This is the Convergent Science Network podcast. Leading researchers in the domain
of neuroscience, brain theory and technology are interviewed by Paul Verschure and Tony Prescott.
This is Paul Verschure with the Convergent Science Network podcast.
And I'm here with Tim Pearce, one of the speakers in our summer school, the 2013 version of it.
And Tim is a great specialist in olfactory systems, both their biological versions
and artificial versions.
So, Tim, what's so special about natural olfaction?
Great. Well, I was very pleased to be here. It's very exciting talks.
So, natural olfaction. Yeah, well, one of the most amazing features, I guess.
Well, there are many, but you have to consider pretty amazing the ability to
detect literally thousands of molecules.
Molecules and beyond that even to the
point where if you made a new molecule this
week you'd be able to if it
was within certain mass criteria which is very
wide anyway between 30 daltons and
300 daltons in molecular mass then
you would almost certainly have some kind
of response to this so it has
this well i think one of the most amazing properties
of the olfactory system is this semi-biotic or foreignness
property that it's designed from the
ground up to detect uh all
sorts of uh foreign signals that uh
may not be uh have some priors right
so your prior in the system is very flat you don't know necessarily in advance
which uh chemical signals out there in the world are going to be uh extremely
relevant for you behaviorally and
you need to have as broad a sampling as possible on this without making.
Necessarily prior assumptions.
Right, but now as a functional feature, that's not specific to olfaction, right?
I mean, also my visual system can detect novel stimuli or my auditory system and.
So, that's not necessarily outstanding, is it?
No, but maybe in the sense of just the diversity of the basic stimuli, right?
I mean, and also, of course, I mean, this is also inherent in the architecture of the system, because,
I mean, basically you've got three receptors or four receptors for vision in
certain animals and humans,
humans um and you know which has
a certain cost uh in terms of the genetic real
estate but when you look at olfaction it's more
than one percent of the genetic real estate
is dedicated just for this reason so there has
to be a good reason to deploy all of that uh diverse coding uh so that you can
have a very broad sampling if there was a simple answer to it i'm sure it would
have been being found in the sort of genetic architecture, yeah.
So now in your talk, you mentioned five outstanding features like concentration
dynamics, temporal dynamics, the specificity and sensitivity.
Also the ability to exploit olfaction in active senses, an active perceptual
system, and to deal with object recognition in a high-dimensional space.
Right so these were the five um outstanding issues
and we will discuss those in a little bit more more detail
but the other thing that was sort of surprising is that you also emphasize this
so-called retronasal olfaction so what what what's the difference sure yeah
so i mean most of us are are used to sniffing things in the world through so-called
orthonormal uh olfaction which which is through the normal route through your external naras,
but maybe less people are aware that a lot of your olfactory signals is coming
when you're eating through volatile compounds going up through the back of the nose,
interacting with the nasal mucosa that way.
And, well, the interesting point is that depending on the flow,
it seems that you get a different experience, whether the compounds are coming
from internally during ingestion or whether they're coming externally.
Even if you control for the fact that obviously eating things has a gustatory
input as well, which is combined.
But even if you control for that, then you still have a very different olfactory
percept depending upon the direction of the odor.
So does this retronasal olfaction,
does it give it another quality of other sensation or other processing or does
it fall the same back of molecular recognition and processing as the orthonormal version.
Yeah, it's a good question. There have been many studies to show that the percept is very different.
So during retronasal olfaction, you will perceive different qualities.
It hasn't really been specified precisely how that changes.
But we also know that the neural dynamics, even at the receptor sheet,
is very different patterns that are elicited by exactly the same chemicals.
Just from the fact whether they're occurring retronasally or orthonasally.
So the obvious explanation for this must be to do with detecting whether things
you're eating are good things to eat or externally whether they're things that
you need to find to eat or avoid, I guess, right?
So there does seem to be good reasons why this might be the case. Right.
