Nature Communications,
Год журнала:
2023,
Номер
14(1)
Опубликована: Май 23, 2023
Abstract
Recent
work
in
cognitive
and
systems
neuroscience
has
suggested
that
the
hippocampus
might
support
planning,
imagination,
navigation
by
forming
maps
capture
abstract
structure
of
physical
spaces,
tasks,
situations.
Navigation
involves
disambiguating
similar
contexts,
planning
execution
a
sequence
decisions
to
reach
goal.
Here,
we
examine
hippocampal
activity
patterns
humans
during
goal-directed
task
investigate
how
contextual
goal
information
are
incorporated
construction
navigational
plans.
During
pattern
similarity
is
enhanced
across
routes
share
context
navigation,
observe
prospective
activation
reflects
retrieval
related
key-decision
point.
These
results
suggest
that,
rather
than
simply
representing
overlapping
associations
or
state
transitions,
shaped
goals.
Cell,
Год журнала:
2020,
Номер
183(5), С. 1249 - 1263.e23
Опубликована: Ноя. 1, 2020
The
hippocampal-entorhinal
system
is
important
for
spatial
and
relational
memory
tasks.
We
formally
link
these
domains,
provide
a
mechanistic
understanding
of
the
hippocampal
role
in
generalization,
offer
unifying
principles
underlying
many
entorhinal
cell
types.
propose
medial
cells
form
basis
describing
structural
knowledge,
this
with
sensory
representations.
Adopting
principles,
we
introduce
Tolman-Eichenbaum
machine
(TEM).
After
learning,
TEM
display
diverse
properties
resembling
apparently
bespoke
responses,
such
as
grid,
band,
border,
object-vector
cells.
include
place
landmark
that
remap
between
environments.
Crucially,
also
aligns
empirically
recorded
representations
complex
non-spatial
generates
predictions
remapping
not
random
previously
believed;
rather,
knowledge
preserved
across
confirm
transfer
over
simultaneously
grid
Journal of Artificial Intelligence Research,
Год журнала:
2022,
Номер
75, С. 1401 - 1476
Опубликована: Дек. 22, 2022
In
this
article,
we
aim
to
provide
a
literature
review
of
different
formulations
and
approaches
continual
reinforcement
learning
(RL),
also
known
as
lifelong
or
non-stationary
RL.
We
begin
by
discussing
our
perspective
on
why
RL
is
natural
fit
for
studying
learning.
then
taxonomy
mathematically
characterizing
two
key
properties
non-stationarity,
namely,
the
scope
driver
non-stationarity.
This
offers
unified
view
various
formulations.
Next,
present
approaches.
go
discuss
evaluation
agents,
providing
an
overview
benchmarks
used
in
important
metrics
understanding
agent
performance.
Finally,
highlight
open
problems
challenges
bridging
gap
between
current
state
findings
neuroscience.
While
still
its
early
days,
study
has
promise
develop
better
incremental
learners
that
can
function
increasingly
realistic
applications
where
non-stationarity
plays
vital
role.
These
include
such
those
fields
healthcare,
education,
logistics,
robotics.
Current Biology,
Год журнала:
2022,
Номер
32(17), С. 3676 - 3689.e5
Опубликована: Июль 20, 2022
tested
humans,
rats,
and
RL
agents
on
a
novel
modular
maze
d
Humans
rats
were
remarkably
similar
in
their
choice
of
trajectories
Both
species
most
to
utilizing
SR
also
displayed
features
model-based
planning
early
trials
Neural Computation,
Год журнала:
2021,
Номер
unknown, С. 1 - 44
Опубликована: Авг. 30, 2021
Replay
is
the
reactivation
of
one
or
more
neural
patterns
that
are
similar
to
activation
experienced
during
past
waking
experiences.
was
first
observed
in
biological
networks
sleep,
and
it
now
thought
play
a
critical
role
memory
formation,
retrieval,
consolidation.
Replay-like
mechanisms
have
been
incorporated
deep
artificial
learn
over
time
avoid
catastrophic
forgetting
previous
knowledge.
algorithms
successfully
used
wide
range
learning
methods
within
supervised,
unsupervised,
reinforcement
paradigms.
In
this
letter,
we
provide
comprehensive
comparison
between
replay
mammalian
brain
networks.
We
identify
multiple
aspects
missing
systems
hypothesize
how
they
could
be
improve
