Collective dynamics of adaptive memristor synapse-cascaded neural networks based on energy flow
Chaos Solitons & Fractals,
Год журнала:
2024,
Номер
186, С. 115191 - 115191
Опубликована: Июнь 28, 2024
Язык: Английский
Reliability and energy function of an oscillator and map neuron
Biosystems,
Год журнала:
2025,
Номер
251, С. 105443 - 105443
Опубликована: Март 3, 2025
Язык: Английский
Generating multi-scroll chaotic attractor in a three-dimensional memristive neuron model
Chinese Journal of Physics,
Год журнала:
2024,
Номер
88, С. 1053 - 1067
Опубликована: Фев. 9, 2024
Язык: Английский
Assessing sigmoidal function on memristive maps
Heliyon,
Год журнала:
2024,
Номер
10(6), С. e27781 - e27781
Опубликована: Март 1, 2024
Memristors
offer
a
crucial
element
for
constructing
discrete
maps
that
have
garnered
significant
attention
in
complex
dynamics
and
various
potential
applications.
In
this
study,
we
integrated
memristive
sigmoidal
function
to
propose
innovative
mapping
techniques.
Our
research
confirms
the
amalgamation
of
memristor
functions
represents
promising
approach
creating
both
2D
3D
maps.
Particularly
noteworthy
are
chaotic
featuring
multiple
memristors,
as
highlighted
our
findings.
Specifically
focusing
on
novel
STMM1
map,
delve
into
its
assess
feasibility.
Intriguingly,
introduction
leads
alterations
quantity
fixed
points
symmetry
map.
Язык: Английский
Complete synchronization of three-layer Rulkov neuron network coupled by electrical and chemical synapses
Chaos An Interdisciplinary Journal of Nonlinear Science,
Год журнала:
2024,
Номер
34(4)
Опубликована: Апрель 1, 2024
This
paper
analyzes
the
complete
synchronization
of
a
three-layer
Rulkov
neuron
network
model
connected
by
electrical
synapses
in
same
layers
and
chemical
between
adjacent
layers.
The
outer
coupling
matrix
is
not
Laplacian
as
linear
networks.
We
develop
master
stability
function
method,
which
invariant
manifold
equations
(MSEs)
does
correspond
to
zero
eigenvalues
connection
matrix.
After
giving
existence
conditions
about
nonlinear
coupling,
we
investigate
dynamics
manifold,
will
be
identical
that
synchronous
fixing
parameters
initial
values.
waveforms
show
transient
chaotic
windows
approximate
periodic
with
increased
or
decreased
periods
occur
alternatively
before
asymptotic
behaviors.
Furthermore,
Lyapunov
exponents
MSEs
indicate
can
achieve
synchronization,
while
not.
Finally,
simulate
effects
small
perturbations
on
regimes
evolution
routes
for
non-synchronous
network.
Язык: Английский
Analysis of memristive maps with asymmetry
Integration,
Год журнала:
2023,
Номер
94, С. 102110 - 102110
Опубликована: Ноя. 9, 2023
Язык: Английский
A Memristive Neuron with Nonlinear Membranes and Network Patterns
Опубликована: Янв. 1, 2025
Memristor-coupled
nonlinear
circuits
can
replicate
biological
neuron
firing
patterns
by
incorporating
memristive
and
magnetic
flux
variables
to
model
electromagnetic
induction.
Traditional
models
with
single
capacitive
fail
capture
the
material
properties
field
differences
of
cell
membranes.
This
work
proposes
a
neural
circuit
dual
capacitors,
resistor,
flux-controlled
memristor
(MFCM)
in
parallel,
driven
phototube
simulate
illumination.
The
accounts
for
dynamics
double-layer
membranes
exhibits
energy-defined
behaviors.
Photocurrent
fully
controls
modes,
stochastic
resonance
analyzed
via
coefficient
variability
energy
distributions
under
noise.
An
adaptive
growth
law
modulates
membrane
capacitance
ratios,
enabling
mode
transitions
shifts.
In
network,
control
coupling
bifurcation
parameters
induces
stable
target
waves,
effectively
regulating
collective
dynamics.
study
highlights
neurons'
potential
mimicking
complex
behaviors
network
Язык: Английский
Collective dynamics of a coupled Hindmarsh–Rose neurons with locally active memristor
Biosystems,
Год журнала:
2023,
Номер
232, С. 105010 - 105010
Опубликована: Авг. 24, 2023
Язык: Английский
Applying exponential unit for breaking symmetry of memristive maps
Physica Scripta,
Год журнала:
2024,
Номер
99(2), С. 025244 - 025244
Опубликована: Янв. 17, 2024
Abstract
The
emergence
of
memristors
has
piqued
significant
interest
in
memristive
maps
due
to
their
unique
characteristics.
In
this
paper,
we
introduce
a
novel
and
effective
method
for
constructing
memristor
maps,
leveraging
the
power
exponential
units.
Interestingly,
incorporation
these
units
disrupts
symmetry
alters
count
fixed
points
within
map.
is
simple
build
with
chaos
higher
order
maps.
These
make
our
work
different
from
existing
methods.
To
demonstrate
efficacy
approach,
have
focused
attention
on
examining
dynamics,
feasibility,
practical
applications
specific
map,
referred
as
EPMM
1
Furthermore,
show
that
by
extending
it
becomes
straightforward
create
other
innovative
including
those
multiple
memristors.
Язык: Английский