So now, one interesting observation that you put forward is also that in some
sense human olfaction might not be of equal sensitivity as many other animals,
but you do believe that the basic forms of processing and strategies in which
we use olfaction might be rather similar.
Is that fair to say? sure yeah i mean i it's pretty clear people like nick strausfeld
and so on they've gotten into incredible
detail in the fossil records for different olfactory systems from.
Mammals all the way down to uh very extremely simple uh forms of forms of life
have shown that there are a number of key uh generic architectural features
which are crucial to to building olfactory systems you know one of these being
uh glomeruli where there's these
convergence of sensory signals at the first stage of processing,
which is completely common to all animals achieving this.
There also seems to be a genuine necessity for a first stage of processing that
has lateral inhibition at an early stage,
which somehow is thought to
sharpen tuning and also impose all sorts of dynamical properties on top.
And um there's also this feature in most animals of a very uh a mixed specificity of certain,
receptors being highly tuned to certain compounds and other receptors being
very widely tuned to large groups of compounds it seems to be this also seems
to be common so yeah there are a number of properties that it seems if you're
going to build um it's a nice thought experiment if you say take a blank piece
of paper and think if we're going to build um.
An olfactory system without any prior knowledge of what animals do,
given that you want to detect huge diversity of compounds with certain high
specificity and high sensitivity to certain groups of compounds that are important to you behaviorally,
you know, what sort of strategies would you have to do to achieve that?
And I think these common architectural sort of motifs that you you see uh from
the fossil record sort of underpin how you might approach that also from an
engineering sense so but then and so that basically means whether we whether
we go from drosophila to humans,
in your mind we will find common design principles behind this olfactory system
right so that that's also interesting so it's how it would suggest it's rather strongly conserved.
So now in the human case so here we go, we have
our nasal cavity I have my epithelium in
there through which I have the sensilla sticking from
the olfactory receptor neurons these olfactory receptor neurons are then projecting
their axons through the skull into the olfactory bulb where they form these
glomeruli that you described and then these glomeruli in turn get read out by
these mitral cells and then and sends this information to other parts of the brain.
Now, in the mammalian case,
What do we know about the levels of processing along this hierarchy?
So, for instance, would you say the olfactory percept in the human case is already
defined in the olfactory bulb?
Or does it require higher levels of processing? Does it require interaction
between different levels?
Yeah, this is a really good question. I mean, in humans, it's pretty clear that
the first stage of processing the olfactory bulb is thought to do a number of things.
One of the most key features seems to be sharpening the representation,
so decorrelating the signals at an early stage is obviously very important for
discrimination of different odor compounds.
But I'm not sure I would say this is the place where the percept comes from,
because it clearly has to be an active process.
And it's also demonstrated that in
higher processing centers like the piriform cortex
they have much more sparse representations of
these odors which again is sharpening the
tunings even further that has
been already demonstrated quite clearly to be
related to odor memories and also learning and
the other important aspect is that these higher centers as we see in many other
sensory systems in the brain are enforcing a top-down processing on the olfactory
bulb itself on the earliest stage to change the gain control and do various things.
So it's also been clear that, for instance, in orbitofrontal cortex,
that this is also an important aspect of the perceptual aspect.
So I would say it's really at these more cortical levels that if you were to
think about percepts, this is where you'd probably look.
So in the human case, how many of these receptor neurons do we have sticking their sensilla in my...
Yeah, so the total real estate at its peak in rats was about a thousand different
diversity of receptor GPCR types.
And the story is that humans, basically our sense of smell is deteriorating in evolutionary terms.
It's not under sufficient selective pressure.
Basically and the story is that uh something like
about 600 of those thousand receptors have become
fairly defunctional now they've got introns within them junk dna and so on that
means that they're they're they're not functioning as olfactory receptors anymore
so we're left with in humans the story is somewhere between three and four hundred
um functional is there any idea when in evolution this sort of started to happen um,
Yeah, it's a good question. I don't know the answer to that in evolutionary terms.
Yeah, but there are very nice studies that have been done which show phylogenetic.
