Huh? Okay.
This is the Convergent Science Network podcast.
Leading researchers in the domain of neuroscience, brain theory and technology
are interviewed by Paul Verschoor and Tony Prescott.
This is Paul Verschoor with the Convergent Science Network podcast together
with my colleague Tony Prescott and with Murray Shanahan who was speaking this
morning in our summer school here in Barcelona.
And Murray, you presented, let's say, a bit more an abstract view on cortical
dynamics and how we could think about cortical dynamics.
So what are the key features of this model of cortex you're presenting and what
are you trying to explain?
So I think the key feature of the model, which reflects my current interests
really, is the richness of the dynamics that it produces.
Produces so um metastability is
the was one of the main themes that i talked about this morning so
that's one important feature uh which
is related to um to the size
of the of the repertoire of different states that the
system can produce as well so metastability just to
to spell that out so uh so a
system is is metastable uh basically if
it's instead of um instead of falling
into a stable attractor it rather it
sort of lingers in the vicinity of an attractor like state
um without you know stabilizing and
falling straight right into that attractor um and
then maybe uh but then maybe moves on to a different attractor
so um so for uh
for listeners who who for whom that isn't very
familiar uh terminology so uh so
the idea would be uh or one kind of
attractor state would would be where um where
all the parts of the brain were all oscillating in synchrony all at the same
time and of course this never happens and it would be it would be very very
bad if it did but that would be that's one kind of attractor state that you
get you can get in dynamical systems is where you've got a whole load of oscillators
they're all completely synchronized with each other.
In the brain, I think the kind of dynamics we see is where there's lots of oscillations going on,
there's lots of patterns of synchrony, but what we're really interested in is
we're interested in states of partial synchrony, where some parts of the brain
are synchronized with other parts of the brain.
That often represents that they're talking to each other.
And we're also interested in metastable states of synchronization,
so where it doesn't stay in that exact combination of synchronized parts,
but that sort of coalition of synchronized parts is like that for a while,
and then the coalition breaks apart and it finds another state.
But now you could argue that this metastable state, which is defined in a state
space, is in some sense arbitrary because as observer,
I can decide at what level I define my state space and what's metastable at
one level of description can just be a fixed point attractor at another level of description.
So do you see that as a problem, this arbitrariness of how we define such a state space?
Well, it certainly doesn't have to be so arbitrary and there are various ways
that you can try to quantify it precisely.
Precisely so uh so one way for example uh
is to is to look at um uh
in the case of these coupled oscillators if you
have a whole collection of oscillators that are say that are that are decoupled
um then um then there's no connection between them at all really and in that
case that's that's the sort of um uh the trivial case where where there's not
going to be any a any um you know,
synchronization except coincidental synchronization.
So that's the statistical baseline.
So if you know, if you're looking at your system and things fall into synchrony.
A lot more a lot more than you would expect them to uh in in the case of that kind of um,
that kind of baseline statistics then you know that that's that's serious synchronization
a serious sort of uh are you are you saying with this meta stability concept that let's say between.
Stability and complete chaos it's in this intermediate zone that interesting
things are happening yes that's certainly certainly like an edge of chaos kind
of it's very much Much related to that, yeah, very much related to that.
So there is a kind of criticality phenomenon there where the sort of systems
that you're interested in or the dynamics that you're interested in is poised
between order and disorder.
So a highly ordered system will be one that was just, say, completely synchronized,
and a highly disordered system will be one like the one I just described,
completely decoupled, there's no relationship between the different oscillators.
What you're interested in is this state which is between the two where there's
a certain amount of order and a certain amount of disorder.
So the parts are sort of interacting with each other, but there isn't one dominant state that persists.
So now you were defining metastability also in relation to the physiology of
people like Pascal Friess, who comes from the school of Wolf Singer,
who sort of ascribe a lot of importance to, let's say, synchronization phenomena in the brain.
Yeah. So how are these two things now related?
Yeah, so that's very much the backdrop for the discussion we've just had,
really, is, so Pascal, according to Pascal Friese's communication through coherence hypothesis.
Which makes a lot of sense to me, you can think of two populations of neurons
that are oscillating in synchrony as basically in a position to communicate
with each other and cooperate with
each other to influence each other and exchange information and so on.
Because when the troughs and peaks of their activity coincide,
then they're in a good position to exchange spikes, so long as all the delays work out and so on.
