Specific connectivity optimizes learning in thalamocortical loops
Cell Reports,
Journal Year:
2024,
Volume and Issue:
43(4), P. 114059 - 114059
Published: April 1, 2024
Thalamocortical
loops
have
a
central
role
in
cognition
and
motor
control,
but
precisely
how
they
contribute
to
these
processes
is
unclear.
Recent
studies
showing
evidence
of
plasticity
thalamocortical
synapses
indicate
for
the
thalamus
shaping
cortical
dynamics
through
learning.
Since
signals
undergo
compression
from
cortex
thalamus,
we
hypothesized
that
computational
depends
critically
on
structure
corticothalamic
connectivity.
To
test
this,
identified
optimal
promotes
biologically
plausible
learning
synapses.
We
found
projections
specialized
communicate
an
efference
copy
output
benefit
while
communicating
modes
highest
variance
working
memory
tasks.
analyzed
neural
recordings
mice
performing
grasping
delayed
discrimination
tasks
communication
consistent
with
predictions.
These
results
suggest
orchestrates
functionally
precise
manner
structured
Language: Английский
Feedback control of recurrent dynamics constrains learning timescales during motor adaptation
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: May 26, 2024
Abstract
Latent
dynamical
models
of
the
primary
motor
cortex
(M1)
have
revealed
fundamental
neural
computations
underlying
control;
however,
such
often
overlook
impact
sensory
feedback,
which
can
continually
update
cortical
dynamics
and
correct
for
external
perturbations.
This
suggests
a
critical
need
to
model
interaction
between
feedback
intrinsic
dynamics.
Such
would
also
benefit
design
brain-computer
interfaces
(BCIs)
that
decode
activity
in
real
time,
where
both
user
learning
proficient
control
require
feedback.
Here
we
investigate
flexible
modulation
demonstrate
its
on
BCI
task
performance
short-term
learning.
By
training
recurrent
network
with
real-time
simple
2D
reaching
task,
analogous
cursor
control,
show
how
previously
reported
M1
patterns
be
reinterpreted
as
arising
from
feedback-driven
Next,
by
incorporating
adaptive
controllers
upstream
M1,
make
testable
prediction
new
decoder
is
facilitated
plasticity
inputs
including
remapping
beyond
connections
within
M1.
input-driven
structure
determines
speed
adaptation
outcomes,
explains
continuous
form
variability.
Thus,
our
work
highlights
input-dependent
latent
clarifies
constraints
arise
statistical
characteristics
activity.
Language: Английский
Transition to chaos separates learning regimes and relates to measure of consciousness in recurrent neural networks
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: May 15, 2024
Recurrent
neural
networks
exhibit
chaotic
dynamics
when
the
variance
in
their
connection
strengths
exceed
a
critical
value.
Recent
work
indicates
also
modulates
learning
strategies;
learn
"rich"
representations
initialized
with
low
coupling
and
"lazier"
solutions
larger
variance.
Using
Watts-Strogatz
of
varying
sparsity,
structure,
hidden
weight
variance,
we
find
that
strength
dividing
from
ordered
differentiates
rich
lazy
strategies.
Training
moves
both
stable
closer
to
edge
chaos,
richer
before
transition
chaos.
In
contrast,
biologically
realistic
connectivity
structures
foster
stability
over
wide
range
variances.
The
chaos
is
reflected
measure
clinically
discriminates
levels
consciousness,
perturbational
complexity
index
(PCIst).
Networks
high
values
PCIst
learning,
suggesting
consciousness
prior
may
promote
learning.
results
suggest
clear
relationship
between
dynamics,
regimes
complexity-based
measures
consciousness.
Language: Английский