A vectorial code for semantics in human hippocampus
Melissa Franch,
No information about this author
Elizabeth A. Mickiewicz,
No information about this author
James L. Belanger
No information about this author
et al.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 23, 2025
ABSTRACT
As
we
listen
to
speech,
our
brains
actively
compute
the
meaning
of
individual
words.
Inspired
by
success
large
language
models
(LLMs),
hypothesized
that
brain
employs
vectorial
coding
principles,
such
is
reflected
in
distributed
activity
single
neurons.
We
recorded
responses
hundreds
neurons
human
hippocampus,
which
has
a
well-established
role
semantic
coding,
while
participants
listened
narrative
speech.
find
encoding
contextual
word
simultaneous
whose
selectivities
span
multiple
unrelated
categories.
Like
embedding
vectors
models,
distance
between
neural
population
correlates
with
distance;
however,
this
effect
was
only
observed
(like
BERT)
and
reversed
non-contextual
Word2Vec),
suggesting
depends
critically
on
contextualization.
Moreover,
for
subset
highly
semantically
similar
words,
even
embedders
showed
an
inverse
correlation
distances;
attribute
pattern
noise-mitigating
benefits
contrastive
coding.
Finally,
further
support
critical
context,
range
covaries
lexical
polysemy.
Ultimately,
these
results
hypothesis
hippocampus
follows
principles.
Language: Английский
A Neural Circuit Framework for Economic Choice: From Building Blocks of Valuation to Compositionality in Multitasking
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 13, 2025
Abstract
Value-guided
decisions
are
at
the
core
of
reinforcement
learning
and
neuroeconomics,
yet
basic
computations
they
require
remain
poorly
understood
mechanistic
level.
For
instance,
how
does
brain
implement
multiplication
reward
magnitude
by
probability
to
yield
an
expected
value?
Where
within
a
neural
circuit
is
indifference
point
for
comparing
types
encoded?
How
do
learned
values
generalize
novel
options?
Here,
we
introduce
biologically
plausible
model
that
adheres
Dale’s
law
trained
on
five
choice
tasks,
offering
potential
answers
these
questions.
The
captures
key
neurophysiological
observations
from
orbitofrontal
cortex
monkeys
generalizes
offer
values.
Using
single
network
solve
diverse
identified
compositional
representations—quantified
via
task
variance
analysis
corroborated
curriculum
learning.
This
work
provides
testable
predictions
probe
basis
decision
making
its
disruption
in
neuropsychiatric
disorders.
Language: Английский
Prediction, inference, and generalization in orbitofrontal cortex
Fengjun Ma,
No information about this author
Huixin Lin,
No information about this author
Jingfeng Zhou
No information about this author
et al.
Current Biology,
Journal Year:
2025,
Volume and Issue:
35(7), P. R266 - R272
Published: April 1, 2025
Our
understanding
of
the
orbitofrontal
cortex
(OFC)
has
significantly
evolved
over
past
few
decades.
This
prefrontal
region
been
associated
with
a
wide
range
cognitive
functions,
including
popular
view
that
it
primarily
signals
expected
value
each
possible
option,
allowing
downstream
areas
to
use
these
for
decision-making.
However,
discovery
rich,
task-related
information
within
OFC
and
its
essential
role
in
inference-based
behaviors
shifted
our
perspective
led
proposal
holds
map
used
by
both
humans
animals
making
predictions
inferences.
Recent
studies
have
further
shown
maps
can
be
abstracted
generalized,
serving
immediate
future
needs.
In
this
review,
we
trace
research
journey
leading
evolving
insights,
discuss
potential
neural
mechanisms
supporting
OFC's
roles
prediction,
inference,
generalization,
compare
hippocampus,
another
critical
mapping,
while
also
exploring
interactions
between
two
areas.
Language: Английский
The neural basis of swap errors in working memory
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Oct. 10, 2023
When
making
decisions
in
a
cluttered
world,
humans
and
other
animals
often
have
to
hold
multiple
items
memory
at
once
-
such
as
the
different
on
shopping
list.
Psychophysical
experiments
shown
remembered
stimuli
can
sometimes
become
confused,
with
participants
reporting
chimeric
composed
of
features
from
stimuli.
In
particular,
subjects
will
make
"swap
errors"
where
they
misattribute
feature
one
object
belonging
another
object.
While
swap
errors
been
described
behaviorally,
their
neural
mechanisms
are
unknown.
Here,
we
elucidate
these
through
trial-by-trial
analysis
population
recordings
posterior
frontal
brain
regions
while
monkeys
perform
two
multi-stimulus
working
tasks.
tasks,
were
cued
report
color
an
item
that
either
was
previously
corresponding
location
(requiring
selection
memory)
or
be
attention
position).
Animals
made
both
data,
find
evidence
correlates
emerged
when
correctly
information
is
selected
incorrectly
memory.
This
led
representation
distractor
if
it
target
color,
underlying
eventual
error.
