Kind: captions
Language: en
the following is a conversation with
Karl Kristen one of the greatest neuro
scientists in history cited over 245
thousand times known for many
influential ideas in brain imaging
neuroscience and theoretical
neurobiology including especially the
fascinating idea of the free energy
principle for action and perception
Karl's mix of humor brilliance and
kindness to me are inspiring and
captivating this was a huge honor and a
pleasure this is the artificial
intelligence podcast if you enjoy it
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the world and now here's my conversation
with Carl Fuerst --an how much of the
human brain do we understand from the
low level of neuronal communication to
the functional level to the to the
highest level maybe the the psychiatric
disorder level well we're certainly in a
better position than we were last
century
how far we've got to go I think is
almost an unanswerable question so you'd
have to set the parameters you know what
constitutes understanding what level of
understanding do you want I think we've
made enormous progress in terms of
broad-brush principles whether that
affords a detailed cartography of the
functional anatomy of the brain and what
he doesn't write down to the
microcircuitry in the neurons that's
probably out of reach at the present
time so the cartography so mapping the
brain do you think mapping of the brain
the detailed perfect imaging of it does
that get us
closer to understanding of the mind of
the brain so how far does it get us if
we have the perfect cartography of the
brain I think there are lower bounds on
that it's a really interesting question
you and it would determine this sort of
scientific career you'd pursue if you
believe that knowing every dendritic
connection every sort of microscopic
synaptic structure and right down to the
molecular level was gonna give you the
right kind of information to understand
the computational Natale then you choose
to be microscopic and you would study
little cubic millimeters of brain for
the rest of your life if on the other
hand you were interested in holistic
functions and a sort of functional
anatomy of the sort that a
neuropsychologist would understand you'd
study brain lesions and strokes you know
just looking at the whole person so
again it comes back to I won't level do
you want understanding I think there are
principled reasons not to go too far if
you commit to a view of the brain as a
machine that's performing a form of
inference and representing things there
are the understanding that level our
understanding is necessarily cast in
terms of probability densities and
ensemble densities
distributions and what that tells you is
that you don't really want to look at
the atoms to understand the
thermodynamics of probabilistic
descriptions for how the brain works so
I personally wouldn't look at the
molecules or indeed the single neurons
in the same way if I wanted and
understand the thermodynamics of some
non equilibrium steady state of a gas or
an active material I wouldn't spend my
life looking at the the individual
molecules that constituted there on
somebody look at their collective
behavior on the other hand if you go to
coarse grain you're gonna miss some
basic canonical principles of
connectivity and architectures I'm
thinking here this bitkha local but this
current excitement about high field
magnetic resonance imaging and seven
tests that why well it gives us for the
first time the opportunity to look at
the brain in action at the level of a
few millimeters that distinguish between
different layers of the cortex that may
be very important in terms of evincing
generic principles of canonical
microcircuitry that are replicated
throughout the brain there may tell us
something fundamental about message
passing in the brain and these density
dynamics of on your own ensemble
population dynamics that underwrite our
you know our brain function so somewhere
between a millimeter and a meter
lingering for a bit under and the big
questions if you allow me what to you is
the most beautiful or surprising
characteristic of the human brain
I think it's hierarchical and recursive
aspect is recurrent aspect of the
structure or of the actual
representation of power of the brain
well I think one speaks to the other
I was actually answering in adèle minded
way from the point of view of purely its
anatomy and and its structural aspects I
mean there are many marvelous organs in
them in the body
let's take your liver for example you
know without it you wouldn't you
wouldn't be around for very long and he
does some
beautiful delicate by chemistry and
homeostasis and you're evolved with a
finesse that would easily parallel the
brain but he doesn't have a beautiful
Anatomy
he has a simple atomy which is
attractive in a minimalist sense but it
doesn't have that crafted structure of
sparse connectivity and that recurrence
and that specialization that the brain
has so you said a lot of interesting
terms here so the recurrence the
sparsity but you also started by saying
hierarchical mm-hmm so I've I've never
thought of our brain as hierarchical
sort of I always thought is just like a
giant mess an interconnected mess was
very difficult to figure anything out
but in what sense do you see the brain
is hierarchical well I see it's not a
magic soup yeah of course it's what I
used to think when I was before I
studied medicine and the like so a lot
of those terms imply each other so
hierarchies if you just think about the
nature of a hierarchy how would you
actually build one and what you would
have to do is basically carefully remove
the right connections that destroy the
completely connected soups that you
might have in mind so a hierarchy is in
and of itself defined by a sparse and
particular connectivity structure I'm
not committing to any particular form of
hierarchy the your senses there is some
oh absolutely in virtue of the fact that
there is a sparsity of connectivity not
necessarily of a quality it's obvious
and if a quantitative sort so they are
it is demonstrably so and that they've
