IEEE Transactions on Molecular Biological and Multi-Scale Communications,
Journal Year:
2023,
Volume and Issue:
9(2), P. 246 - 256
Published: May 1, 2023
Living
systems
face
both
environmental
complexity
and
limited
access
to
free-energy
resources.
Survival
under
these
conditions
requires
a
control
system
that
can
activate,
or
deploy,
available
perception
action
resources
in
context
specific
way.
In
Part
I,
we
introduced
the
principle
(FEP)
idea
of
active
inference
as
Bayesian
prediction-error
minimization,
show
how
problem
arises
systems.
We
then
review
classical
quantum
formulations
FEP,
with
former
being
limit
latter.
this
accompanying
II,
when
are
described
executing
driven
by
their
flow
always
be
represented
tensor
networks
(TNs).
TNs
implemented
within
general
framework
topological
neural
networks,
discuss
implications
results
for
modeling
biological
at
multiple
scales.
Neuroscience & Biobehavioral Reviews,
Journal Year:
2023,
Volume and Issue:
156, P. 105500 - 105500
Published: Dec. 5, 2023
This
paper
concerns
the
distributed
intelligence
or
federated
inference
that
emerges
under
belief-sharing
among
agents
who
share
a
common
world-and
world
model.
Imagine,
for
example,
several
animals
keeping
lookout
predators.
Their
collective
surveillance
rests
upon
being
able
to
communicate
their
beliefs-about
what
they
see-among
themselves.
But,
how
is
this
possible?
Here,
we
show
all
necessary
components
arise
from
minimising
free
energy.
We
use
numerical
studies
simulate
generation,
acquisition
and
emergence
of
language
in
synthetic
agents.
Specifically,
consider
inference,
learning
selection
as
variational
energy
posterior
(i.e.,
Bayesian)
beliefs
about
states,
parameters
structure
generative
models,
respectively.
The
theme-that
attends
these
optimisation
processes-is
actions
minimise
expected
energy,
leading
active
model
(a.k.a.,
learning).
first
illustrate
role
communication
resolving
uncertainty
latent
states
partially
observed
world,
on
which
have
complementary
perspectives.
then
requisite
language-entailed
by
likelihood
mapping
an
agent's
overt
expression
(e.g.,
speech)-showing
can
be
transmitted
across
generations
learning.
Finally,
emergent
property
minimisation,
when
operate
within
same
econiche.
conclude
with
discussion
various
perspectives
phenomena;
ranging
cultural
niche
construction,
through
learning,
complexity
ensembles
self-organising
systems.
The
context
for
our
paper
comes
from
the
neurophenomenology
research
program
initiated
by
Francisco
Varela
at
end
of
1990s.
Varela’s
working
hypothesis
was
that,
to
be
successful,
a
consciousness
must
progress
relating
first-person
phenomenological
accounts
structure
experience
and
their
third-person
counterparts
in
neuroscience
through
reciprocal
or
mutual
constraints.
Leveraging
Bayesian
mechanics,
particular
deep
parametric
active
inference,
we
demonstrate
potential
epistemically
advantageous
constraints
between
phenomenological,
computational,
behavioural
physiological
vocabularies.
Specifically,
dual
information
geometry
mechanics
serves
establish,
under
certain
conditions,
generative
passages
lived
its
instantiation.
This
argues
epistemological
necessity
such
inclusion
trained
reflective
awareness
neurophenomenological
empirical
approaches,
showcasing
incremental
epistemic
gains
that
come
shifting
focus
contents
(i.e.
what
subject
experiences
given
experimental
set-up)
how
-
activities
allow
meaningful
world
appear
us
as
experience.
explanatory
power
resulting
framework,
computational
neurophenomenology,
arises
disciplined
circulation
first
perspectives
enabled
formalism
where
depth
refers
property
models
can
form
beliefs
about
parameters
own
modelling
process.
Hence,
this
contributes
understanding
bridging
descriptions
instantiations,
whilst
also
highlighting
significance
person
investigation
protocols.
Philosophical Studies,
Journal Year:
2024,
Volume and Issue:
181(8), P. 1947 - 1970
Published: June 26, 2024
Abstract
Does
the
assumption
of
a
weak
form
computational
functionalism,
according
to
which
right
neural
computation
is
sufficient
for
consciousness,
entail
that
digital
simulation
such
computations
conscious?
Or
must
this
be
implemented
in
way,
order
replicate
consciousness?
From
perspective
Karl
Friston’s
free
energy
principle,
self-organising
systems
(such
as
living
organisms)
share
set
properties
could
realised
artificial
systems,
but
are
not
instantiated
by
computers
with
classical
(von
Neumann)
architecture.
