Yeah, right. I guess we need some guidance on how to interpret it. Oh, yeah, we can do that.
That's not a problem. Okay, good.
This is the Convergent Science Network podcast. But you'll be interested in Nunga.
Yeah. Leading researchers in the domain of neuroscience, brain theory,
and technology are interviewed by Paul Vershoor and Tony Prescott.
This is Paul Vershoor with the Convergent Science Network podcast,
And I'm here with my colleague Tony Prescott in the BCBT workshop of 2014.
And we're with our speaker, Henry Kennedy, who has been talking about architecture
of a particular neocortex.
And Henry, you sort of, you came in not sort of opening the door,
you sort of knocked down the door by just saying, look, all the stuff you guys
know and love is wrong. like small world networks you should take with a big grain of salt.
Standard ideas about how we
think about the connectivity of the human neocortex are maybe incorrect.
But what's your position exactly towards these standard views?
Not that the small world network is wrong, It's not.
It's simply a proposition based on inadequate data.
If you look at the complete data of inter-aerial networks, it just so happens
that they're very high density.
At that kind of density, the small world model is not appropriate.
It doesn't tell you anything about it.
Actually, Actually, what we're trying to say is that what does give you a much
more meaningful explanation about what the architecture corresponds to is if
you take into consideration the distance and the weight, the strength of connections.
And this breaks away completely from the small world tradition because you're
no longer dealing with a binary network. You're dealing with a directed weighted network.
That was the point we were trying to make.
So you're saying it's just too crude a view to really help you understand how a cortex is wired up.
It's ignoring too much about what actually specifies the network.
If you have a network which has a density of 70%, it's not what is connected
to what that will tell you very much, it's how strongly which area is connected to which area.
It's the strength of the connections, and it's a wide range of strengths that
makes the cortical network work so very, very surprising in some ways.
There's a big range of strengths of connections.
They cross over five orders of magnitude, and it's taking those strengths into
consideration that you'll have an insight into what is actually the fingerprint,
the connectivity profile of a particular area. Right.
So now you've been spending a lot of time doing very detailed studies using
tracer injections in understanding the connectivity in particular of the cat neocortex.
And I guess with emphasis on the visual areas, right? I started off in cat visual
cortex many, many years ago, yes.
More years than I care to remember, 20 years maybe since I've touched a cat, yeah. Right.
But just to make the point that you're not basing your conclusions on let's
say a functional description of the system or in terms of what you might want
to call a functional connectivity,
you base it really on a detailed study of, let's say, injected cells, right?
So it's a very direct measure of connectivity. Okay.
Are these... So then given that data set...
How do you think of, what is the template you have in mind of cortical connectivity?
How is it different from what a small world would tell you? How is it really
wired up? What are the wiring rules?
Well, there is a wiring rule, and that we felt was the backbone of the publications
we've been making recently.
There is a very simple, very straightforward
wiring rule, and that is that there's a minimization of wire.
And we're certainly not the first people to find it but what I think we have
been able to put our finger on is a principle which explains that wire minimization
which is such a strong constraining feature of the cortex.
So the constraint basically that
we've been able to demonstrate is that
there's a weight-distance relationship so there's an exponential fall of strength
of connection with distance And if you take that on board and you generate random
networks based on that particular feature,
using the space constants, the lambda value that we've observed,
you generate networks which in many ways reflect the properties of the cortical
network that you can look at down the microscope that you can actually measure.
Just to clarify, we're talking about a primate, not a cat, aren't we?
It's a non-human primate. But then, isn't this contradictory what you said earlier about weights?
Because the data is roughly telling you that over a distance of,
let's say, 60, 70 millimeters, you would have a roughly exponential kind of
decay of the probability to connect and of the connection strength.
Yes. But earlier you said the….
The small world view doesn't help you because you have maybe low probability
connections, but their strength matters.
Their strength actually can tip the balance, if you want, into a significant
function relation or not.
But now your data shows that the weight also drops off with distance.
So would that then not undercut your earlier argument against the small world network? work?
Well, the point about the criticism about the small world network is simply
if you take the density, that is to say the number of connections that you have
between different cortical areas,
it turns out to be about 70%.
So 70% of the connections that can exist actually do exist. That's a very, very high density.
And at that density, you have a small world. So you don't have to measure the
clustering index, path length, or what have you.