far further apart two parts of the brain
are the less likely that they are to be
wired you know to possess axonal
processes neuronal processes that
directly communicate one message or
messages from one part of the brain to
the other part of the brain so we know
there's a sparse connectivity
and furthermore on the basis of
anatomical connectivity and traces
studies we know that that a that has
that sparsity under writes a higher high
rock on a very structured sort of
connectivity that might be best
understood like a little bit like an
onion you know that there there is a
concentric sometimes refer to as
centripetal by people like Marcel mess
ulam hierarchical organization to the
brain so you can think of the brain as
in a rough sense like an onion and all
the sensory information and all the
afferent outgoing messages that supply
commands to your muscles or to your
secrete ryokans come from the surface so
there's a massive exchange interface
with the world out there on the surface
and then underneath there's a little
layer that sits and looks at the
exchange on the surface and then
underneath that there's a layer right
there way down to the very center
through the deepest part of the onion
that's what I mean by a mirror
hierarchical organization there's a
discernible structure defined by the
sparsity of connections that lends the
architecture a hierarchical structure
that tells one a lot about the kinds of
representations and messages so karate
on any question is this about the
representational capacity or is it about
the anatomy
well one under writes the other you know
if one this simply thinks of the brain
as a message passing machine a process
that is in the service of doing
something then the the circuitry and the
connectivity that shape that message
passing also dictate its function so
you've done a lot of amazing work in a
lot of directions so let's look at one
aspect of that of looking into the brain
and trying to study this onion structure
of what can we learn about the brain by
imaging it which is one way to sort of
look at the anatomy of it broadly
speaking what what are the methods of
imaging but even bigger what can we
learn about it right so well most
imaging human neural imaging you might
see you know in science journals the
speaks to the way the brain works
measures brain activity over time so you
know that's the first thing to say the
way we're effectively looking at
fluctuations in neuronal responses
usually in response to some sensory
input or some instruction some task not
necessarily and there's a lot of
interest in just looking at the brain in
terms of resting state endogenous or
intrinsic activity but crucially at
every point looking at these
fluctuations either induced or intrinsic
in the neural activity and understanding
them at two levels so normally people
would recourse to two principles of
brain or kin organization that are
complimentary one functional
specialization or segregation so what
does that mean it simply means that
there are certain parts of the brain
that may be specialized for certain
kinds of processing you know for example
visual motion our ability to recognize
or to perceive movement in the visual
world and furthermore that specialized
processing may be spatially or
anatomically segregated leading to
functional segregation which means that
if I were to compare your brain activity
during a period of studying viewing a
static image and then compare that to
the responses of fluctuations in the
brain when you are exposed to a moving
image say a flying bird
eirick we would expect to see restricted
segregated differences in activity and
those are basically the hot spots that
you see in me in surgical parametric
maps that test for the significance of
the responses that are circumscribed so
now basically we're talking about some
people of perhaps and currently Calder
and neocartography this is a phrenology
augmented by modern day near imaging
basically finding blobs or bumps on the
brain that do this or do that and trying
to understand the cartography of that
functional specialization so how much
how much is there such this is such a
beautiful sort of ideal to strive for we
we humans scientists would like you like
this to hope that there is a beautiful
structure to this was like you said
there are segregated regions that are
responsible for the different function
how much hope is there to find such
regions in terms of looking at the
progress of studying the brain oh I
think in Nomis progress has been made in
the past you know 20 or 30 years you
know so this is beyond incremental you
know at the advent of brain imaging the
very notion of functional segregation
was just a hypothesis based upon a
century if not more of careful
neuropsychology looking at people who
had lost via insult or traumatic brain
injury particular parts of the brain and
then saying well they can't do this or
they can't do that for example losing
the visual cortex and not being able to
see or using losing particular parts of
the visual cortex or regions known as v5
or the middle temporal region MT
noticing that they selectively could not
see moving things and so that created
the the hypothesis that perhaps movement
processing visual movement processing
was located in this functionally
segregated area and you could then put
go and put invasive electrodes in animal
models and say yes indeed we can excite
activity here we can form receptive
fields that are sensitive to or defined
in terms of visual motion but at no
point could you exclu the possibility
that everywhere else in the brain was
also very interested in visual motion by
the way I apologize to interrupt buzz
tiny little tangent you said animal
models just out of curiosity from your
perspective how different is the human
brain versus the other animals in terms
of our ability to study the brain well
clearly the far further away you go from
a human brain the the greater the
difference is but not not as remarkable
as you might think so people will choose
their level of approximation to the
human brain depending upon the other
kinds of questions that they want to
