<p>Designing
processors
for
implantable
closed-loop
neuromodulation
systems
presents
a
formidable
challenge
due
to
the
constrained
operational
environment,
requiring
low
latency
and
high
energy
efficiency.</p>
<p>Previous
benchmarks
have
provided
limited
insights
into
efficiency
latency.
This
paper,
however,
introduces
algorithmic
metrics
that
capture
potential
limitations
of
neural
decoders
intra-cortical
brain-computer
interfaces
in
context
hardware
constraints.
study
common
decoding
methods
predicting
primate’s
finger
kinematics
from
motor
cortex,
explores
suitability
compute
decoding.
The
finds
ANN-based
provide
superior
accuracy,
many
operations
decode
signals
effectively.
Spiking
networks
emerge
as
solution,
bridging
this
gap
by
achieving
competitive
performance
within
sub-10ms
while
utilizing
fraction
computational
resources.</p>
<p>These
distinctive
advantages
neuromorphic
spiking
networks,
positions
them
highly
suitable
challenging
environment
modulation.
Their
capacity
balance
accuracy
offers
immense
reshaping
landscape
decoders,
fostering
greater
understanding,
opening
new
frontiers
intracortical
human-machine
interaction.</p>
Soft Matter,
Год журнала:
2024,
Номер
20(40), С. 7993 - 8011
Опубликована: Янв. 1, 2024
This
review
aims
to
show
the
evolution
of
biohybrid
robots,
their
key
technologies,
applications,
and
challenges.
We
believe
that
multimodal
monitoring
stimulation
technologies
holds
potential
enhance
performance
robots.
The Journal of Physical Chemistry Letters,
Год журнала:
2024,
Номер
15(44), С. 11139 - 11147
Опубликована: Окт. 31, 2024
We
demonstrate
a
flexible
organic
synaptic
transistor
(FOST)
with
an
ion-composite
electrolyte
film
resistant
to
chemical
reagents,
which
uses
three-dimensionally
cross-linked
polyimide
matrix
accommodate
high-concentration
ionic
liquid.
FOST
shows
versatile
plasticity
for
classical
conditioning,
high-pass
filtering,
and
the
learning–forgetting
process.
The
device
achieves
low-energy
consumption
down
1.02
femtojoule
per
event
ultrasensitive
impulse
presynaptic
spike
0.5
mV.
Moreover,
electrical
performance
of
is
still
stable
after
1000
mechanical
bending
cycles.
These
results
that
can
be
applied
future
neuromorphic
electronics.
Intracortical
brain-computer
interfaces
(iBCIs)
promise
revolutionary
clinical
and
research
applications.
State-of-the-art
iBCIs
rely
on
high-density
(HD)
microelectrode
arrays
(MEAs)
to
sense
massive
neuronal
populations.
However,
HD
MEAs
are
bandwidth-demanding,
posing
a
significant
challenge
for
wireless
iBCIs.
Prior
iBCI
systems
have
relied
compression
reduce
neural
signal
bitrate.
Unfortunately,
existing
schemes
blind
neurons'
characteristics,
resulting
in
poor
efficiency
severe
degradation
performance.
<p>Designing
processors
for
implantable
closed-loop
neuromodulation
systems
presents
a
formidable
challenge
due
to
the
constrained
operational
environment,
requiring
low
latency
and
high
energy
efficiency.</p>
<p>Previous
benchmarks
have
provided
limited
insights
into
efficiency
latency.
This
paper,
however,
introduces
algorithmic
metrics
that
capture
potential
limitations
of
neural
decoders
intra-cortical
brain-computer
interfaces
in
context
hardware
constraints.
study
common
decoding
methods
predicting
primate’s
finger
kinematics
from
motor
cortex,
explores
suitability
compute
decoding.
The
finds
ANN-based
provide
superior
accuracy,
many
operations
decode
signals
effectively.
Spiking
networks
emerge
as
solution,
bridging
this
gap
by
achieving
competitive
performance
within
sub-10ms
while
utilizing
fraction
computational
resources.</p>
<p>These
distinctive
advantages
neuromorphic
spiking
networks,
positions
them
highly
suitable
challenging
environment
modulation.
Their
capacity
balance
accuracy
offers
immense
reshaping
landscape
decoders,
fostering
greater
understanding,
opening
new
frontiers
intracortical
human-machine
interaction.</p>