Everything is virtually connected to everything else. Every area is one and a half hops away.
Now, what you're referring to now, the fact is that when you look over very
long distance, yes, very few areas are connected, and that's interesting because
it means that you have a sort of binary specificity over those distances.
Presence of a connection in itself is going to be significant in terms of trying
to understand the biology. Our point is that globally, overall,
it's going to be the strength of connections which matter.
But there are exceptions over these long distances.
So for example, if you take...
The frontal eye field projection to area v4 that connection is actually rather
strong it's stronger than what you would predict so it's an it's an outlier
and i think that that's uh something which comes out of uh our investigations
which i think need to be considered in more detail,
you're really doing the analysis two spatial scales here
so you're talking about connectivity between cortical
areas yes of what which how many are
we talking there sort of well we're working with an atlas
of 91 areas in the in the macaque monkey right and
so and then at another spatial scale you're talking about
connectivity within an area which you're saying is 80 to 90 percent of the connections
are within cortical areas yes yeah but and then spilling out into surrounding
areas with the remaining 10 to 20 percent of connections yeah yeah so um but
i think when you're analyzing the data,
are you analyzing it in different ways when you're doing these two analyses or is?
We haven't, the connectivity within the cortical area, this is a local connectivity, if you will.
So if you take a point in a cortical area, 80% of the projections to that point
come from within two millimeters actually.
That's the local connectivity and we're not doing, we're not looking at that in any great detail.
Actually, it should be looked at because there's been very little work on that.
So you have the canonical model, which is developed by Kevin Martin and Rodney
Douglas, that was for area B1 of the cat.
It's never been done for other cortical areas. We don't know what that looks
like. We're not doing that.
So when you say most of the connections are within an area, they're actually
within a very small part of that area. Absolutely, yeah.
Well, I think that the range you were talking about in the data you presented
another day was, like I said earlier, up to 80 millimeters or something, right? Not beyond that.
For the scaling loss that you showed. Yeah, for the macaque monkey,
it's about 7 centimeters is about the size of the brain, yeah.
Okay. Of course.
So, what is then the,
So if we now look at these rules of connectivity that you presented,
that seems to suggest that indeed, like you said earlier, everything is connected
to everything, directly or indirectly.
So that would seem a little bit unspecific to talk about, let's say,
an architecture that actually has a certain function.
And also when you look at the physiological properties of it,
it doesn't necessarily look as some sort of uniform structure.
It just seems much more fractionated in its dynamics. So, how do you match these two?
So, is there an element of fractionation and specialization that we just have not seen in this data?
Well, I think you were describing it, though, weren't you, with the Dossler and Banchel stream?
There was some fractionation. Well, no, this is what I want to get to, right?
So, how do we get, just at face value of this Markov kind of data,
the scaling laws, you could say, okay, well, this looks pretty uniform wherever I go.
It's sort of connected in a similar way. way, similar kinds of weight distributions,
similar kind of length distributions.
But how does it give you functional specialization of areas and functionally
organized forms of dynamics?
That's the question, though. Okay. So the short answer to that is the binary
specificity is low, as you pointed out.
Everything is not connected to everything, but there is, at least within.
Within, say, 15 millimeters of an area, there is a very, very high connectivity,
so you are virtually approaching 80%, maybe 90%.
So it's going to be the strength of connections which are important.
So we've looked at the global properties of these networks.
So you can make an efficiency measure, which is a sort of conductance where
you're treating the strength of connection as an inverse resistance.
When you do that, you look at local efficiency and global efficiency, and then you can look,
how the global efficiency, the local efficiency, is affected by fresh-holding,
removing the weakest connections until you have just the backbone of very strong connections,
and then look how your efficiency changes with removal of connections,
and show that the,
network which you've produced using the exponential distance rule actually very,
very faithfully mimics the efficiency changes you can observe as you remove connections.
The point I want to make is that this distance rule, the exponential distance
rule, actually gives you a handle on looking at global properties,
not just the motives and the click, which I was talking about earlier on.
The click actually is coming back to the core of the previous speaker,
so it's not without its own significance.
The strength of connection does give you a very strong degree of specificity,
but again, it requires looking at the strength of connections.
I think one of the reasons why this has been perhaps ignored in the literature is, first of all.