answer so if you're talking about sort
of canonical principles of
microcircuitry it might be perfectly
okay to look at a mouse indeed you could
even look at flies worms if on the other
hand you wanted to look at the finer
details of organization of visual cortex
and v1 v2 there's a designated sort of
patches of cortex that may or may do
different things indeed do you probably
want to use a primate that looked a
little bit more like a human because
there are lots of ethical issues in
terms of you know the use of non-human
primates to transfer questions about the
about human anatomy I think most people
assume that the most of the important
principles are conserved in a continuous
way you know from right from well yes
worms right to yummy
so now returning to so that was the
early of ideas are studying the the the
really functional regions of the brain
base if there's some damage to it to try
to infer that there's that part of the
brain might be somewhat responsible for
this type of function so what where does
that lead us what are the next steps
beyond that right well this actually
reverse a bit come back to your sort of
notion that the brain is a magic sue but
that was actually a very prominent idea
at one point notions such as Lashley's
law of mass action inherited from the
observation that for serve
animals if you just took out spoonfuls
of the brain it didn't matter where you
took these spoonfuls out they always
showed the same kinds of deficits so you
know it was very difficult to infer
functional specialization pure on the
base basis of lesion deficit studies but
once we had the opportunity to look on
the brain or lighting up in its it's
literally it's sort of excitement
neuronal AM excitement when looking at
this versus that one was able to say yes
indeed
these functionally specialized responses
are very restricted and then they're
here or they're over there if I do this
then this part of the brain lights up
and that became doable in the early 90s
in fact shortly before with the advent
of positron emission tomography and then
functional magnetic resonance imaging
came along in the early 90s and since
that time there has been an explosion of
discovery refinement confirmation you
know there are people who believe that
it's all in the anatomy if you
understand the anatomy then you
understand the function at some level
and many many hypotheses were predicated
on a deep understanding of the anatomy
and the connectivity but they were all
confirmed and taking much further with
newer imaging so that's what I meant by
we've made an enormous amount of
progress in in this century indeed and
in relation to the previous century by
looking at these funky selective
responses but that wasn't the whole
story so there's a sort of near
phrenology but finding bumps and
hotspots in the brain that did this or
that the bigger question was of course
the functional integration how all of
these regionally specific responses were
orchestrated how they were distributed
how did they relate to distributed
processing and indeed representations in
the brain so then you turn to the more
challenging issue of the integration the
connectivity and then we come back to
this beautiful sparse recurrent
hierarchical connectivity that seems
characteristic of the brain and probably
not many other organs and but
nevertheless we'll come back to this
this challenge of trying to figure out
how everything is integrated but what's
your feeling what's the general
consensus how we moved away from the
magic soup view of the brain yes so
there is a deep structure to it yeah
that and then maybe further question you
said some people believe that the
structure is most of it that you can
really get at the core of the function
by just deeply understanding the
structure yeah where do you sit on that
do you
I think it's called some monster yes
yeah yes it's a worthy pursuit of going
of studying of through imaging and all
the different methods to actually study
no absolutely let's go yeah yeah sorry
I'm just I'm just nutty you you you were
accusing me of using lots of long words
and then you introduce one that which is
deep which is interesting and because
deep is this or Millenial equivalent of
hierarchical so if you've put a deep in
front of anything
you're very millennial and start
trending but you yes you're also
implying a hierarchical architecture so
that's it is a depth which is for me the
beautiful thing that's right the word
deep kind of yeah exactly it implies
hierarchy I didn't even think about that
that indeed the implicit meaning of the
word deep is a hierarchy yep yeah yeah
so deep inside the onion is a central
view so if you put maybe briefly if you
could paint a picture of the kind of
methods of neuro imaging maybe the
history which you are a part of you know
from statistical parametric mapping I
mean just what what's out there that's
interesting for people maybe outside the
field that to understand of what are the
actual methodologies of looking inside
the human brain right well there you can
answer that question from two
perspectives basically it's the modality
you know what kind of signal are you
measuring and they can range from and
let's limit ourselves to some imaging
based non-invasive
techniques so you've essentially got
brain scanners and Brent's cannons can
either measure the structural attributes
the amount of water of the Mount of fat
on the amount of iron in different parts
of the brain you can make lots of
inferences about the structure of the
organ of the sort that you might have
abuse from an x-ray but a you know a
very nuanced x-ray that is looking at
this kind of property of that kind of
property so looking at the anatomy not
invasively
is would be the first sort of earner
imaging that people might want to employ
then you move on to the kinds of
measurements that reflect dynamic
function the most prevalent of those
fall into two camps you've got these
metabolic sometimes hemodynamic blood
related signals so these metabolic
and/or hemodynamic signals are basic
proxies for elevated activity and
message passing and neuronal dynamics in
