bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 16, 2024
Abstract
When
rodents
learn
to
navigate
in
a
novel
environment,
high
density
of
place
fields
emerges
at
reward
locations,
elongate
against
the
trajectory,
and
individual
change
spatial
selectivity
while
demonstrating
stable
behavior.
Why
demonstrate
these
characteristic
phenomena
during
learning
remains
elusive.
We
develop
normative
framework
using
maximization
objective,
whereby
temporal
difference
(TD)
error
drives
field
reorganization
improve
policy
learning.
Place
are
modeled
Gaussian
radial
basis
functions
represent
states
an
directly
synapse
actorcritic
for
Each
field’s
amplitude,
center,
width,
as
well
downstream
weights,
updated
online
each
time
step
maximize
cumulative
reward.
that
this
unifies
three
disparate
observed
navigation
experiments.
Furthermore,
we
show
convergence
when
single
target
relearning
multiple
new
targets.
To
conclude,
model
recapitulates
several
aspects
hippocampal
dynamics
mechanisms
offer
testable
predictions
future
Nature,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 12, 2025
Abstract
Cognitive
maps
confer
animals
with
flexible
intelligence
by
representing
spatial,
temporal
and
abstract
relationships
that
can
be
used
to
shape
thought,
planning
behaviour.
have
been
observed
in
the
hippocampus
1
,
but
their
algorithmic
form
learning
mechanisms
remain
obscure.
Here
we
large-scale,
longitudinal
two-photon
calcium
imaging
record
activity
from
thousands
of
neurons
CA1
region
while
mice
learned
efficiently
collect
rewards
two
subtly
different
linear
tracks
virtual
reality.
Throughout
learning,
both
animal
behaviour
hippocampal
neural
progressed
through
multiple
stages,
gradually
revealing
improved
task
representation
mirrored
behavioural
efficiency.
The
process
involved
progressive
decorrelations
initially
similar
within
across
tracks,
ultimately
resulting
orthogonalized
representations
resembling
a
state
machine
capturing
inherent
structure
task.
This
decorrelation
was
driven
individual
acquiring
task-state-specific
responses
(that
is,
‘state
cells’).
Although
various
standard
artificial
networks
did
not
naturally
capture
these
dynamics,
clone-structured
causal
graph,
hidden
Markov
model
variant,
uniquely
reproduced
final
states
trajectory
seen
animals.
cellular
population
dynamics
constrain
underlying
cognitive
map
formation
hippocampus,
pointing
inference
as
fundamental
computational
principle,
implications
for
biological
intelligence.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 10, 2025
Topological
techniques
have
become
a
popular
tool
for
studying
information
flows
in
neural
networks.
In
particular,
simplicial
homology
theory
is
used
to
analyze
how
cognitive
representations
of
space
emerge
from
large
conglomerates
independent
neuronal
contributions.
Meanwhile,
growing
number
studies
suggest
that
many
functions
are
sustained
by
serial
patterns
activity.
Here,
we
investigate
stashes
such
using
path
—an
impartial,
universal
approach
does
not
require
priori
assumptions
about
the
sequences’
nature,
functionality,
underlying
mechanisms,
or
other
contexts.
We
focus
on
hippocampus—a
key
enabler
learning
and
memory
mammalian
brains—and
quantify
ordinal
arrangement
its
activity
similarly
topology
has
previously
been
studied
terms
homologies.
The
results
reveal
vast
majority
sequences
produced
during
spatial
navigation
structurally
equivalent
one
another.
Only
few
classes
distinct
form
an
schema
remains
stable
as
pool
consolidates.
Importantly,
structure
both
maps
upheld
combinations
short
sequences,
suggesting
brief
motifs
dominate
physiological
computations.
This
organization
emerges
stabilizes
timescales
characteristic
learning,
displaying
similar
dynamics.
Yet,
generally
do
reflect
topological
affinities—spatial
sequential
analyses
address
qualitatively
different
aspects
spike
flows,
representing
two
complementary
formats
processing.
Significance
statement
study
employs
examine
hippocampus,
critical
region
memory.
While
traditional,
approaches
model
maps,
provides
framework
analyzing
without
presupposing
their
nature
function.
findings
limited
sequence
classes,
supported
motifs,
over
corresponding
periods
learning.
Notably,
derived
these
capture
affinities,
highlighting
but
dimensions
Spatial
periodicity
in
grid
cell
firing
has
been
interpreted
as
a
neural
metric
for
space
providing
animals
with
coordinate
system
navigating
physical
and
mental
spaces.
However,
the
specific
computational
problem
being
solved
by
cells
remained
elusive.
Here,
we
provide
mathematical
proof
that
spatial
is
only
possible
solution
to
sequence
code
of
2-D
trajectories
hexagonal
pattern
most
parsimonious
such
code.