So that's the reason why synchronisation is one hypothesis, but it's one reason
why synchronised collections of neurons might be of interest if you're looking
at populations on different parts of the brain.
Counterintuitive because in some sense
if i would look at these two neurons and i would plot
their activity over time but against each
other they might actually be in some sort
of fixed point they might be in a very limit a limit cycle but it would not
look like a very variable metastable state so how is then the coherence in gamma
reflecting this metastability or this criticality phenomena yeah well first
of all you've got to look at different time scales um.
So, um, uh, and, and you're also looking at whole populations of neurons.
So you're certainly not looking at just single neurons, but you're looking at
populations of neurons.
So, so you might look at one population of neurons. You might see that the overall
firing rate, the mean firing rate is oscillating in a, you know,
for, for a while is oscillating in a really, in a regular kind of way,
say a gamma type frequency, 40 Hertz or something.
And, um, and then you've got another population that maybe is also oscillating in the same kind of way.
And the oscillations are in are in synchrony and then
then those two populations are in a position to
exchange information um and so
that's the kind of situation that that that you're interested in
now the information is going to be exchanged during those
little peaks of of of activation and
during a peak of activation um then that
whole population is going to be most receptive to incoming spikes
and is going to be the individual neurons going to be more likely statistically
more likely to fire than in a trough of
of activation and so that's why the communication
can take place you know in that in that time but surely that
if you're thinking about maximizing the opportunity to fire the postsynaptic
neuron you don't want to come when it's firing you want to come just a bit before
that yeah okay that's absolutely true but but but you've uh in within 40 hertz
you've got quite a few milliseconds uh it's quite a it's a
it's a little window of opportunity so you want you know the
timing's got to work out that's that's for sure uh you've
got a window of opportunity you know um uh so quite
a lot can happen in that in that little window um uh and uh and also you you've
also got to worry about the delays so so you know the there are going to be
delays between uh depending on the length of the connection and the type of
connection and so on they're going to be delays as well so all of that's got to work out.
But the point is that it's getting,
the right relationship in the phase to work out is going to maximize the opportunity
for information to be exchanged.
And also, allows for competition.
So you might have three populations. Two populations are trying to influence
a third, and one is going to win out by entraining the downstream population.
Once it's become entrained, then it can shut out the other guy who will end
end up in the opposite phase of
the relationship and that's when the information sort of gets exchanged.
The dominant idea about gamma is that it's very much a locally generated response, right?
So you have let's say cortical circuits or the hippocampus, it's actually the
tight coupling of excitatory neurons with local fast GABA-A mediated inhibitory neurons.
So as soon as you start to drive the excitatory cells they will drive the local
inhibition that will shut down
the whole population. So now you get the rhythmicity in the gamma range.
So you could then first argue, okay, if I have a gamma in two areas,
it means, okay, there's a drive onto these cells. This is the first thing.
And then you can say, well, now if these two areas talk to each other,
okay, by necessity it will happen within this gamma rhythm because they cannot
fire at any other rhythm, but they become driven.
Yeah. So then how meaningful is it really to talk about, let's say,
enhanced communication between areas when gamma becomes more or less coherent?
Yeah. Well, I think it's particularly meaningful in the context of this kind
of competition. So where, indeed, you may have….
You know several populations several neuronal populations are
all as it were relevant to the ongoing situation so
if you've got you've got uh uh well
actually actually an example a potential example is
binocular rivalry maybe you know about binocular rivalry so where
you've got two um two gratings one
horizontal one vertical uh presented to each eye and uh so the right eye can
see the say the horizontal grating the left eye the vertical grating and uh
and then there's this binocular rivalry phenomenon whereby you don't see the
two gratings overlaid on each other, you don't see a crisscross pattern,
but rather you tend to see consciously or become aware of either one or the other.
The one will fade in and out while the other one fades in and out.
So that's known as binocular rivalry.
That's an example of where there's clearly some competition petition going on
between rival neuronal populations.
One way possibly to account for that is in terms of this communication through
coherence idea, where one population temporarily entrains another population further downstream.
That dynamics goes on for a little while, but then But then it can sort of burn
itself out for statistical reasons, basically, because they're equally salient stimuli.
And then the other population becomes entrained instead.
And so you have this flickering in and out. And we had a paper in Frontiers
in Computational Neuroscience paper.
I had a paper with one of my PhD students, Mark Wildey, where we showed that
kind of phenomenon in the context of Pascal Frese's ideas.