particular parts of the brain
characteristically though the time
constants of these hemodynamic or
metabolic responses to neural activity
are much longer than the neural activity
itself and this is uh this is refering
forgive me for the dumb questions but
this would be referring to blood like
the flow of blood absolutely
so there's a ton of it seems like
there's a ton of blood vessels in the
brain yeah so but what's the interaction
between the flow of blood and the
function of the new and neurons is there
an interplay there or yeah yeah yeah and
that interplay accounts for several
careers of world-renown solutely so this
is known as neurovascular coupling is
exactly what you said it's how how does
a neural activity the neuronal
infrastructure natural message passing
that we think underlies our capacity to
perceive and act how is that coupled to
the vascular responses that that supply
the energy for that neural processing so
there's a delicate web or
of large vessels arteries and veins that
gets progressively finer and finer in
detail until it perfuses at a
microscopic level the machinery where
little neurons lie so coming back to
this sort of onion perspective we were
talking before using the onion there's a
metaphor for a deep hierarchical
structure but also I think it's just an
anatomical anatomically quite a useful
metaphor all the action all the heavy
lifting in terms neural computation is
done on the surface of the brain and
then the interior of the brain is
constituted by fatty wires essentially
axonal processes that are enshrouded by
myelin sheaths and these give the ER
when you dissect them they look fatty
and white and so it's called white
matter as opposed to the actual neuro
peel which does the computation
constituted largely by neurons and
that's known as gray matter so the gray
matter is a a a surface or a skin that
sits on top of this big ball now we are
talking magic soup but it's a big ball
of collections like spaghetti very
carefully structured with sparse
connectivity that preserve this deep
hierarchical structure but all the
action takes place on the surface on the
cortex of the onion and that means that
you have to supply the right amount of
blood flow the right amount of nutrient
which is rapidly absorbed and used by
neural cells that don't have the same
capacity that your leg muscles would
have to basically spend their energy
budget and then claim it back later
so one peculiar thing about cerebral
metabolism brain metabolism is it really
needs to be driven in the moment which
means you basically have to turn on the
taps so if there's lots of neural
activity in one part of the brain a
little patch of a cup few millimeters
even less possibly you really do have to
water that piece of the garden now and
quickly and that by quickly I mean
within a couple of seconds so that
contains a lot of infant
the imaging could tell you a story of
what's happening absolutely but it is
slightly compromised in terms of the
resolution so the the deployment of
these little micro vessels that the
water the garden to enable the activity
to to the neural activity to play out
the the spatial resolution is in order
of a few millimeters and crucially the
temporal resolution is the order of a
few seconds so you can't get right down
and dirty into the actual spatial and
temporal scale of neuronal activity in
and of itself to do that you'd have to
turn to the other big imaging modality
which is the recording of
electromagnetic signals as they're
generated in real time so here the
temporal bandwidth if you like on the
temp the low limit on the temporal
resolution is incredibly small you're
talking about near nalle' seconds
milliseconds and then you can get into
the phasic fast responses there is in of
itself the neural activity and start to
see the succession or cascade of
hierarchal recurrent message-passing
evoked by a particular stimulus but the
problem is you're looking at
electromagnetic signals that have passed
through an enormous amount of magic soup
or spaghetti of collectivity and through
the scalp and the skull and it's become
spatially very diffused so it's very
difficult to know where you are so
you've got this sort of catch-22 you can
either use an imaging modality it tells
you within millimeters which part of the
brain is activated we don't know when or
you've got these electromagnetic a EEG m
EG setups that tell you to within a few
milliseconds when folks something has
responded being aware so you've got
these two complementary measures either
in direct via the blood flow or direct
via the electromagnetic signals caused
by neural activity these are the two big
imaging devices and
the second level of responses your
question what what are they yeah from
the outside one of the big ways of of
using this technology so once you've
chosen your the kind of mirror imaging
they want to use to answer your set
questions and sometimes it would have to
be both then you've got a whole raft of
analyses time series analysis usually
that you can bring to bear in order to
answer your questions or address your
hypothesis about those data and
interesting that they they've both fall
into the same two camps we're talking
about before you know this dialectic
between specialization and integration
differentiation and integration so it's
the cartography that blob ology analyses
my apology and probably shouldn't
transfer much but just the herd of fun
word the blur the robot ology blood
ology its ideologies of which means the
study of blobs that's nothing for are
you being witty and humorous or is there
an actual there's the word blob ology
ever appear in a text book somewhere it
would appear in a popular book it would
not appear in a worthy specialist
journal yeah it's the fond word for the
study of literally little blobs on brain
maps showing activations so the kind of
thing that you'd see in you know the
newspapers on ABC or BBC reporting the
latest finding from a from brain imaging
interestingly though the maths involved
in that stream of analysis does actually
call upon the mathematics of blobs so