Um you can't see this kind of range of strength of
connections with diffusion mri you can see
it with track tracing if you're going to do this with track tracing then
you've got to count neurons or synapses and that's a lot of work so if you want
to have that kind of data then you you can't just do what a lot of us have been
doing for many many years and myself included which is saying strong weak and
medium strong weak and medium isn't going to tell you about
the range of strengths of connections where the specificity is coming in.
It requires a much more thorough approach than that.
And what that shows you is that you have these five orders of magnitude.
That means that the counts that you're making run into very, very high numbers.
And that's an important point.
But the other thing that I would like to understand better, So if I take now
these scaling laws for weights and for the probability to connect,
these are probabilistic. They're probability distributions.
So I just take a lattice of units and I'm going to wire them up following these
two probability distributions of lateral conductivity and the strength of these
connections. directions, right?
Then you would expect if I start to sort of chip away those guys and I removed
the weights with the smallest, the lowest value, the lowest strength,
that I would get also the random patterns.
Because these two scaling laws or wiring laws themselves don't give me any kind
of symmetry breaking, right?
So if I do this a million times, I would have a million different kinds of topologies or not.
No, you don't. What you find when you take the data, the interaerial connectivity
database that we have, which is 29 areas and some hundreds of connections,
and you remove the weakest connections, you get a backbone.
You find the backbone of the cortex,
and that gives you those features and characteristics of that backbone.
When you do the same thing to your random networks that you generated using
the exponential distance rule that we have, what we're saying is that is a probability,
so you pick more frequently the very strong short-distance connections, what have you.
When you remove the weak connections and you end up eventually with your backbone,
you find that it has many of the features of the backbone in the data that you've
observed. Which features do you recover best?
Well, I mentioned the motives. That's pretty good. The correspondence between
the motives is actually excellent.
So you have 16 different motives of, say, three nodes.
Of those 16 different motives, your distribution, which is captured by the exponential
distance rule, is actually excellent. Then I referred to this question of hubs.
Now, people have been finding hubs, let's just say, areas with high degree distribution
to them, a large number of connections with other cortical areas.
And the idea of the cortical core is that the hubs form more connections between
themselves than you would predict statistically.
Statistically so that's actually uh what is
referred to as a rich club the rich club analysis was introduced
in in network science uh by coriza
and other people about seven or eight
years ago now you can't do that
kind of analysis on a high density network it
the normalization doesn't work so what
we've done is look at the clicks so the clicks are
sets of areas which are 100 connected
connected amongst themselves and we find a very very
large number of clicks and when you look at
the probability of finding that by chance it's extremely low when we do this
same analysis of networks which we've constructed using this EDR we find the
same number of clicks with a very very similar frequency and the correspondence
is really very remarkable,
you're not imposing any other constraints you just follow this exponential distance
rule and that's it Absolutely. Okay.
So some fairly simple geometric rules are giving us a lot of information about
connectivity within the brain.
Do you then draw inferences about the developmental processes that are building brains?
And because this has been a week where we've looked at evolution and development
and their impact on the way the adult brain is.
And your data, I guess, gives hope to people that want there to be some useful
developmental rules that we can
apply, perhaps, to build a brain on which experience then has some impact.
Right. I think that's a very important issue, which we haven't looked at,
despite the fact that I also have another side to myself, which is to do with cortical development.
I mentioned in my presentation that there's this log-normal distribution of weights.
A number of studies have looked at distribution of synaptic weights at the single-cell
level and also find a log-normal distribution.
There's a recent paper review in Nature Neuroscience looking at log-normal distributions
in frequencies of firing and another sort of phenomena.
It seems to be a sort of signature you're finding a lot.
The log-normal distribution in itself suggests that there could be a very simple
algorithm which would be controlling outgrowth of axons.
For many years, I was interested in the formation of connections,
and that was in cat and monkey cortex.
In those days, the predominant theme in cortical development was this notion of exuberance.
You had an overproduction of connections and the selective pattern,
the precise pattern which is characteristic of the adult, emerges through a pruning process.
This pruning process is actually necessary because there's not enough information
in the genome to set up the correct connectivity.
We challenged that, And every time we challenged it, it turned out not to be
the case. So what did you find?
Well, in the case of inter-hemispheric connections, that was the model which
was hugely used between 1970 and 1985 by Giorgio Innocenti,
Doug Frost, and many, many people were looking at Herb Kalacki.