We
did
not
consistent
arose
misinterpretation
cue
during
encoding
storage
These
results
suggest
alternative
established
views
origins
errors,
highlight
manipulation
crucial
yet
surprisingly
brittle
processes.
Language: Английский
Multimodal subspace independent vector analysis captures latent subspace structures in large multimodal neuroimaging studies
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Sept. 17, 2023
A
key
challenge
in
neuroscience
is
to
understand
the
structural
and
functional
relationships
of
brain
from
high-dimensional,
multimodal
neuroimaging
data.
While
conventional
multivariate
approaches
often
simplify
statistical
assumptions
estimate
one-dimensional
independent
sources
shared
across
modalities,
between
true
latent
are
likely
more
complex
-
dependence
may
exist
within
span
one
or
dimensions.
Here
we
present
Multimodal
Subspace
Independent
Vector
Analysis
(MSIVA),
a
methodology
capture
both
joint
unique
vector
multiple
data
modalities
by
defining
cross-modal
unimodal
subspaces
with
variable
In
particular,
MSIVA
enables
flexible
estimation
varying-size
their
one-to-one
linkage
corresponding
modalities.
As
demonstrate,
main
benefit
ability
subject-level
variability
at
voxel
level
subspaces,
contrasting
rigidity
traditional
methods
that
share
same
components
subjects.
We
compared
initialization
baseline
baseline,
evaluated
all
three
five
candidate
subspace
structures
on
synthetic
datasets.
show
successfully
identified
ground-truth
datasets,
while
failed
detect
high-dimensional
subspaces.
then
demonstrate
better
detected
structure
two
large
datasets
including
MRI
(sMRI)
(fMRI),
baseline.
From
subsequent
subspace-specific
canonical
correlation
analysis,
brain-phenotype
prediction,
voxelwise
brain-age
delta
our
findings
suggest
estimated
optimal
strongly
associated
various
phenotype
variables,
age,
sex,
schizophrenia,
lifestyle
factors,
cognitive
functions.
Further,
modality-
group-specific
regions
related
measures
such
as
age
(e.g.,
cerebellum,
precentral
gyrus,
cingulate
gyrus
sMRI;
occipital
lobe
superior
frontal
fMRI),
sex
cerebellum
sMRI,
fMRI,
precuneus
sMRI
schizophrenia
temporal
pole,
operculum
cortex
lingual
shedding
light
phenotypic
neuropsychiatric
biomarkers
linked
function.
Language: Английский
Modular representations emerge in neural networks trained to perform context-dependent tasks
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 1, 2024
Abstract
The
brain
has
large-scale
modular
structure
in
the
form
of
regions,
which
are
thought
to
arise
from
constraints
on
connectivity
and
physical
geometry
cortical
sheet.
In
contrast,
experimental
theoretical
work
argued
both
for
against
existence
specialized
sub-populations
neurons
(modules)
within
single
regions.
By
studying
artificial
neural
networks,
we
show
that
this
local
modularity
emerges
support
context-dependent
behavior,
but
only
when
input
is
low-dimensional.
No
anatomical
required.
We
also
specialization
at
population
level
(different
modules
correspond
orthogonal
subspaces).
Modularity
yields
abstract
representations,
allows
rapid
learning
generalization
novel
tasks,
facilitates
related
contexts.
Non-modular
representations
facilitate
unrelated
Our
findings
reconcile
conflicting
results
make
predictions
future
experiments.
Language: Английский
A universal hippocampal memory code across animals and environments
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 24, 2024
How
learning
is
affected
by
context
a
fundamental
question
of
neuroscience,
as
the
ability
to
generalize
different
contexts
necessary
for
navigating
world.
An
example
swift
contextual
generalization
observed
in
conditioning
tasks,
where
performance
quickly
generalized
from
one
another.
A
key
identifying
neural
substrate
underlying
this
how
hippocampus
(HPC)
represents
task-related
stimuli
across
environments,
given
that
HPC
cells
exhibit
place-specific
activity
changes
(remapping).
In
study,
we
used
calcium
imaging
monitor
hippocampal
neuron
rats
performed
task
multiple
spatial
contexts.
We
investigated
whether
cells,
which
encode
both
locations
(place
cells)
and
information,
could
maintain
their
representation
even
when
encoding
remapped
new
context.
To
assess
consistency
representations,
advanced
dimensionality
reduction
techniques
combined
with
machine
develop
manifold
representations
population
level
activity.
The
results
showed
remained
stable
place
cell
changed,
thus
demonstrating
similar
embedding
geometries
Notably,
these
patterns
were
not
only
consistent
within
same
animal
but
also
significantly
animals,
suggesting
standardized
or
‘neural
syntax’
hippocampus.
These
findings
bridge
critical
gap
between
memory
navigation
research,
revealing
maintains
cognitive
environments.
suggest
function
governed
framework
shared
an
observation
may
have
broad
implications
understanding
memory,
learning,
related
processes.
Looking
ahead,
work
opens
avenues
exploring
principles
strategies.
Language: Английский