bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Июнь 4, 2023
The
connection
patterns
of
neural
circuits
form
a
complex
network.
How
signaling
in
these
manifests
as
cognition
and
adaptive
behaviour
remains
the
central
question
neuroscience.
Concomitant
advances
connectomics
artificial
intelligence
open
fundamentally
new
opportunities
to
understand
how
shape
computational
capacity
biological
brain
networks.
Reservoir
computing
is
versatile
paradigm
that
uses
nonlinear
dynamics
high-dimensional
dynamical
systems
perform
computations
approximate
cognitive
functions.
Here
we
present
conn2res
:
an
open-source
Python
toolbox
for
implementing
networks
modular,
allowing
arbitrary
architectures
be
imposed.
allows
researchers
input
connectomes
reconstructed
using
multiple
techniques,
from
tract
tracing
noninvasive
diffusion
imaging,
impose
systems,
simple
spiking
neurons
memristive
dynamics.
versatility
us
ask
questions
at
confluence
neuroscience
intelligence.
By
reconceptualizing
function
computation,
sets
stage
more
mechanistic
understanding
structure-function
relationships
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 22, 2024
Adaptive
cognition
relies
on
cooperation
across
anatomically
distributed
brain
circuits.
However,
specialised
neural
systems
are
also
in
constant
competition
for
limited
processing
resources.
How
does
the
brain's
network
architecture
enable
it
to
balance
these
cooperative
and
competitive
tendencies?
Here
we
use
computational
whole-brain
modelling
examine
dynamical
relevance
of
interactions
mammalian
connectome.
Across
human,
macaque,
mouse
show
that
models
most
faithfully
reproduce
activity,
consistently
combines
modular
with
diffuse,
long-range
interactions.
The
model
outperforms
cooperative-only
model,
excellent
fit
both
spatial
properties
living
brain,
which
were
not
explicitly
optimised
but
rather
emerge
spontaneously.
Competitive
effective
connectivity
produce
greater
levels
synergistic
information
local-global
hierarchy,
lead
superior
capacity
when
used
neuromorphic
computing.
Altogether,
this
work
provides
a
mechanistic
link
between
architecture,
properties,
computation
brain.
Tracking
a
turbulent
plume
to
locate
its
source
under
variable
wind
and
statistics
is
complex
task;
flying
insects
routinely
accomplish
such
tracking,
often
over
long
distances,
in
pursuit
of
food
or
mates.
Several
aspects
this
remarkable
behavior
underlying
neural
circuitry
have
been
studied
experimentally.
Here,
we
take
complementary
silico
approach
develop
an
integrated
understanding
computations.
Specifically,
train
artificial
recurrent
network
(RNN)
agents
using
deep
reinforcement
learning
(DRL)
the
simulated
plumes.
Interestingly,
agents'
emergent
behaviors
resemble
those
insects,
RNNs
learn
compute
task-relevant
variables
with
distinct
dynamic
structures
population
activity.
Our
analyses
put
forward
testable
behavioral
hypothesis
for
tracking
plumes
changing
direction,
provide
key
intuitions
memory
requirements
dynamics
tracking.
Chinese Journal of Chemistry,
Год журнала:
2023,
Номер
41(11), С. 1313 - 1318
Опубликована: Фев. 16, 2023
Comprehensive
Summary
Biological
systems
use
intricate
networks
of
chemical
reactions
to
exchange
information.
How
simulate
complex
with
simple
strand‐displacement
is
crucial
broaden
the
application
scenario
DNA
reaction
network.
Here,
we
report
artificial
network
mimic
operation
and
function
biological
information
transfer
via
reaction.
used
as
analogs
schematize
structures
transmit
Using
synapses
in
neural
an
example,
show
that
proposed
enables
core
functions
systems,
such
long‐term
potential
synapses,
which
underpin
learning
memory.
Also,
performed
“silicon
mimetic”
link
electronic
circuits
network‐based
structures.
As
such,
synaptic
communication
simulated
by
provides
a
complete
demonstration
for
designing
based
on
essence
interaction.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Июнь 4, 2023
The
connection
patterns
of
neural
circuits
form
a
complex
network.
How
signaling
in
these
manifests
as
cognition
and
adaptive
behaviour
remains
the
central
question
neuroscience.
Concomitant
advances
connectomics
artificial
intelligence
open
fundamentally
new
opportunities
to
understand
how
shape
computational
capacity
biological
brain
networks.
Reservoir
computing
is
versatile
paradigm
that
uses
nonlinear
dynamics
high-dimensional
dynamical
systems
perform
computations
approximate
cognitive
functions.
Here
we
present
conn2res
:
an
open-source
Python
toolbox
for
implementing
networks
modular,
allowing
arbitrary
architectures
be
imposed.
allows
researchers
input
connectomes
reconstructed
using
multiple
techniques,
from
tract
tracing
noninvasive
diffusion
imaging,
impose
systems,
simple
spiking
neurons
memristive
dynamics.
versatility
us
ask
questions
at
confluence
neuroscience
intelligence.
By
reconceptualizing
function
computation,
sets
stage
more
mechanistic
understanding
structure-function
relationships
