaped
new
types
distinct
those
dominate
dynamics.
We
review
and
discuss
(a)
techniques
across
spatial
scales
can
both
perturb
novel
states
resolve
its
relaxation
trajectory
back
(b)
how
we
understand
terms
physiological,
phenomenological,
data-driven
models.
A
tight
integration
with
generative
quantitative
modeling
provides
important
opportunity
uncover
are
difficult
detect
settings.
Язык: Английский
Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity
PLoS Computational Biology,
Год журнала:
2024,
Номер
20(12), С. e1012693 - e1012693
Опубликована: Дек. 27, 2024
Treatment
for
major
depressive
disorder
(depression)
often
has
partial
efficacy
and
a
large
portion
of
patients
are
treatment
resistant.
Recent
studies
implicate
reduced
somatostatin
(SST)
interneuron
inhibition
in
depression,
new
pharmacology
boosting
this
via
positive
allosteric
modulators
α5-GABA
A
receptors
(α5-PAM)
offers
promising
effective
treatment.
However,
testing
the
effect
α5-PAM
on
human
brain
activity
is
limited,
meriting
use
detailed
simulations.
We
utilized
our
previous
computational
models
depression
microcircuits
with
SST
effects,
to
simulate
EEG
individual
across
severity
doses.
developed
machine
learning
that
predicted
optimal
dose
from
high
accuracy
recovered
microcircuit
EEG.
This
study
provides
prediction
administration
based
biomarkers
severity.
Given
limitations
doing
above
living
brain,
results
tools
we
will
facilitate
translation
clinical
use.
Язык: Английский