seriously they actually called Euler
characteristics and you know they have a
lot of fancy names in mathematics we'll
talk about about your ideas in free
energy principle I mean there's a echoes
of blobs there when you consider sort of
entities so mathematically speaking yes
absolutely yeah yes anyway well the
first circumscribe well-defined
yes--you entities of
well in from the free energy point of
view entities of anything but from the
point of view of the analysis the
cartography of you know of the brain
these are the entities that constitute
the evidence for this functional
segregation you have segregated this
function in this blob alledge is not
outside of the blob
that's basically the oh if you were a
map maker of America and you did not
know instruction the first thing were
you doing constituting or creating a map
will be to identify the cities for
example or the route mountains and all
the rivers all of these uniquely
spatially localizable features possibly
topological features have to be placed
somewhere because that requires our
mathematics of identify what does a set
it City look like on a satellite image
or what does a river look like I want as
a mountain look like what would it you
know what data features wood is wood
evidence that that particular table you
know that particular thing that you
wanted to put on the map and they
normally are characterized in terms of
literally these blobs or these of now
the way looking at and this is a certain
statistical measure of the degree of
activation crosses a threshold and in
crossing that threshold in a spatially
restricted part of the brain it creates
a blob and that's basically what
physical parametric mapping does it's
basically mathematically finessed
phlebology okay so those you kind of
describe these two methodologies for one
is temporally noisy one is spatially
noisy and you kind of have to play and
figure out what what can be useful yeah
it'd be great if you can sort of comment
I got a chance recently to spend a day
at a company called neural link that
uses brain computer interfaces and their
dream is to well there's a bunch of sort
of dreams but one of them is to
understand the brain by sort of you know
getting in there past the so calls that
are factory wall getting in there be
able to listen communicate both
directions what are your
about this the future of this kind of
technology of brain computer interfaces
to be able to now have a have a window
or direct contact within the brain to be
able to measure some of the signals to
be able to send signals to understand
some of the functionality of the brain
ambivalent my sense is ambivalent so
it's a mixture of good and bad and I
acknowledge that freely so the good bits
if you just look at the legacy of that
kind of reciprocal but invasive geo
brain stimulation I didn't paint a
complete picture when I was talking
about some of the ways we understand the
brain prior to your imaging it wasn't
just leave lesion deficit studies some
of the early work in fact literally a
hundred years from where we're sitting
at the institution neurology what was
done by stimulating the brain of say
dogs and looking at how they responded
either but with them the muscles or with
the salivation and imputing what that
part of the brain must be doing that if
i stimulated then yeah and i vote this
kind of response then that tells me
quite a lot about the functional
specialization so there's a long history
of brain stimulation which in continues
to enjoy a lot of attention nowadays
positive attention oh yes absolutely
you know deep brain stimulation for
Parkinson's disease is now a standard
treatment and also a wonderful vehicle
to try and understand the neuronal
dynamics underlie movement disorders
like Parkinson's disease even interest
in transmitting its magnetic stimulation
stimulating with the magnetic fields and
will it work in people who depressed for
example quite a crude level of
understanding what you're doing but you
know there are there is historical
evidence that these kinds of brute force
and interventions do change things then
you know a little bit like buying the TV
whether the valves are working properly
but it still it works so
you know there is a long history brain
computer interfacing a BCI
I think is a beautiful example of that
it's sort of carved out its own lesion
its own aspirations and they've been
enormous advances within limits advances
in terms of our ability to understand
how the brain the embodied brain engages
with the world I'm thinking of here of
sensory substitution the augmenting our
sensory capacities by giving ourselves
extra ways of sensing than sampling the
world ranging from sort of trying to
replace lost visual signals through to
giving people completely new signals so
the well I think most engaging examples
of this is equipping people with a sense
of magnetic fields so you can actually
give them magnetic sensors that enable
them to feel should we say tactile
pressure around their tummy where they
are in relation to them to the magnetic
field of the earth incredible and after
a few weeks they take it for granted
they integrated the embody assimilate
this new sensory information into the
way that they feet literally feel their
world were now equipped with this sense
of magnetic direction so that tells you
something about the brain's plastic
potential to remodel to in term and its
plastic capacity to suddenly try to
explain the sensory data at hand by
augmenting or augmenting the the sensory
sphere and the kinds of things that you
can measure clearly that's purely for
entertainment and understanding the knee
or the nature and the power of our
brains I would imagine the most BCI is
pitched at solving clinical and human
problems such as locked-in syndrome
paraplegia or replacing lost sensory
capacitors like blindness and death
deafness so then we come to the more on
the negative part of my own the other
side of it so I you know I don't want to
be deflation because much of my
deflationary comments was probably large
out of ignorance there anything else but
generally speaking the the bandwidth and
