Journal of Neuroscience,
Год журнала:
2018,
Номер
38(44), С. 9390 - 9401
Опубликована: Окт. 31, 2018
In
the
1960s,
Evarts
first
recorded
activity
of
single
neurons
in
motor
cortex
behaving
monkeys
(Evarts,
1968).
50
years
since,
great
effort
has
been
devoted
to
understanding
how
neuron
relates
movement.
Yet
these
exist
within
a
vast
network,
nature
which
largely
inaccessible.
With
advances
recording
technologies,
algorithms,
and
computational
power,
ability
study
networks
is
increasing
exponentially.
Recent
experimental
results
suggest
that
dynamical
properties
are
critical
movement
planning
execution.
Here
we
discuss
this
systems
perspective
it
reshaping
our
cortices.
Following
an
overview
key
studies
cortex,
techniques
uncover
"latent
factors"
underlying
observed
neural
population
activity.
Finally,
efforts
use
factors
improve
performance
brain-machine
interfaces,
promising
make
findings
broadly
relevant
neuroengineering
as
well
neuroscience.
Annual Review of Neuroscience,
Год журнала:
2020,
Номер
43(1), С. 249 - 275
Опубликована: Июль 8, 2020
Significant
experimental,
computational,
and
theoretical
work
has
identified
rich
structure
within
the
coordinated
activity
of
interconnected
neural
populations.
An
emerging
challenge
now
is
to
uncover
nature
associated
computations,
how
they
are
implemented,
what
role
play
in
driving
behavior.
We
term
this
computation
through
population
dynamics.
If
successful,
framework
will
reveal
general
motifs
quantitatively
describe
dynamics
implement
computations
necessary
for
goal-directed
Here,
we
start
with
a
mathematical
primer
on
dynamical
systems
theory
analytical
tools
apply
perspective
experimental
data.
Next,
highlight
some
recent
discoveries
resulting
from
successful
application
systems.
focus
studies
spanning
motor
control,
timing,
decision-making,
working
memory.
Finally,
briefly
discuss
promising
lines
investigation
future
directions
framework.
Humans
process
information
hierarchically.
In
the
presence
of
hierarchies,
sources
failures
are
ambiguous.
resolve
this
ambiguity
by
assessing
their
confidence
after
one
or
more
attempts.
To
understand
neural
basis
reasoning
strategy,
we
recorded
from
dorsomedial
frontal
cortex
(DMFC)
and
anterior
cingulate
(ACC)
monkeys
in
a
task
which
negative
outcomes
were
caused
either
misjudging
stimulus
covert
switch
between
two
stimulus-response
contingency
rules.
We
found
that
both
areas
harbored
representation
evidence
supporting
rule
switch.
Additional
perturbation
experiments
revealed
ACC
functioned
downstream
DMFC
was
directly
specifically
involved
inferring
switches.
These
results
reveal
computational
principles
hierarchical
reasoning,
as
implemented
cortical
circuits.