So now we know a little bit, let's say, the physiological phenomena we would
like to understand and explain, right?
And what you told us is that actually you start to model.
These large-scale network phenomena with fairly
abstract oscillator models yeah so so
why did you make that step yes um i
think i made that step just because i just fell into it it wasn't really that
i uh that i i took a uh you know a very carefully um a very careful decision
about what i should look into next um uh but i mean my you know my background
is in computer computer modeling.
So, so my natural, my natural tendency is to tinker with my computer model.
So I'm sitting there with MATLAB fiddling around and, uh, playing with my computer
models. And that's what I, that's what I do.
Um, and then, yeah, but, uh, but, but I'm always vulnerable to,
you know, there's, so, so a lot of people are interested in that kind of thing
in its own, in its own right.
Physicists in particular, you know, kind of quite interested in that sort of thing in its own right.
But then if you're trying to make a claim about its relevance to brain dynamics
or cognition or anything like that, then of course you're instantly vulnerable
to the criticism that, well, where's the data? How does it relate to data?
And so of course it's very important to try and relate it to real data.
But in my case it's been a bit back to front, so I was tinkering around with
the models for a lot and only a little bit later did the opportunity to make
it match up with real data come in.
So what are the key parameters that you then fiddle with when you're sitting
behind MATLAB on your desktop computer in your office?
Ah, well, so in coming up with that particular model, the metastable chimera
states, I tinkered with so many things, but particularly with the network construction.
So, yeah, the sparsity of the network, lots of parameters to do with the network construction. Yeah.
Yeah, the parameters of the oscillator models, the coupling.
I mean, there are hundreds of them.
And of course, if I was a good mathematician, I mean, I'm handicapped by being
a mediocre mathematician, by not dealing with real data, not being a proper scientist.
The only thing I'm any good at is programming. So, you know, so you end up tackling,
it's like Dorothy just now was talking about, you know, if you're a capuchin
and you see you've got a hammer stone, then you see everything is a nut.
Well, it's a little bit like that with programming, you know,
I see everything as a programming problem. Right.
So, I tend to… But for the model to close that, I think the key parameters you're
controlling is, let's say, the phase of your oscillators, I guess the transduction
delay in their interactions,
these would be roughly the key parameters that would be dominating the properties of your network.
Yeah, so the coupling, so the two main parameters that we end up with fiddling
around with are the coupling and the delays.
I'd just like to go into those models a bit more because one of the things that
surprised me in your talk was you described some of the history of the study
of oscillators, which has been going on for hundreds of years.
But then there's some fairly fundamental discoveries that you have made,
and I think you mentioned a 2002 paper about properties of oscillators that we didn't know about.
And uh can you say a bit more about those discoveries why
didn't we know those things before and uh you
know is there a lot much more to know about these simple oscillator systems
i know there are large numbers of oscillators but there seems to be um potentially
for areas like brain science lots of scope for more uh discoveries in this yeah
i think i think it's because the so So it's physicists,
you know, do all the serious,
you know, legwork with all the heavy lifting with all of these oscillator models.
And they have a particular mindset which tends to focus in on things like,
oh, let's look at, you know, let's look at the limiting case where there's an
infinite number of oscillators, for example.
Or, you know, let's look at the total, you know, where everything is completely connected.
And um and and also
let's look at stability conditions let's look at all these bifurcation diagrams
and the stability conditions and in fact there's kinds of um dynamics which
i'm interested in i i don't think they're actually very surprised that they
exist i just don't think they particularly thought that they were interesting
uh as is what i i suspect and it's only um.
But I mean, they seem to be getting interested, because that particular paper
of mine has been quite well cited by physicists in looking at it, relatively speaking.
But I think it's partly because they probably knew that these sorts of things
went on, but they weren't interested in irregular networks that are not homogenous.
But could you then elaborate a bit what you discovered with this model,
the so-called Chimera states, and why they are interesting? And perhaps also
how they relate to different network configurations, because that's obviously
a key parameter. Yeah, sure.
So why they're interesting is… Well, maybe first define what they are.
So these chimera states is where you have a set of oscillators that basically
partitions into two or more subsets,
well, two subsets really,
where one of them is synchronized and the other is desynchronized.
So that's a chimera state.