Monkey, there you could observe a widespread connectivity in the very young
animal which would decrease as the animal would mature.
We challenged that by looking at the collosal connectivity of the prenatal monkey in the visual system.
The characteristic of the monkey is that the area V1 is a collosal in the adult.
Then you can really ask the question, is there.
Is it a collosal during development? Because it shouldn't be if this exuberancy
rule is going to be general. And it's not.
We were able to show that there's never a collosal connection going into area V1 of the monkey.
So I think it's very much a problem. You've got to be able to distinguish with
the growth of the brain and the expansion and the increasing and therefore the decreasing density.
Density simply because of the expansion you're going
to be able to sort that out from the actual
creation of the specificity so that was
in inter inter hemispheric connections but we then did the same thing looking
at the connections between v2 and v4 which are in uh you have these bands where
you have projections to v4 and projections to mt and we were able to show that
they're correctly aligned from the word go.
There's an overall decrease in density, but it's not the decrease in density
that creates the pattern.
But then this old-fashioned idea, we can call it now, of exuberance and pruning
would still hold, but for a smaller set of, let's say, neurons and connections?
Or you think it's really the wrong way to think about how the system gets wired
up? I think it might be an oversimplification.
I think the idea that you You don't have enough genes to specify connections
is not really very interesting.
I think the question, the confusion which was made was not, is there a decrease
in density of connections with growth? There is.
The question which was posed was,
is it that decrease in connections which creates the specific pattern?
And that, I think, has never really been satisfactorily shown to be the case.
I think it's not, I'm not saying it plays absolutely no role at all,
but I think there's much more, there's much more guided growth and target specification.
But couldn't you argue that if you apply the exponential distance rule to a developing brain,
you should see something that might look like pruning by definition,
because you're imposing a distance relationship of your connectivity in an expanding volume?
Well, I think that would be an interesting thing to look at.
Simply look at the brain and see how does this exponential distance rule look
in a very immature animal.
The problem is that numbers get very big, and you really need much more automated
techniques in counting, which are coming through actually.
So I think that sort of thing is something that will be addressable.
So one of the other things that you discussed today was how the primate brain
seems to minimize the wiring length as a consequence of applying these rules but and.
Bearing in mind this point about changing brain size, we also discussed some
data about animals with smaller brains like mice, and that the same rules don't necessarily apply.
So are there some scaling issues here for brains that perhaps would help us
unravel some of these issues?
Absolutely, yes. This is something we're very interested in looking at.
With Zoltan Tarakai and his postdoc who's now back in Romania.
We have funding with John Cass to look exactly at that.
We're looking at the mouse and the microcebus, which is a small primate,
and we want to look at the baboon, which is about as big as we can go in primates. That's not a huge deal.
The idea is to see how this exponential distance rule plays out in changes in brain size.
We're still working with our own database, which we're putting together with Andreas Burkhalter.
To keep us going on this, we've been looking at the Allen Brain Institute database,
and it's actually really rather interesting.
So this is very, very preliminary, and with Zoltan Torukai and Ken Koblok and
our colleagues, we're still analyzing this data.
But the glaring thing is in the mouse, there's a huge amount of wire economy
that you can impose on it.
So the areas in the brain are not localized optimally as they are in the primate.
And and that makes it look straight
away very strange because you have areas which are
not connected which are actually quite near to another area and they're not
connected to it and that's what you you absolutely don't see in in in the monkey
and um the the thing that uh we're very interested with tori kai and and uh
the the the group in general is to see how folding comes into to this.
So the comparison between mouse and monkey is a bit complicated because we have
a change in brain size, but we also have a change in folding.
So what we've been doing with David Van Essen is we've been,
instead of taking the distances which we've been using up until now,
which have been white matter distances between areas,
approximating the trajectory of the axon, we've been taking surface distances.
And when you do that, you're sort of unfolding the brain as it were right and
and when you do that you get a very very different uh distribution of distances.
In the monkey so this is unfolding the monkey brain this
is flattening the cortical sheet virtually yeah and
then when you look at your distance distribution you get something
much flatter it's it's not at all like a gaussian pointed a very pointed gaussian
and it looks just like the mouse it looks just like the mouse and so we're now
playing with that and seeing how this distance distribution interacts with this
exponential distance rule to set up the specificity that we're seeing.