We
thereby
likely
teleological
cause
existence
reveal
underlying
nature
global
geometric
organization
maps
direct
consequence
simple
local
A
provides
intuitive
explanations
many
previously
puzzling
experimental
observations
may
transform
our
thinking
about
cells.
Spatial
periodicity
in
grid
cell
firing
has
been
interpreted
as
a
neural
metric
for
space
providing
animals
with
coordinate
system
navigating
physical
and
mental
spaces.
However,
the
specific
computational
problem
being
solved
by
cells
remained
elusive.
Here,
we
provide
mathematical
proof
that
spatial
is
only
possible
solution
to
sequence
code
of
2-D
trajectories
hexagonal
pattern
most
parsimonious
such
code.
We
thereby
likely
teleological
cause
existence
reveal
underlying
nature
global
geometric
organization
maps
direct
consequence
simple
local
A
provides
intuitive
explanations
many
previously
puzzling
experimental
observations
may
transform
our
thinking
about
cells.
Major
Depressive
Disorder
(MDD)
is
a
complex,
heterogeneous
condition
affecting
millions
worldwide.
Computational
neuropsychiatry
offers
potential
breakthroughs
through
mechanistic
modeling
of
this
disorder.
Using
the
Kolmogorov
Theory
consciousness
(KT),
we
develop
foundational
model
where
algorithmic
agents
interact
with
world
to
maximize
an
Objective
Function
evaluating
affective
\textit{valence}.
Depression,
defined
in
context
by
state
persistently
low
valence,
may
arise
from
various
factors---including
inaccurate
models
(cognitive
biases),
dysfunctional
(anhedonia,
anxiety),
deficient
planning
(executive
deficits),
or
unfavorable
environments.
Integrating
algorithmic,
dynamical
systems,
and
neurobiological
concepts,
map
agent
brain
circuits
functional
networks,
framing
etiological
routes
linking
depression
biotypes.
Finally,
explore
how
stimulation,
psychotherapy,
plasticity-enhancing
compounds
such
as
psychedelics
can
synergistically
repair
neural
optimize
therapies
using
personalized
computational
models.
eLife,
Journal Year:
2024,
Volume and Issue:
unknown
Published: June 5, 2024
Spatial
periodicity
in
grid
cell
firing
has
been
interpreted
as
a
neural
metric
for
space
providing
animals
with
coordinate
system
navigating
physical
and
mental
spaces.
However,
the
specific
computational
problem
being
solved
by
cells
remained
elusive.
Here,
we
provide
mathematical
proof
that
spatial
is
only
possible
solution
to
sequence
code
of
2-D
trajectories
hexagonal
pattern
most
parsimonious
such
code.
We
thereby
likely
teleological
cause
existence
reveal
underlying
nature
global
geometric
organization
maps
direct
consequence
simple
local
A
provides
intuitive
explanations
many
previously
puzzling
experimental
observations
may
transform
our
thinking
about
cells.
Nature Communications,
Journal Year:
2024,
Volume and Issue:
15(1)
Published: Nov. 15, 2024
Cognitive
problem-solving
benefits
from
cognitive
maps
aiding
navigation
and
planning.
Physical
space
involves
hippocampal
(HC)
allocentric
codes,
while
abstract
task
engages
medial
prefrontal
cortex
(mPFC)
task-specific
codes.
Previous
studies
show
that
challenging
tasks,
like
spatial
alternation,
require
integrating
these
two
types
of
maps.
The
disruption
the
HC-mPFC
circuit
impairs
performance.
We
propose
a
hierarchical
active
inference
model
clarifying
how
this
solves
interaction
tasks
by
bridging
physical
task-space
Simulations
demonstrate
model's
dual
layers
develop
effective
for
space.
alternation
through
reciprocal
interactions
between
layers.
Disrupting
its
communication
decision-making,
which
is
consistent
with
empirical
evidence.
Additionally,
adapts
to
switching
multiple
rules,
providing
mechanistic
explanation
supports
effects
disruption.
How
interact
when
executing
not
fully
understood.
This
paper
models
hippocampal-prefrontal
circuits
memory-guided
taskspace.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 3, 2024
A
bstract
Nervous
systems
learn
representations
of
the
world
and
policies
to
act
within
it.
We
present
a
framework
that
uses
reward-dependent
noise
facilitate
policy
opti-
mization
in
representation
learning
networks.
These
networks
balance
extracting
normative
features
task-relevant
information
solve
tasks.
Moreover,
their
changes
reproduce
several
experimentally
observed
shifts
neural
code
during
task
learning.
Our
presents
biologically
plausible
mechanism
for
emergent
optimization
amid
evidence
plays
vital
role
governing
dynamics.
Code
is
available
at:
NeuralThermalOptimization.