When these were first discovered by the physicists, I think they found it quite
surprising that these things could exist, especially because they had very homogenous
connectivity in those ones that they were looking at. So they were a bit surprised by that.
This is certainly of interest from the perspective of brain dynamics,
because you're not really interested in states either where there's no synchrony,
because nothing's happening of interest at all there in the brain.
Nor are you interested in the case where everything is synchronized because
that's basically seizure and nothing interesting is happening either.
You're interested in the case where some populations are synchronized and some
are not, and the ones that are synchronized are the ones that are governing behavior.
So we should try and relate this maybe a little bit to the behavior of animals,
right? Because we've been talking very abstract terms so far.
I think the key thing about your Chimera states was also you see there's a signature
of, let's say, a network property, right?
Where you would say, well, now I start to get minorities, if you want,
in the whole population of oscillators that start to, let's say,
be out of phase with the majority. Yeah, that's right.
Well, actually, I think it's the minority that are going to be synchronized,
and the majority are going to be… Oh, your data looks a bit different, I must say.
The data, in that particular model, it's usually about half and half, actually.
So in that particular model, about half the oscillators end up synchronized
with each other, and about half end up desynchronized.
But I think in real data, you probably expect a smaller set to be synchronized
and to be governing the behavior of the animal at that time.
That's also the link to your meta-stability, right? Where you say,
look, we want to be in between order and chaos, which would roughly be expressed
in such a network as these chimera states.
You say, look, now I'm at this critical point where interesting stuff can happen.
Yeah, but it's not just the chimera states, it's the metastability,
because you don't want to be stuck in one chimera state either. Sure, absolutely.
You want that particular coalition of synchronized oscillators,
you don't want that to be the same one forever.
It's doing its thing for a while and then you're expected
to break apart and then another coalition of oscillators so a different
chimera state to arise exactly but you've got this
uh minority group of coupled uh oscillators and then you say these ones synchronize
these ones that are desynchronized what do you think they're doing are they
just well they're along in the background but not having any influence well
that's i think that's exactly what they're what they're doing so So any evidence for that?
Well, I mean, things that are not synchronized or not even exhibiting regular oscillatory behavior,
I mean, that's mostly what's going on in the brain, right? Right.
But I mean, I wouldn't want to go as far and say that things that aren't oscillatory
aren't of importance. Most of the sensory events aren't oscillatory.
No, no, sure. Okay, sure, sure. Sure, sure, that's true.
Yes, I think, I mean, I see it as, I mean, this is a hypothesis that,
you know, demands empirical validation, but I see it as a mechanism for communication,
and cooperation among populations that are anatomically distributed around the brain.
So I think we should talk a bit about behavior, right? Because all this has been very abstract.
Before we come to behavior, though, when you're saying, okay,
the interesting stuff, happens in the synchronized oscillators yeah is that
because you have some fundamental theory about brain hardware that you know.
Perhaps goes beyond this information transmission thing i mean so what's happening
in the oscillation is information is being passed across the network but these
other decoupled ones are still processing information perhaps and passing it
around and i'm just not clear why that wouldn't be important or.
I'm not saying it's not important i mean i i i'm very
much um uh you know against the idea
of of saying there's one mechanism to
rule them all in the brain because everything i mean
i i come to the brain as an engineer and i'm
constantly trying to do that and that's because that's
my background and training you want some kind of set of engineering principles that
you want to squeeze the brain into and i'm constantly being
brought up short and made to realize that no in fact what you
thought was a principle is only you know is only a
statistical tendency and in fact there's much more of a
mess there so uh so i think this is one mechanism that
may be quite important you know in
the brain but I wouldn't want to say it's the only one on by any
means right I was just wondering if there's some kind of thing about clock speed
or whatever going on in the brain here that you think might be interesting oh
well I think there is actually as as Paul was saying earlier on there is actually
an inherent tendency for these systems to enter oscillatory regimes when they're
driven and if you when you build models you see this pretty quickly you build a
model, say, with spiking neurons.
And there's a lot of excitatory connections there, and you drive it,
it's going to naturally start to oscillate.
And if you've got broadly biologically accurate parameters for your spiking
neurons, it does tend to oscillate in around about the gamma frequency.
It just does that. So that probably falls a little bit out of the neurobiology,
out of the biology and then I suspect that the brain is going to fight I think
whatever rich technology.