So what kind of rule would hold in the mouse brain?
That's a key question. Certainly, the exponential distance rule is part of it.
But the jury is out completely.
I mean, Zoltan Tarakay and Maria Havaz is doing simulations on that, and we don't quite know.
But you seem to suggest this morning that the probability to find long-range
connections is higher in the mouse brain than in the macaque brain,
or did I misunderstand that?
No, that's correct. So when you look in the macaque, you have an exponential
lambda value with a very sharp drop.
When you look in the mouse, the decline in strength with distance is much more shallower.
And that's certainly part of the exponential distance rule being altogether
specifying much less specificity in your mouse.
So you we talked about the cortical corn in the macaque you have this very large
number of clicks Which is extremely improbable in the mouse.
You have a much smaller number and don't forget what?
Although the overall number of areas are the same in two species.
We're actually making these networks on same number of areas using the same
column Comparable number of areas so The cortical core is much less defined.
So what I think this is pointing to is Whereas in a world where one might be
tempted to say, well, the mouse can be a very interesting model for the brain,
it might be because you can have knockouts, you can have knock-in,
you can use optogenetics very easily and what have you.
But simply from these kind of large-scale properties, it appears to be very, very, very different.
But in the areas you're looking at, the cell densities are comparable to primate? No, they're not.
So the scaling rules, and John Kaus could talk about this, he's with his colleague
Herculeano Huzel, they've done a lot of work on that.
And the scaling rules in primates and rodents are quite different.
So basically, I mean, the way I've understood it, in fact,
I want to talk to this about John at this meeting, is that the um your um as
the brain changes in size you basically you can change the density of cells
in in rodents and my feeling is that this might lead to a capacity for miniaturization.
The microcebus is about a centimeter and something, and it's the smallest primate brain that exists.
And as the lady this morning was pointing out, there's a huge radiation in primates,
and there's a huge adaptation.
The mouse, the rodent brain, can go down to tiny little things,
I mean, they have moles and voles and what have you that have… This means,
Henry, that possibly the scaling law still holds, but it is modulated by cell density.
It scales in turn with cell density.
So the cells are packed more tightly, and that gives you an exponential decay.
But if the cells are packed more loosely, it gets stretched out.
Would that be reasonable?
I think that's what Herculeano Huzel's results are saying.
I think she's saying that the density has a much bigger variation in rodents
so that you can make smaller and smaller brains by making smaller and smaller cells.
But the other thing about the mouse example that might be problematic is that
for the primate brain, you were saying, look, these laws we have on connectivity,
tells you something about the optimal wiring of a brain because you see that
regions that are connected are placed together and so on, right?
And you make this distinction, or the example of the ventral dorsal visual stream.
However, now for the mouse case, you say that you find
regions there that are adjacent but not connected not adjacent
but nearby yes no but okay still right so
they're nearby but not connected so that seems to be a violation of the principle
that you identify so that means a mouse brain in its development is really setting
up fixed borders which i know we're not going to wire these guys up yes while
you apparently are not doing that in the primate brain is that correct exactly yes that that's what,
I found so very, very surprising. I didn't expect that at all. Yeah.
And if you, if you optimize your, your mouse brain.
So you place the areas optimally so you don't have these absent connections, as it were.
And then you look at your lambda value. It looks much more like a monkey.
So you can convert it into a monkey. Now, how we got to that state, I don't know.
But I'm wondering if the ancestral primate was actually, I think, probably quite large.
And so today's rodents might have undergone a miniaturization.
And is that the cost that you're paying for miniaturization,
or is it another kind of adaptation that we're not putting our finger on?
So about optimality, what I would like to understand is how you define that.
Because if you talk about optimally wiring a brain, what does that really mean?
Does it mean that you have a constant number of wires crossing certain distances
independent of where you are on this cortical sheet?
Does it mean that you want to optimally transfer signals between areas that
you want to use as a minimum number of intermediate steps to get from A to B?
What's optimality here?
Well, I've been using the term maybe rather loosely. What we're talking about is optimal placement.
Can you replace the areas in a monkey brain in such a way that you would be
using less wire given the strength values that we observe?
And the answer to that is no, you can't.
