Next generation neural population models
Frontiers in Applied Mathematics and Statistics,
Journal Year:
2023,
Volume and Issue:
9
Published: Feb. 28, 2023
Low-dimensional
neural
mass
models
are
often
invoked
to
model
the
coarse-grained
activity
of
large
populations
neurons
and
synapses
have
been
used
help
understand
coordination
scale
brain
rhythms.
However,
they
phenomenological
in
nature
and,
although
motivated
by
neurobiological
considerations,
absence
a
direct
link
an
underlying
biophysical
reality
is
weakness
that
means
may
not
be
best
suited
capturing
some
rich
behaviors
seen
real
neuronal
tissue.
In
this
perspective
article
I
discuss
simple
spiking
neuron
network
has
recently
shown
admit
exact
mean-field
description
for
synaptic
interactions.
This
many
features
coupled
additional
dynamical
equation
describes
evolution
population
synchrony.
next
generation
ideally
understanding
patterns
ubiquitously
neuroimaging
recordings.
Here
review
equations,
way
which
synchrony,
firing
rate,
average
voltage
intertwined,
together
with
their
application
modeling.
As
well
as
natural
extensions
new
approach
modeling
dynamics
open
mathematical
challenges
developing
statistical
neurodynamics
can
generalize
one
discussed
here.
Language: Английский
A personalizable autonomous neural mass model of epileptic seizures
Journal of Neural Engineering,
Journal Year:
2022,
Volume and Issue:
19(5), P. 055002 - 055002
Published: Aug. 22, 2022
Work
in
the
last
two
decades
has
shown
that
neural
mass
models
(NMM)
can
realistically
reproduce
and
explain
epileptic
seizure
transitions
as
recorded
by
electrophysiological
methods
(EEG,
SEEG).
In
previous
work,
advances
were
achieved
increasing
excitation
heuristically
varying
network
inhibitory
coupling
parameters
models.
Based
on
these
early
studies,
we
provide
a
laminar
NMM
capable
of
reproducing
electrical
activity
SEEG
epileptogenic
zone
during
interictal
to
ictal
states.
With
exception
external
noise
input
into
pyramidal
cell
population,
model
dynamics
are
autonomous.
By
setting
system
at
point
close
bifurcation,
seizure-like
generated,
including
pre-ictal
spikes,
low
voltage
fast
activity,
rhythmic
activity.
A
novel
element
is
physiologically
motivated
algorithm
for
chloride
dynamics:
gain
GABAergic
post-synaptic
potentials
modulated
pathological
accumulation
cells
due
high
and/or
dysfunctional
transport.
addition,
order
simulate
signals
comparison
with
real
recordings,
embedded
first
layered
neocortex
then
realistic
physical
model.
We
compare
modeling
results
data
from
four
epilepsy
patient
cases.
key
pathophysiological
mechanisms,
proposed
framework
captures
succinctly
phenomenology
observed
states,
paving
way
robust
personalization
based
NMMs.
Language: Английский
Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks
PLoS Computational Biology,
Journal Year:
2023,
Volume and Issue:
19(4), P. e1010781 - e1010781
Published: April 12, 2023
Spatiotemporal
oscillations
underlie
all
cognitive
brain
functions.
Large-scale
models,
constrained
by
neuroimaging
data,
aim
to
trace
the
principles
underlying
such
macroscopic
neural
activity
from
intricate
and
multi-scale
structure
of
brain.
Despite
substantial
progress
in
field,
many
aspects
about
mechanisms
behind
onset
spatiotemporal
dynamics
are
still
unknown.
In
this
work
we
establish
a
simple
framework
for
emergence
complex
dynamics,
including
high-dimensional
chaos
travelling
waves.
The
model
consists
network
90
regions,
whose
structural
connectivity
is
obtained
tractography
data.
each
area
governed
Jansen
mass
normalize
total
input
received
node
so
it
amounts
same
across
areas.
This
assumption
allows
existence
an
homogeneous
invariant
manifold,
i.e.,
set
different
stationary
oscillatory
states
which
nodes
behave
identically.
Stability
analysis
these
solutions
unveils
transverse
instability
synchronized
state,
gives
rise
types
as
chaotic
alpha
activity.
Additionally,
illustrate
ubiquity
route
towards
next
generation
models.
Altogehter,
our
results
unveil
bifurcation
landscape
that
underlies
function
Language: Английский
The algorithmic agent perspective and computational neuropsychiatry: from etiology to advanced therapy in major depressive disorder
Published: March 14, 2024
Major
Depressive
Disorder
(MDD)
is
a
complex,
heterogeneous
condition
affecting
millions
worldwide.
Computational
neuropsychiatry
offers
potential
breakthroughs
through
mechanistic
modeling
of
this
disorder.
Using
the
Kolmogorov
Theory
consciousness
(KT),
we
develop
foundational
model
where
algorithmic
agents
interact
with
world
to
maximize
an
Objective
Function
evaluating
affective
\textit{valence}.
Depression,
defined
in
context
by
state
persistently
low
valence,
may
arise
from
various
factors---including
inaccurate
models
(cognitive
biases),
dysfunctional
(anhedonia,
anxiety),
deficient
planning
(executive
deficits),
or
unfavorable
environments.
Integrating
algorithmic,
dynamical
systems,
and
neurobiological
concepts,
map
agent
brain
circuits
functional
networks,
framing
etiological
routes
linking
depression
biotypes.
Finally,
explore
how
stimulation,
psychotherapy,
plasticity-enhancing
compounds
such
as
psychedelics
can
synergistically
repair
neural
optimize
therapies
using
personalized
computational
models.
Language: Английский