the bit rates that you get from brink of
Pewter interfaces as we currently know
them we're talking about bits per second
so that would be like me only being able
to communicate with any world or with
you using very very very slow Morse code
and it is not in the in even within an
order of magnitude near what we actually
need for an inactive realization of what
people aspire to when they think about
sort of curing people with paraplegia or
replacing site despite heroic efforts so
one has to ask is there a lower bound on
the kinds of recurrent information
exchange between a brain and some
augmented or artificial interface and
let me come back to interestingly what I
was talking about before which is your
if you're talking about function in
terms of inference and I presume we'll
get to that later on in terms of the
free energy principle Minh the moment
they may be fundamental reasons to
assume that is the case we talk about
ensemble activity we're talking about
basically for example let's paint
challenge facing brain-computer of
interfacing in terms of controlling
another system that is highly and deeply
structured very relevant to our lives
very nonlinear the rests upon the kind
of non-equilibrium steady states and
dynamics that the brain does the weather
right so good example here imagine you
had some very aggressive satellites that
could produce signals that could be
termed some little parts of the of the
weather system and then what you're
asking now is can i meaningfully get
into the weather and change it
meaningfully and make the weather
respond in a way that I want it to
you're talking about chaos control on a
scale which is almost unimaginable so
there may be fundamental reasons why BCI
as you might read about it in a science
fiction novel aspirational BCI may never
actually work in the sense that to
really be integrated and be part of the
system isn't impermanent requires you to
have evolved with that system that you
know you you have to be part of a very
delicately structured deeply structured
dynamic ensemble activity that is not
like rewiring a broken computer or
plugging in a peripheral interface
adapter it is much more like getting
into the weather pans or a come back to
your magic soup is getting into the
active matter and meaningfully relate
that to the outside world so I think
there are an enormous challenges there
so I think the example the weather is a
brilliant one and I think you paint a
really interesting picture and it wasn't
as negative as they thought it's
essentially saying there's it might be
incredibly challenging including the
lower bound of the bandwidth and so on I
kind of so and just to full disclosure I
come from the machine learning world so
my my
natural thought is the hardest part is
the engineering challenge of controlling
the weather of getting those satellites
up and running in and so on and once
they are then the rest is of
fundamentally the same approaches that
allow you to be to win in the game of Go
will allow you to potentially play in
this soup in this chaos so I have I have
a hope that so machine learning methods
will will help us play in the soup as
but perhaps you're right that it is a
via biology and the brain is just an
incredible an incredible system that may
be almost impossible to get in but for
me what seems impossible is is the
incredible mess of blood vessels that
you also described without you know we
also value the brain you can't make any
mistakes you can't damage things so to
me that engineering challenge seems
nearly impossible one of the things I
was really impressed by at neuro-link is
just just talking to brilliant
neurosurgeons and the roboticists that
it made me realize that even though it
seems impossible if anyone can do it
it's some of these world-class engineers
that are trying to take it on so so I
think the conclusion of our discussion
here is of this part is is basically
that the problem is really hard but
hopefully not impossible absolutely so
if it's ok let's start with the basics
so you've also formulated a fascinating
principle the free energy principle
could we maybe start at the basics and
what is the free energy principle well
in fact the free energy principle
inherits a lot from the building of
these data analytic approaches to these
you know very high dimensional time soon
as you get get from the brain so I think
is interesting to acknowledge that and
in particular the analysis tools that
try to address the other side which is a
functional integrations on the
connectivity analyses on the one hand
but I should also acknowledge it
inherits an awful lot from machine
learning as well so the free energy
principle and is just a formal statement
that the the existential imperatives for
any system that manages to survive in a
changing world
is can be cast as a an inference problem
in the sense that you can interpret the
probability of existing as the evidence
that you exist and if you can write down
that problem of existence as a
statistical problem that you can use all
the maths that has been developed for
inference to understand and characterize
the ensemble dynamics that must be in
play in the service of that inference so
technically what that means is you can
always interpret anything that exists in
virtue or being separate from the
environment in which it exists as trying
to minimize variational free-energy and
if you're from the machine learning
community you will know that as a
negative evidence lower bound or a
negative elbow which is the same as
saying you're trying to maximize or it
will look as if all your dynamics are
trying to maximize the complement of
that which is the marginal likelihood or
the evidence for your own existence so
that's basically that you know that the
free energy principle of it but even
take a sort of a small step back or as
you said the existential imperative
there's a lot of beautiful poetic words
here but to put it crudely there's a
it's a fascinating idea of basically
just of trying to describe if you're
looking at a blob how do you know this
thing is alive
what does it mean to be alive what does