There's no way you can do that these
are simulations that take days and days and days
to do but there's there's no way of doing that either to the binary or
that maybe there's one or two areas you can flip positions but the weight is
absolutely not in the mouse you can get 15 percent reduction in wire both for
the binary and for the and for the weighted network so we're talking about optimal
placements the the second part of the your question touched on this thing that I was talking about,
that if you take this exponential distance rule to heart.
And you look at the visual cortex, for example, and you look at the central
visual one and peripheral area visual one, you'll notice that they're in very
different neighborhoods in terms of the areas which are surrounding them.
That would suggest that the central area V1 should be more strongly connected
with a very different population of areas than the peripheral V1 and the same for V2.
And we've looked at that. And the answer is, yes, the connectivity is very different.
So central V1 is in front of the temporal areas, so you find that central V1
and central V2 look like ventral stream areas.
And if you look at the peripheral representation, they're in front of the parietal
cortex, and you look at the strength of the connection and you do a summation
of strength, then these areas, or these parts of these areas,
appear to be dorsal stream areas and quite different.
So from that, I'm wondering if we have an optimization of the position of areas,
but we also have an optimization of the shape of areas.
And if you look at the flat maps of David Van Essen and others,
they have very, very distinctive shapes.
I mean, you remember the motor cortex and the somatosensory cortex,
these long, thin strips of cortex where you put the whole homologous in this peculiar sort of strip.
You could imagine something very different. Would you accept the hypothesis
that what you're optimizing is a transduction delay between these areas?
Well, yeah. I think that was part of the motivation that we had to look at the
distance through the white matter.
So we felt that we were looking at the… And I think in the first instance,
we measured the white matter distances and the surface distance.
And you could imagine that these distance relationships we've been reporting,
could simply be reflecting change of the cortical property, in which case the
surface distance would be very good.
Or it's something to do with transduction signals, in which case your trajectory
through the white matter should be very good.
And so I was expecting to have a sort of black and white answer comparing those
two. And that didn't happen. What did happen?
Well, not much actually. We stuck to the white matter distances because that's
what we'd started off with.
But then when we were looking, when we were making this monkey-mouse comparison,
that's where we asked ourselves the question, what about if we unfold the monkey
cortex and how will that behave?
And we're still looking at that. But it certainly changes the distribution of distances.
When you say that you want to do this minimal wiring test and see if you can
rearrange cortical areas,
to reduce the wiring, how do you deal with the fact that the pieces of the jigsaw
are all very different shapes and there's really only one way it fits together?
So if you rearrange it, how do you compensate for,
Oh, well, so what we're rearranging, we're not tackling shape here, Tony.
Yeah, and I've perceived you're not. Yeah, so we're measuring distances between
the barycenters of the areas.
And so we're switching barycenters around in space.
We're not trying to shift these peculiar shapes and make them all fit in.
It's not a jigsaw puzzle thing.
So we're moving the barycenters. and we're considering the distance from one
barycenter to another is the distance between one cortical area and another.
But as you say, the shape of these areas can be quite extreme to long thin strips.
Yeah. Which then does have an impact.
Yeah, that introduces its own problem about what is a barycenter of a long oblong shape.
Yeah, but that's where we are. So then if you had to choose one objective that
these wires are optimizing, it would be just to minimize the physical wires between areas.
That would be your bet today. day um i
as i say i'm i'm not sure if you look at if
you look at area v1 and v2 in the monkey in the macaque it's the the two biggest
areas uh in in the macaque brain in fact they're very very large if you relatively
speaking compared to any brain they're huge and in the central representation representation,
they're folded in such a way that V1 and V2 lie opposite each other,
separated by one millimeter of white matter.
So it's the most extraordinary engineering to make sure that you've really minimized
the distance between the two biggest areas of the macaque brain.
That tends to make one feel, either it's because, well, you know,
if you had as much white matter in your brain as a mouse, it would be the size of a bathtub.
Which would make getting out of the door rather awkward.
We know that there's a reduction in white matter, there's a reduction in total
connectivity as brains get bigger.
It could be that that piece of engineering is dealing with that problem.
Alternatively, it's dealing with the transduction problem. You really need that
kind of very, very short distance for V1 and V2 to do its job.
And so, in fact, that could maybe explain some of these scaling distances is
that the amount of wire you have in the brain really becomes.