Possibilities for dynamics you've got in the brain the brain
and evolution are going to find some way of exploiting them so so
you've got these oscillating things going on and
the brain is going to find a way of exploiting these potential the potential
of this and i think one way is is that it facilitates
competition and cooperation among anatomically
distributed populations that raises on the next question right whether these
chimera states would also just drop out
of the kind of local circuitry of
cortical networks in the same way gamma just you get
it for free or whether something else is required
for that yeah yeah yeah um that's a very good question i suspect that they probably
do drop out but i would uh but i would that would that's a very good question
it would be good to construct models that prove that prove that i mean one reason
why they might drop out is is because,
I mean, another thing I've noticed is that in competitive mechanisms,
you try and build some kind of competitive winner-takes-all mechanism.
I mean, Tony might well know about this as well. But you've got to build it
in a very particular kind of way to make the winner persist,
really persist indefinitely.
And it doesn't take much, especially if you've got small populations of neurons,
for a bit of statistical variation to flip the winner to the other one,
if you've got two, you know, you've got two rivals.
And so that's, again, I think is a natural property.
So that does lead pretty naturally to these kind of chimera states and flipping between them.
But now the next thing you did is essentially you took like the state of the
art, if you want, on human brain connectivity, like the human connectome and
its different statistical properties.
And we had an interesting discussion about how to interpret that this morning,
but we don't have to get into that.
If you now combine these dynamics and the notions of criticality with our understanding
of the human brain, right? So you build these kinds of models. What happened?
Well, so the credit really has to go to the people from here in Barcelona who did this first of all.
Gustavo Deco's group and Joanna Cabral, who built a model based on the Hagman
dataset, where they used the Hagman connectivity dataset.
Set and basically put one of these oscillators, Kuramoto oscillators of the
type that I was playing with, one on each of the nodes in Hagman's network.
So it's very, very similar to the thing that I built, but the thing that I built
used a completely synthetic network.
It was a synthetic network that had some brain-like features such as modularity,
but it was just a little constructed by an algorithm.
Now, they did it with a real DTI-based dataset.
Then the interesting thing was that they showed that they were able to produce
a strong correlation with resting-state fMRI data.
I have to say that it did rather surprise me, and it pleased me enormously that
this only happens when it's in this metastable chimera state,
exhibiting this metastable chimera state dynamics.
So the kind of dynamics that I had found in my model, and nobody really had
published anything showing that Kuramoto models could produce this.
Although, as I say, I think the physicists really knew it. They just didn't
think it was interesting enough.
But the amazing thing was just to find that somebody could show that it could
be used to model real data, and only if it was in that regime.
I would like to push you on that a little bit more, because you could argue,
look, to get to something like the dynamics of a resting state network.
The key thing is that you get your connectivity defined. So then in this case,
you take this notion of functional connectivity, which is a statistical structure of interactions.
You exploit that to seed the topology of your network.
But in this case, it's structural connectivity.
So they were using a structural connectivity. So you have your structural connectivity,
and you start to drive it now.
Couldn't you argue that any kind of activity model, Even a rate-based model,
a linear threshold unit,
would be able to give you a resting state kind of pattern because the secret
is in the connectivity and not in the dynamics of the nodes.
Yeah, that may well be the case.
And indeed, other people, including, again, Gustavo Deco's group,
have used different kinds of dynamical models.
But few of them are simpler than this Kuramoto model.
I mean, it's about as simple as you can get and produce interesting kinds of results, I think.
So that's what strikes me. so the the data
set that you're looking at there is fmri which of course
doesn't really have the temporal resolution to look at
things like oscillations so i mean can
you impact unpack for us a little bit how
that data really validates the model because obviously you're looking
indirectly for evidence for the kind of states that
your model is creating yeah so so so
we're looking at so in this kind of this kind of work um
you're looking at how the you know the model over
a long run over quite a long run so the
model itself is oscillating at a gamma a gamma
frequency but it's producing a phenomena at
a much slower um you know it's producing dynamical
phenomena at a much slower rate as well where where large you
know groups will synchronize for a while and then that synchrony will then go
away so that is happening at a much slower rate so there's a slower dynamics
which you can then look so there's slower dynamics yeah so so so so the actual
model is constructed at the level of this faster dynamics,
but the results that you produce.
Are at the level of the slower dynamics. So that's where you're making the matches.
And what are the phenomena at this slower rate that you're matching on?
So, well, you'd have to ask my co-authors exactly what's going on.
But there's an issue here, right?