I
argue
at
least
one
these
properties,
viz.
certain
kind
causal
flow,
can
used
draw
distinction
between
merely
simulate,
and
those
actually
consciousness.
In
this
paper
we
explore
the
known
connection
among
sustainability,
resilience,
and
well-being
within
framework
of
active
inference.
Initially,
revisit
how
notions
resilience
intersect
inference
before
defining
sustainability.
We
adopt
a
holistic
concept
sustainability
denoting
enduring
capacity
to
meet
needs
over
time
without
depleting
crucial
resources.
It
extends
beyond
material
wealth
encompass
community
networks,
labor,
knowledge.
Using
Free
Energy
Principle,
can
emphasize
role
fostering
resource
renewal,
harmonious
system-entity
exchanges,
practices
that
encourage
self-organization
as
pathways
achieving
both
in
an
agent
collectives.
start
by
connecting
Active
Inference
with
well-being,
building
on
exsiting
work.
then
attempt
link
asserting
alone
is
insufficient
for
sustainable
outcomes.
While
absorbing
shocks
stresses,
must
be
intrinsically
linked
ensure
adaptive
capacities
do
not
merely
perpetuate
existing
vulnerabilities.
Rather,
it
should
facilitate
transformative
processes
address
root
causes
unsustainability.
Sustainability,
therefore,
manifest
across
extended
timescales
all
system
strata,
from
individual
components
broader
system,
uphold
ecological
integrity,
economic
stability,
social
well-being.
explain
manifests
at
level
agent,
collectives
systems.
To
model
quantify
interdependencies
between
resources
their
impact
overall
introduce
application
network
theory
dynamical
systems
theory.
optimization
precision
or
learning
rates
through
framework,
advocating
approach
fosters
elastic
plastic
necessary
long-term
abundance.
There
is
a
renewed
interest
in
taking
phenomenology
seriously
consciousness
research,
contemporary
psychiatry,
and
neurocomputation.
The
neurophenomenology
research
program,
pioneered
by
Varela
(1996),
rigorously
examines
subjective
experience
using
first-person
methodologies
inspired
contemplative
practices.
This
review
explores
recent
advancements
neurophenomenological
approaches,
particularly
their
application
to
meditation
practices
potential
clinical
translations.
We
first
examine
innovative
multi-dimensional
phenomenological
assessment
tools
designed
capture
subtle,
dynamic
shifts
experiential
contents
structures
of
during
meditation.
These
sampling
approaches
allow
shedding
new
light
on
the
mechanisms
trajectories
practice
retreat.
Secondly,
we
highlight
how
empirical
studies
leverage
expertise
experienced
meditators
deconstruct
aversive
self-related
processes,
providing
detailed
reports
that
guide
researchers
identifying
novel
behavioral
neurodynamic
markers
associated
with
pain
regulation,
self-dissolution
acceptance
mortality.
Finally,
discuss
framework,
deep
computational
neurophenomenology,
which
updates
theoretical
ambitions
“naturalize
phenomenology”
(Varela,
1997).
framework
uses
formalism
parametric
active
inference,
where
depth
refers
property
generative
models
can
form
beliefs
about
parameters
own
modeling
process.
Collectively,
these
methodological
innovations,
centered
around
rigorous
investigation,
epistemologically
beneficial
mutual
constraints
among
phenomenological,
computational,
neurophysiological
domains.
could
contribute
an
integrated
understanding
biological
basis
mental
illness,
its
treatment
tight
connections
lived
patient.
Entropy,
Journal Year:
2025,
Volume and Issue:
27(2), P. 143 - 143
Published: Feb. 1, 2025
Active
inference
under
the
Free
Energy
Principle
has
been
proposed
as
an
across-scales
compatible
framework
for
understanding
and
modelling
behaviour
self-maintenance.
Crucially,
a
collective
of
active
agents
can,
if
they
maintain
group-level
Markov
blanket,
constitute
larger
agent
with
generative
model
its
own.
This
potential
computational
scale-free
structures
speaks
to
application
self-organizing
systems
across
spatiotemporal
scales,
from
cells
human
collectives.
Due
difficulty
reconstructing
that
explains
emergent
agents,
there
little
research
on
this
kind
multi-scale
inference.
Here,
we
propose
data-driven
methodology
characterising
relation
between
dynamics
constituent
individual
agents.
We
apply
methods
cognitive
psychiatry,
applicable
well
other
types
approaches.
Using
simple
Multi-Armed
Bandit
task
example,
employ
new
ActiveInference.jl
library
Julia
simulate
who
are
equipped
blanket.
use
sampling-based
parameter
estimation
make
inferences
about
agent,
show
is
non-trivial
relationship
models
constitute,
even
in
setting.
Finally,
point
number
ways
which
might
be
applied
better
understand
relations
nested
scales.