it mean to be to exist and so you can
look at the brain you can look at parts
of the brain or you this is just the
general principle that applies to almost
thing and ye and you system it that's
just a fascinating sort of
philosophically at every level question
and the methodology to try to answer
that question what does it mean to be
alive yeah so that that that's a huge
endeavor and it's nice that there's at
least some from some perspective a clean
answer so maybe can you talk about that
optimization view of it so what what's
trying to be minimized to maximize what
a system that's alive what is it trying
to minimize right you've you've made a
big move yes to make big moves but
you've assumed that the things the thing
exists before the in a state that could
be living on nonliving so I may ask you
or what licenses you to say that
something exists that's why I use the
word existential it's beyond living it's
just existence so if you drill down onto
the definition of things that exist then
they have certain properties if you
borrow the maths from non-equilibrium
steady state physics that enable you to
interpret their existence in terms of
this optimization procedure so it's good
you introduce the word optimization so
what the free-energy principle in its
sort of most ambitious but also most
deflationary and simplest says is if
something exists then it must by the
mathematics of non-equilibrium steady
state exhibit properties that may
look as if it is optimizing a particular
quantity and it turns out that
particular quantity happens to be
exactly the same as the evidence lower
bound in machine learning or Bayesian
model evidence in Bayesian statistics or
and then I can list a whole other you
know list of ways of understanding this
this this key quantity which is a bound
on on surprisal self information if you
know information theory there are whole
there are a number of different
perspectives on this contry it's this
basically the log of probability of
being in a particular state I'm telling
this story as an honest and attempt to
answer your question and I'm answering
it as if I was pretending to be a
physicist who was trying to understand
the fundaments of non-equilibrium steady
state and I shouldn't really be doing
that because the last time I was taught
physics I was in my twenties what kind
of systems when you think about the free
energy principle what kind of systems
are you imagining it's a sort of more
specific kind of case study you know I'm
imagining a range of systems but you're
at its simplest
a sim a single-celled organism that can
be identified from its eco nation or its
environment so at its simplest that
that's basically what what I always
imagined in my head and you may ask well
is there anything how on earth can you
even in elaborate questions about the
existence of a acing a single drop of
oil for example yeah
what but there aren't D questions there
why doesn't the oil why doesn't the
thing the interface between the drop of
oil that contains an interior and the
thing that is not the drop of oil which
is the solvent in which it is immersed
how does that interface persist over
time why doesn't the oldest dissolve
into solvent
so what special properties of the
exchange between the surface of the oil
drop and the external states in which
it's immersed if you're physicists say
would be the heat bath you know you've
got a you've got a physical system an
ensemble again with about ten stomachs
ensemble dynamics an ensemble of ik of
atoms or molecules immersed in the heat
path but the question is how did the
heat bath get there and why is it not
dissolved why was it maintaining itself
exactly what actions is it I mean it's
such a fascinating idea of a drop of oil
and I guess it would dissolve in water
wouldn't dissolve in water so what
precisely so why not so why not why not
and how do you mathematically describe
me is such a beautiful idea and also the
idea of like where does the thing where
does the drop of oil and yeah and where
does it begin right so I mean you're
asking the questions deep in in a normal
area but what you can do you see so this
is a deflationary part of it
can I just qualify mouths so by saying
that normally when I'm asked this
question I answer from the point of view
of a psychologist we talk about
predictive processing and pretty coding
and you know the brain is an inference
machine but you haven't asked me from
that perspective I'm answering from the
point of view of a physicist so you you
know the question is not so much why but
if it exists what properties must it
display so that's the deflation in part
the 300 prints we print the 300
principal does not supply and answer as
to why it's saying if something exists
then he must display these properties
that's that's the other sort of the
thing that's on offer and it so happens
that these properties a must display are
actually intriguing and have this
inferential gloss this there's this sort
of self evidencing loss that inherits
from the fact that the very preservation
of the boundary between the oil drop and
the not oil drop requires an optimized
of a particular function or a functional
that's that defines the presence of the
existence of of this order which is why
I started with existential imperatives
and the it ISM it is a necessary
condition for existence that this must
occur because the thing the boundary
basically defines the things that's
existing so it is that self-assembly
aspect it's that for the you hinting at
in biology sometimes known as Auto
poiesis in computational chemistry Mis
self-assembly it's the what what does it
look like sorry how would you describe
things that configure themselves out of
nothing the where they clearly demarcate
themselves from the states on the soup
in which they are immersed so from the
point of view of computational chemistry
for example you just understand that as
a configuration of a macromolecule to
minimize its free energy is
thermodynamic free energy it's exactly
the same principle that we've been
talking about that thermodynamic free
energy is just the negative elbow it's
the same mathematical calm construct so