Because, as you know, there's quite a discussion again now about what are we
really measuring with fMRI. Yeah, sure.
And there's some correlation with blood flow.
But actually, the link to neural activity is still being debated.
So now, at best, with your oscillator model, you capture, let's say,
the hemodynamics that are sort of indirectly reflecting something neural that
we don't really understand yet.
On the other hand, the origin of your model was neurophysiology,
a very different level of description, both spatially and temporally,
because now we're talking about single cells doing things.
Things right so isn't it quite a stretch to actually
first say ah and i capture pascal frees's synchronization gamma range communication
data and explain it yeah and on top of that i also give you now here the whole
human fmri yeah well actually the origin of the model is a little bit higher
level than that so you're really talking about populations of of neurons so if you're,
so what you what you might maintain that one oscillator
represents would be the activity of a quite a
large population of of neurons exhibiting a
gamma type uh uh bit uh you
know dynamics right but but uh you know i have to say
i have to i completely agree with you i i i it came as a total surprise to me
that you could use this kind of model in this sort of way and that it would
actually um uh match uh the data you know um so but your closest link with the
data is more at this fMRI level.
Than at the neurophysiological level where we look at direct cell...
Well, indeed, but that's only because those are the only experiments that people
have, where they've tried to make the match, as far as I'm aware.
I'd be very interested indeed to do it at a lower level.
Right, but now the other thing is that if you go for the whole,
let's say, populations, where we talk about, let's say, cubic millimeters of
cell volume that we're measuring with fMRI.
So if we describe that as an oscillator model, then what we're capturing is
the activity of millions of neurons of very heterogeneous types.
Also, the dynamics of these neurons might be much more heterogeneous than your model captures.
Absolutely, yeah. Like if you look at gamma, and say, okay, gamma is locally driven, right?
You might actually have all sorts of subpopulations of cells and all sorts of
complex dynamical relations, metastable or not, that you're completely blind to. Yeah, absolutely.
So how are we going to cross that bridge now? Well, I think by,
I mean, as I said quite early in my talk today, that I did actually start off
with spiking neuron models and showed how the spiking neuron models produced
a kind of dynamics that I was interested in,
or complex dynamics that I was interested in.
And people were saying to me, oh, why do you need all the complexity of these spiking neurons?
Why not do it with a simpler model? So I thought, okay, I'll give that a go.
And so I moved, I went up a layer of abstraction and moved to these oscillator
models and kind of got drawn into that because it's a whole world in itself.
But I do very much think it's a good idea to go back down again and to build
larger scale spiking models than the ones I was doing before.
And I have a PhD student, David Baumig, who's done a lot of work.
And there's a PLOS paper last year that describes exactly that.
So you've got a much richer variety of phenomena, in fact, with lots of different
frequencies interrelating in different ways. Right, exactly.
Is there some possibility that you might see metastability at different spatial
scales in the brain? Oh, absolutely.
In fact, I would be astonished if you didn't.
Right. So here we are. You still have a model that's actually pretty powerful
in capturing data and describing and interpreting some of this data on the brain.
And now you're also applying that to sort of pretty extreme states in which
we can push your brain like under a cello bison.
Celocibin, yeah. Celocibin, sorry. Yeah.
So how has the model helped you to understand the dynamics that you would induce
with magic mushrooms? Yeah.
Can I give a little bit of background there?
One of my colleagues at Imperial College, Robin Carr-Harris,
has done some pioneering work with psilocybin, which is the active ingredient of magic mushrooms,
where subjects are administered with a psilocybin preparation,
and then you do functional MRI on the subjects.
And you can interpret the results as an increase in metastability,
which in turn you can interpret in terms of a larger variety of states being produced,
flipping between a larger variety of states.
So you can kind of explain some of these results, some of the dynamics that
you see under psilocybin as moving to a more extreme version of the metastable
regime that was identified in this model.
And Robin Carhart-Harris is quite amenable to this kind of interpretation,
and we had a joint paper in Frontiers describing that kind of interpretation.
Interpretation and um um so but
i think the uh i think a lot of and so we're trying to
apply the model to that as well but i think that it's it's very much work in
progress and we have to in fact i think the application of this whole methodology
is very much a work in progress i mean we have to uh uh you know um refine it
a good deal before it becomes really accepted okay you said before
you wanted to bring this back to behavior and one of the ways that i expect
you will do that is through the work that you've done on on cognitive architecture
which is thinking much more about function of brain circuits particularly cortical
circuits so could you say a bit more about first of all.