Under pressure as you get bigger brains so you really
have to optimize for wire length in a way that in a small brain
it's not so important absolutely yes but now another element of this is that
we could argue well these you measure this in adult in adult sub monkeys right
so this is a cortex that has been learning and changing its properties due to plasticity rules,
plasticity rules are activity dependent so what you're looking at is really
the history of activity in this system.
Now, the history of its activity is strongly constrained by subcortical systems.
Like in the case of cortex, you'll depend on your thalamus. And now,
thalamic projections are not random.
Thalamic projections actually also define very specific envelopes of interaction
with cortex. Absolutely.
So I could argue what you're actually measuring is an echo of the combination
of the envelope of thalamic projections into this system modulated by the local
plasticity rules that make these guys wire up together. Would you accept that interpretation?
I'd go a long further, much further than that. The work we've been doing with
Colette de Hay over the last 10 years shows that the thalamic fibers release
a mitogenic factor which governs the proliferation in the germinal zones.
So the size of your areas, the size of the cortex is largely determined by the
interaction between the thalamic fibers and these germinal zones.
That whole relationship between the thalamus and the cortex setting up,
for many years people thought that the thalamus was playing an important role
in the specification post-mitotic.
You had the barrel cortex and the effect of
plucking whiskers and seeing the representation the
barrels changing in some fashion on
in on the cortical surface so what we've been arguing
for for a long time now is that the the thalamic
fibers are actually their primary target and particularly in primates is it's
much more accentuated is not the cortical plate it's the germinal zone and and
um so that the thalamic fibers get into the cortex in the primate very very
early, before there is any cortical plate.
In fact, before there is any release of mitototic neurons into the cortex.
And what they're doing is actually, I think, hugely to do with shaping the proliferation
and possibly the specification.
But would it also mean that if we look at these two types of thalamic projections,
like powerful magnocellular, where the powerful cellular seems to be not very
plastic and the magnocellular is, shouldn't there then be a correlation between
parvocellular projections and your scaling law?
Um well for me the scaling law
is is uh a little different
from that it's it's to do with how you how
white matter gray matter changes over a
range of brain sizes and so what people have been able to show is as brains
get bigger the volume of white matter doesn't increase at the same rate as the
volume of gray matter and so this is where this this this notion that was It
was introduced by Ringo some 20 years ago that as brains get bigger,
there's a huge pressure to economize numbers of connections,
which if you think about it, is really rather extraordinary because brains are
all to do with connections.
And you've got to actually reduce the number of connections and reduce the number
of long-distance connections.
But the question is, how do you do that? How do you control the developmental
program to achieve this, to economize on these connections? So then the question
is, is the thalamus then the key to understand the genesis of your scaling law?
Well, I don't know. I think if I understand Herculeanus' work,
Herculeanus' work correctly,
as your rodent brains get bigger, the size of the neurons get larger,
so you have this change in density which you don't have in primates.
So that seems to me to be a very different sort of algorithm for building the
brain. If you say, well, okay, we're going to have primates,
and primates are going to have basically a small variation of cell size.
We want to go from a small brain to a big brain, so we're going to economize on connections.
And then you do the same thing for rodents. You say, well, we're going to have
to do the same economy on connections.
That's a kind of given. But here we can change the neuron size a bit more.
So you seem to run into a bit of a problem with creating very big brains.
You have these South American capybaras. You have these big South American rodents.
But then I'm wondering if the adaptation is much more adapted to making small brains.
So that is the sort of rules I see for the scaling.
I haven't been thinking about how the… Because what I'm after,
what I try to figure out is… Now what we're trying to do this week in the school
is this relationship between genetics and development.
And as we discussed earlier what you measure with the scaling law is like an
echo of these two processes working together. Right.
So then I was trying to push you a bit and try to come to some understanding,
okay, what are the causal factors that give rise to these scaling laws that you measured?
Right. Okay. Well, we're going to look at a particular case which would probably come back to that.
You have this situation of microcephaly where you have very, very small brains.
And there's a lot of interest in understanding that because expansion of the
brain is very much a hallmark of human evolution. And so microcephaly raises
something of a question about our evolutionary origins.
People with that condition actually show remarkable levels of cognitive capacity.
And so we're now going to start working with a mouse which is a model for this,
and so it will explore what is the genetics regulating brain size and how that
will touch on this exponential distance rule,
and also on the the whole relationship between brain size and the explanation of distance rules.
So these are questions which we will address in mouse. Okay.