So you can, I'm not an expert on this, but you can look at the phylogenetic similarities.
There have been a number of papers where effectively you're looking at sort
of dendrogram of similarity of receptors between different animals.
Now, for instance, primates, do they also show the same deterioration?
Okay. So, primates are in a similar class to us.
So, in the human case, I have, let's say, 300 receptor neuron types expressed.
Then how many individual receptor neurons would I have in my epithelium?
Yeah, so the epithelium has between 10 and 100 million in total. For humans?
Yeah. So, you've got a large number of the same type expressed many times. And red, how many?
They probably have similar numbers, slightly more with a higher density because
it's obviously crammed into a smaller area.
Things like dogs and pigs would have at least an order of magnitude greater
than that in terms of numbers.
And probably slightly more different types as well.
Right. Okay, so now we have these 300 different types of receptor neurons.
As a population, they're active. We also know that they are all highly specific
in how they get integrated at the next stage in this glomeruli.
Glomeruli are very much specific to the olfactory receptor neuron types, roughly.
And now with that, also in the case of humans, let's say we can at least detect
about 10,000 different molecules with this.
Well, yeah, it's not so much molecules. There's this number of 10,000 sort of
flies around olfaction.
You're always hearing this number of 10,000. It has kind of a funny story,
actually, that originally this number of 10,000 came from perfumers,
I think, who were asked how many different, say,
different perceptual qualities or different perceptual nuances could you have for a trained perfumer?
And some perfumer just sort of plucked this number of 10,000.
They thought there might be a possibility of 10,000 different perceptions that
you might be able to have.
Um, and that's about the limit of the evidence for it really.
And that number has been sort of bandied, bandied about in, uh, olfaction ever since.
So it doesn't necessarily correspond to the number of chemicals. Uh, and in fact.
You can have almost an infinite variety of molecules that you could have a smell response to.
But I'm sure in the ethology of olfaction, people must have made some sort of
taxonomy of the whole collection of molecules that are detectable in principle
by our olfactory system.
So how large would that set be? Sure, yeah. How is that organized?
We know at least a few thousand different chemically active,
olfactory active molecules that would give you a response in some way.
And how big a subset is that of all molecules?
Well, all molecules, of course, that's including going way up to,
you know, massive proteins, 30,000, you know, Daltons, you know,
30 kilodaltons, enormous molecular machines, right?
Because we're only looking at quite a narrow bandwidth.
There's actually an interesting story about that. At our recent NICE Odor Maps workshop,
we heard that someone did an analysis of the lower end of this quite narrow
spectrum between 30 and 300 Daltons of what we can appreciate.
And they looked at all the molecules down at the bottom
end in terms of um uh in
terms of our response them and they they looked
at the molecules just under the threshold so just under 30 30 daltons and they
called these um infra smells okay and then they looked at the molecules just
above the 300th uh threshold and they called these ultra smells you know in
line with vision as you can imagine and um,
And it showed some interesting things. In fact, there seems to be a kind of
story that these infra-smells may share with them a principle of kind of toxicity.
That maybe these lighter molecules are getting towards an area of toxicity.
And this may also be a guiding principle in terms of how our perception of odors may be organized.
That we're obviously detecting these to be safe or not.
Right. But it should be telling us something about also the specific niche for
which we have been optimized, right?
So why – because it's interesting that we're not sensitive to all possible molecules
because actually in the big picture, it's actually a very small subset of all
molecules we could ever encounter.
So why is this subset then behaviorally and also from a perspective of fitness,
evolutionary fitness? It's really relevant.
Really a good question. i mean i think i think it's probably limited if
uh by by most things is that once you get
beyond 300 daltons things uh don't become
volatile anymore right they're heavier molecules uh they
become less volatile so basically your chances of
being able to uh get these into the air to even be able to smell them at all
starts to become very limited right so you're sort of limited by the physics
now below uh below 30 daltons i'm not quite sure why that lower limit is right
because because you certainly can have lighter molecules than that.