The ideas of cognitive architecture that you're interested in and how that might
mesh with this work on oscillators.
Yeah. Well, one of the really fundamental problems that I've been interested
in for a while is how it is that a novel coalition of processes can form,
can sort of coalesce out of,
nowhere to deal with a situation that's never been encountered before.
And that's clearly, Clearly, I think that's clearly at the root of human level cognition.
Well, actually, maybe even some animals, some non-human animals can do this as well.
And so I see the rich dynamics that I've been talking about here as a way of
maybe addressing that kind of problem.
And it also relates to the kind of cognitive architecture that I've been interested in.
So I've been interested in global workspace architectures,
you can relate global workspace architectures to a certain kind of connectivity
in the brain where you have a connective core of hub regions which perhaps can
facilitate coalition formation,
in particular the formation of novel coalitions to deal with a new kind of situation.
So it's this formation of novel coalitions which I
i think is quite difficult to account for and that's the kind of thing
that i'm that ultimately i'd really like to be able
to model so that the connective core that you're talking about
there is is the bit that's actually active when we're
not doing anything which has you know external where
we seem to be thinking rather than attending yeah and is
that right so what kinds of activities that humans do do you think we're going
to understand better through this well it probably Probably the kinds of activities
where you're confronted with a novel situation and you have to pause for a second
and sit back and think and stare at it and then suddenly, aha,
a solution has come as if from nowhere.
And so I think that probably is engaging a sort of mode, a dynamical mode, that enables.
Processes to talk to each other and form a novel coalition that otherwise they
wouldn't have been able to do.
And I suspect that they can do this via these sort of centralized regions in
the human human brain but then we still don't have behavior so what's it we have a kind of a.
An unobservable behavior so you've got reflection so
that's the point we want to get to what's the overt behavior
we're going to then explain with that right yeah well i mean uh so
so i i say you know i was caricaturing a
little a little bit by saying you take you sit back and you say
aha but i think if i mean you've probably seen
this famous uh video of betty the crow uh bending
the hook for the first time to retrieve the bucket and
i you know of course you can ask questions about
that experiment because it's hard to reproduce that kind of experiment but
if we take that that bit of behavior at face value i think
something interesting is going on there or when
children solve the same problem because you can reproduce that that they'll
they'll play around they'll play around and maybe there's a you know maybe you'll
see this physically but maybe i'm not maybe you won't but there'll be a little
moment when uh when their brain is going to when maybe they actually pause but
their brain is going to go into a um a mode where.
Where a new combination of processes can come together and then it's going to lock onto that.
Somehow the brain, as it were, knows that that's the right solution.
That's the sort of aha moment. It locks onto that possibility and of course
it's going to do it straight away.
And that phenomenon in the brain, I think, is key, absolutely key to understanding
human-level cognition.
I think it's as important as insight. It's as important as language,
I think, and if not more important than language.
And that's what I'd really like to be able to understand and capture in a model. Okay.
So then, Murray, being an engineer and trying to understand the brain using
these kinds of models and actually covering quite a range of phenomena, phenomena,
what would be Murray's law that we have to adhere to to study the brain?
I don't think i'm entitled to coin you are now you're now uh i think i think it would be,
um play one word law
play play with your models and make
your models play okay i think playfulness in
in as as a scientist uh and engineer
um and playfulness in the models they're
in a sense they're the same thing right we're being creative and
create the root of creativity and the creative things
we want to create is playfulness now the
other thing is that tony and i trying to control the
future so um that's why we have tony check the predictions people make and since
he can just take a train from sheffield to london so we can save money that
way the question is four years from now tony's tony's going to come visit your
lab there at imperial and he's going to say look Look, you know,
four years ago on this podcast interview,
you made this prediction to them checking whether it was validated.
What's the one prediction, concrete prediction you could make?
I think it's that I'll have no more funding to address this issue than I do now.
We hope that isn't true. All right, Ray Shannon, thank you very much for this conversation.
I was queued up for that question. The CSN podcast was produced by the Convergent
Science Network of Biometrics and Biohybrid Systems, a project funded by the
European Sevens Research Framework Program.
For more interviews, recorded lectures, or upcoming conferences in the field
of biometrics and biohybrid systems, go to csnnetwork.eu.
Music.