bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 5, 2024
Abstract
To
model
the
dynamics
of
neuron
membrane
excitability
many
models
can
be
considered,
from
most
biophysically
detailed
to
highest
level
phenomenological
description.
Recent
works
at
single
have
shown
importance
taking
into
account
evolution
slow
variables
such
as
ionic
concentration.
A
reduction
a
integrate-and-fire
family
is
interesting
then
go
large
network
models.
In
this
paper,
we
introduce
way
consider
impairment
regulation
by
adding
third,
slow,
variable
adaptive
Exponential
(AdEx).
We
implement
and
simulate
including
model.
find
that
was
able
generate
normal
epileptic
discharges.
This
should
useful
for
design
simulations
pathological
states.
arXiv (Cornell University),
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 1, 2023
A
pervasive
challenge
in
neuroscience
is
testing
whether
neuronal
connectivity
changes
over
time
due
to
specific
causes,
such
as
stimuli,
events,
or
clinical
interventions.
Recent
hardware
innovations
and
falling
data
storage
costs
enable
longer,
more
naturalistic
recordings.
The
implicit
opportunity
for
understanding
the
self-organised
brain
calls
new
analysis
methods
that
link
temporal
scales:
from
order
of
milliseconds
which
dynamics
evolve,
minutes,
days,
even
years
experimental
observations
unfold.
This
review
article
demonstrates
how
hierarchical
generative
models
Bayesian
inference
help
characterise
activity
across
different
scales.
Crucially,
these
go
beyond
describing
statistical
associations
among
about
underlying
mechanisms.
We
offer
an
overview
fundamental
concepts
state-space
modeling
suggest
a
taxonomy
methods.
Additionally,
we
introduce
key
mathematical
principles
underscore
separation
scales,
slaving
principle,
are
being
used
test
hypotheses
with
multiscale
data.
hope
this
will
serve
useful
primer
computational
neuroscientists
on
state
art
current
directions
travel
complex
systems
modelling
literature.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Дек. 10, 2023
Abstract
The
study
of
brain
activity
spans
diverse
scales
and
levels
description,
requires
the
development
computational
models
alongside
experimental
investigations
to
explore
integrations
across
scales.
high
dimensionality
spiking
networks
presents
challenges
for
understanding
their
dynamics.
To
tackle
this,
a
mean-field
formulation
offers
potential
approach
reduction
while
retaining
essential
elements.
Here,
we
focus
on
previously
developed
model
Adaptive
Exponential
(AdEx)
networks,
utilized
in
various
research
works.
We
provide
systematic
investigation
its
properties
bifurcation
structure,
which
was
not
available
this
model.
show
that
provides
comprehensive
description
characterization
assist
future
users
interpreting
results.
methodology
includes
construction,
stability
analysis,
numerical
simulations.
Finally,
offer
an
overview
dynamical
methods
characterize
model,
should
be
useful
other
models.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 5, 2024
Abstract
To
model
the
dynamics
of
neuron
membrane
excitability
many
models
can
be
considered,
from
most
biophysically
detailed
to
highest
level
phenomenological
description.
Recent
works
at
single
have
shown
importance
taking
into
account
evolution
slow
variables
such
as
ionic
concentration.
A
reduction
a
integrate-and-fire
family
is
interesting
then
go
large
network
models.
In
this
paper,
we
introduce
way
consider
impairment
regulation
by
adding
third,
slow,
variable
adaptive
Exponential
(AdEx).
We
implement
and
simulate
including
model.
find
that
was
able
generate
normal
epileptic
discharges.
This
should
useful
for
design
simulations
pathological
states.