Tony, you have any more? Okay, so Henry, now looking at this,
your tour through, let's say, the anatomy of the brain that's going on for quite
a while, and which also you have made amazing progress,
if we want to follow in that trajectory, what's Henry's law that that we should
adhere to um i think there's a number of cases without saying any names,
where people you do you remember the decade of
the brain very well so the decade
of the brain was spurred by a book um i won't say the name of the book which
said well we've got people endlessly producing data and you go to the sfn meeting
and it's so depressing because you've got We've got miles and miles of posters
of people showing endless data.
What we now need is somebody to come along and give us a model of this,
much in the way that Watson and Crick were able to do with the double helix.
And we'll have a kind of Eureka moment, and we'll suddenly understand everything,
and we will have understood the brain. Basically, we would have done it, gone there.
It would be a finished story, and we can pass on to something else.
I find this absolutely ludicrous because um.
Based on an idea and there's a there's a present a european project to understand
the brain and it has this idea built into it that we've got a lot of data and
we can go back and accumulate,
over 150 years of journal comparative neurology and and skim through and and
and take out all this data and pile it up together and it will add up to some
total explanation and i think this is dangerous.
I think it underestimates the challenge to understand intelligence,
biological intelligence.
I think it underestimates the challenge of relating that to neurological principles.
It forgets the fact that any experimental result is done in a certain intellectual
framework, and the interpretation of those results is not extendable.
You can't extract these this information willy-nilly and
apply it across the board so i think
that either governments are going to decide that understanding how brains work
is worthwhile and they will provide money to do basic research which means not
endless science papers and nature papers and what have you but actually pay
people to how many synapses do you have on your pyramidal cell what What is the,
you know, the connectivity of your average whatever and pay people to do that kind of work.
And so I think that there's a need for empirical data.
I think that the power of simulation is fantastic and has to go along hand in hand.
But if you don't actually have the investigations, if everything's got to be
a breakthrough now and then, you know, immediately,
if you're only going to have high profile kind of projects, then you're going
to find that you're actually playing around with inadequate data.
And I think that this small world thing, it's not that there isn't a small world complex network.
Work there is what i want the point i want to
make is it doesn't exist at the aerial level and there's
been over i don't know how many dozens of publications claiming
that that is the case and they've been using inappropriate data somebody's got
custard on their face and i think that the um the willingness to challenge big
questions i think there needs to be much more interaction between people doing
um the experimental work and the people doing the simulation and i I think these
have got to be hand in hand.
And this is exactly what we've been trying to do with Zoltan Torekai and now
with Chao Jingwang and others.
And I think it's tremendously exciting because you can see your anatomy in a
much larger framework, in a much bigger context.
And I think this will ultimately lead to real breakthroughs.
I think sort of collapsing things and overselling and saying,
well, we're going to have a decayed with the brain and okay, we need big science.
That's for certain. But we also need reason science and we need empirical data.
And now, four years from now, Tony's going to visit you in Lyon.
Yes. Have some pâté with you. No, no, andouille. Okay, even better.
But he's also going to confront you with a prediction you're going to make today.
So what's the one specific prediction you would share with us today that Tony's
going to check out four years from now.
Well, I think that we're going to find...
That there's a very large range of solutions that biology has brought to the brain.
The dream that you can have one brain and extrapolate from that and understand
the brain principle goes right against all our understanding of zoology and how it works.
So I think that when Tony comes to Lyon, we have a pot of white wine and an andouillette.
– andriets, you have to have very, very white wine, very dry white wine,
and in very large quantities – is that we'll know something about different
sizes of brains and how folding,
interacts with these things.
And I think that the idea that you can use the mouse brain as a model brain
for all brains will be seen to be completely fallacious.
I hope between now and then somebody else will have done the local circuitry,
because because this is where the machine really is.
This is where the machine lies across different brain regions.
At the moment, we have no idea about the local circuitry of area 46.
We know the local circuitry of V1 in the cat, and we're extrapolating that across
all species, all brains, and what have you.
So I think that we'll have a much more deeper understanding of the variability
of brains and the solutions they bring.
Great. Henry Kennedy, thank you very much for this conversation.
Thank you very much. I hope it was not too boring for you. The CSN podcast was
produced by the Convergent Science Network of Biometrics and Biohybrid Systems,
a project funded by the European 7th Research Framework Program.
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Music.
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