the very emergence of existence of
structure or form that can be
distinguished from the environment or
the thing that is not the thing
necessitates the you know the existence
of an objective function then it looks
as if it is minimizing it's finally a
free energy minima and so just to
clarify I'm trying to wrap my head
around so the the free energy principle
says that if something exists these are
the properties it should display yes so
what what that means is we can't just
look we can't just go into a soup and
there's no mechanism if free energy
principle doesn't give us a mechanism to
find the things that exist is that what
it was implying is being applied that
you can kind of use it to reason to
think about like study a particular
system and say
does this exhibit these qualities that's
an excellent question to answer that
after I have to return to your previous
question but what's the difference
between living and nonliving things
actually Society so yeah that maybe we
can go there you kind of drew a line and
and forgive me for the stupid questions
but the you kind of draw a line between
living and existing yeah is there an
interesting sort of distinction
distinction
yeah I think there is so you know things
do exist grains of sand rocks on the
moon trees you so all of these things
can be separated from the environment in
which they are immersed and therefore
there must at some level be optimizing
their free energy taking this sort of
model evidence interpretation of this
quantity that basically means their self
evidencing another nice little twist of
phrase here is that you are your own
existence proof you know statistically
speaking which I don't think I said that
somebody did but I love that phrase you
are your own existence proof yeah
so it's ur existential isn't it I'm
gonna have to think about there for a
few days
yeah the view there's a beautiful line
so the the step through to answer your
question about you know what's it good
for big girl on the following lines
first of all you have to define what it
means to exist which down as you rightly
pointed out you have to define what
probabilistic properties must the states
of something possess so that it has so
it knows where it finishes and then you
write out that down in terms of
statistical independence is again
sparsity again it's not what's connected
or what score elated or what depends
upon what it's what's not correlated and
what doesn't depend upon something again
it comes down to the the deeper
structures not in this is hierarchal
but the suddenly the the structures that
emerge from removing connectivity in
dependency in this instance basically
being able to identify the surface of
the oil drop from the water in which it
is immersed and when you do that you
start to realize well there are actually
four sub kinds of states in any given
universe that contains anything the
things that are internal to the surface
the things that are external to the
surface and the surface in and of itself
which is why I use a metaphor a little
single-celled organism that has an
interior and exterior and then the the
surface of the cell and that's
mathematically a Markov blanket just to
pause I'm in awe of this concept that
there's the stuff outside the surface
stuff inside the surface in the surface
itself the Markov blanket it's just the
most beautiful kind of notion about
trying to explore what it means to exist
you're automatically I apologize this is
a beautiful idea but came out of
California so that's I changed my mind I
take it all so sorry anyway so what you
were just talking about the surface
about the market yeah so this surface or
this blanket these blanket states that
are this you know the because they are
now defined in relation to these
Independence's and your Walker what
different states internal or blanket or
external states can which ones can
influence each other and which cannot
influence each other you can now apply
standard results that you would find in
non equilibrium physics or steady state
or thermodynamics or hydrodynamics
usually out of equilibrium solutions and
apply them to this partition and what it
looks like as if all the Norman normal
gradient flows that you would associate
with any non equilibrium system
apply in such a way that to part of the
Markov blanket and the internal states
seem to be hill climbing or doing a
gradient descent on the same quantity
and that means that you can now describe
the very existence of this oil drop you
can write down the existence of this
holdup in terms of flows dynamics
equations of motion where the blanket
States or part of them we call them
active States and the internal states
now seem to be and must be trying to
look as if they're minimizing the same
function which is a lot of probability
of occupying this but these states the
interesting thing is that what would
they be called if you were trying to
describe these things there were what
we're talking about are internal states
external states and blanket States
now let's carve the blanket States into
to sensory states and active States
operationally it has to be the case that
in order for this carving up in two
different sets of states to exist the
active States the Markov blanket cannot
be influenced by the external states and
we already know that the internal States
can't be influenced by the external
States cousin the blanket separates them
so what does that mean well it means the
active States the internal states are
now jointly not influenced by external
states they only have autonomous
dynamics so now you've got a picture of
an oil drop that has autonomy
it has autonomous States it has
autonomous days in the sense that there
must be some parts of the surface of the
oil drop that are not influenced by the
external states and all the Interior and
together those two states endow even a
little oil drop with autonomous states
that look as if they are optimizing
their variational free energy or their
negative elbow their model evidence
and that would be an interesting
intellectual exercise and you could say
you can even go into the