Time‐Multiplexed Reservoir Computing with Quantum‐Dot Lasers: Impact of Charge‐Carrier Scattering Timescale
physica status solidi (RRL) - Rapid Research Letters,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 3, 2025
Reservoir
computing
with
optical
devices
offers
an
energy‐efficient
approach
for
time‐series
forecasting.
Quantum
dot
lasers
feedback
are
modeled
in
this
article
to
explore
the
extent
which
increased
complexity
charge‐carrier
dynamics
within
nanostructured
semiconductor
can
enhance
prediction
performance.
By
tuning
scattering
interactions,
laser's
and
response
time
be
finely
adjusted,
allowing
a
systematic
investigation.
It
is
found
that
both
system
task
requirements
need
considered
find
optimal
operation
conditions.
Further,
pronounced
relaxation
oscillations
outperform
those
strongly
damped
dynamics,
even
if
underlying
more
complex.
This
demonstrates
reservoir
performance
relies
not
only
on
high
internal
phase
space
dimension
but
also
effective
utilization
of
these
through
output
sampling
process,
quantum
laser,
computing,
delay,
rate,
oscillation.
Язык: Английский
Reservoir computing with state-dependent time delay
Physical review. E,
Год журнала:
2025,
Номер
111(3)
Опубликована: Март 27, 2025
We
examine
a
new
design
of
reservoir
computing
based
on
an
otherwise
linear
dynamical
system
subject
to
feedback
in
which
delay
time
linearly
depends
the
system's
state.
Despite
apparent
linearity
under
casual
perusal,
nonetheless
possesses
nonlinearity
that
can
be
used
for
time-delay
computing.
find
close
multiple
Hopf
bifurcation
points
lead
rich
sawtooth-shaped
transient
response
input
signals,
beneficial
capabilities.
benchmark
memory
capacity
and
performance
solving
delayed
XOR,
Iris
flower
classification
tasks,
Santa
Fe
time-series
prediction
task.
demonstrate
how
tuned
by
changing
dependence.
Язык: Английский
The Drosophila Connectome as a Computational Reservoir for Time-Series Prediction
Biomimetics,
Год журнала:
2025,
Номер
10(5), С. 341 - 341
Опубликована: Май 21, 2025
In
this
work,
we
explore
the
possibility
of
using
topology
and
weight
distribution
connectome
a
Drosophila,
or
fruit
fly,
as
reservoir
for
multivariate
chaotic
time-series
prediction.
Based
on
information
taken
from
recently
released
full
connectome,
create
connectivity
matrix
an
Echo
State
Network.
Then,
use
only
most
connected
neurons
implement
two
possible
selection
criteria,
either
preserving
breaking
relative
proportion
different
neuron
classes
which
are
also
included
in
documented
to
obtain
computationally
convenient
reservoir.
We
then
investigate
performance
such
architectures
compare
them
state-of-the-art
reservoirs.
The
results
show
that
connectome-based
architecture
is
significantly
more
resilient
overfitting
compared
standard
implementation,
particularly
cases
already
prone
overfitting.
To
further
isolate
role
synaptic
weights,
hybrid
reservoirs
with
but
random
weights
topologies
study,
demonstrating
both
factors
play
increased
resilience.
Finally,
perform
experiment
where
entire
used
Despite
much
higher
number
trained
parameters,
remains
has
lower
normalized
error,
under
2%,
at
regularisation,
all
other
regularisation.
Язык: Английский
Large sampling intervals for learning and predicting chaotic systems with reservoir computing
Journal of Physics A Mathematical and Theoretical,
Год журнала:
2024,
Номер
57(29), С. 295702 - 295702
Опубликована: Июль 9, 2024
Abstract
Reservoir
computing
(RC)
is
an
efficient
artificial
neural
network
for
model-free
prediction
and
analysis
of
dynamical
systems
time
series.
As
a
data-based
method,
the
capacity
RC
strongly
affected
by
sampling
interval
training
data.
In
this
paper,
taking
Lorenz
system
as
example,
we
explore
influence
on
performance
in
predicting
chaotic
sequences.
When
increases,
first
enhanced
then
weakened,
presenting
bell-shaped
curve.
By
slightly
revising
calculation
method
output
matrix,
with
small
can
be
improved.
Furthermore,
learn
reproduce
state
large
interval,
which
almost
five
times
larger
than
that
classic
fourth-order
Runge–Kutta
method.
Our
results
show
applications
where
intervals
are
constrained
laid
foundation
building
fast
algorithm
iteration
steps.
Язык: Английский