Could it be a limitation of biophysics of the receptor neuron?
For the lighter molecules? Yeah, well, I'm not sure.
It may also be that many of these lighter molecules,
you don't necessarily want to be detecting, like extremely light molecules,
oxygen, CO2, and so on, are so common in the world that potentially maybe they
would flood the whole system.
So maybe you need to be more selective to these compounds.
Although the common story in olfaction is that carbon dioxide doesn't have a smell.
In fact, it does have a slight smell, and that may be through interacting with
other molecular things.
But probably these extremely light molecules, maybe there just isn't a behavioral
reason for responding to them on the basis that they're there all the time.
Yeah, but that's of course with the circular argument, right?
Because you're saying, look, well, we don't smell them because we don't smell
them. I mean, it's illogical in that way.
But at the end of, but no, the argument may be, I mean, if they're there all
the time, they don't give you any information, right? So in the end, it's okay.
But also that argument is not so convincing, right?
But for instance, is it possible that we have molecules binding to our receptors
but not leading to sufficient activation to create percepts?
That certainly seems to be true. I mean, part of the story in my talk was that
there really can be two parameters for a molecule interacting with a receptor.
Receptor, for a long time everyone's considered that the main parameter for
a ligand is its affinity to a receptor, which actually tells you how strongly it wants to bind to it.
And we know that ligands and receptors have a very wide distribution of these affinities.
But it's also very clear that in more recent evidence is that there's a second parameter,
which in pharmacology has been called efficacy and
this tells you once a ligand has
bound to a receptor how much kind of activity does it give downstream
to an orn right and so that's related
very much to your question that if you have for instance uh molecules
that are binding there uh but they don't necessarily contribute much to uh to
a um to a uh to a neural response then this is a classic so-called antagonist
which is effectively filling up a space and sort of creating a blind spot, right?
And up until now, until the last five years, we didn't really know about these antagonisms.
And as you can imagine, they're kind of a bit scary experimentally.
Because in order to be able to see them, you have to look at all possible mixtures,
and you have to see how certain molecules may be blocking other molecules.
And it's only really in the last five years that we've started to appreciate
that, in fact, these types of antagonisms could indeed be really widespread in a faction.
That basically our neural response is nowhere near the linear addition of just
adding on lots of different odors.
There's all sorts of these nonlinear competitions and interactions that are
taking place, and this is probably what defines olfaction.
So that might also mean that the response of glomeruli,
mitral cells, and so on, is reflecting a much more complex flux of binding and
unbinding of very broadly shaped ligands.
Certainly. And how they interact. And we haven't even said anything about the
so-called odorant binding properties which live in the mucosa.
And these OBPs are doing two things.
One thing they're doing is, in quite a clever way, shifting the sorption spectra
for odors out there in the air phase so that we can detect odors that are hydrophobic.
They don't want to be in the liquid phase. Yes.
We need to be able to detect those as well. So that's a solution from nature
to get those into the liquid phase so that we can interact with them.
And another thing they do, they're very large proteins. They themselves have
selective bindings to different chemicals.
And so it's actually an extra layer of building specificity and tuning into
the system to create more information.
It's really quite an exquisitely layered sequence of events that deliver the
certain subsets of molecules in certain places in the epithelium to be processed.
But these odor-binding molecules, is there a belief that they in turn set up
a local dynamic in the mucus layer, or are they just like transporters?
I think you can think of them more like transporters because the mucosal layer
is very thin. It's only a few microns.
And their main job is to basically shift the sorption spectrum to get those
hydrophobic compounds into the liquid phase and transport them to the binding
site so that they can be… You see, they're probably not traveling lengthways.
There probably isn't time really for them to, say, move them to different portions
of the sheet, for instance, or something complicated like this, right? Right.
So there are other mechanisms for that. For instance,
the fact that you've got preferential sorption means that some odors are staying
in longer in the air phase and some shorter,
which means that you've already got a nice sort of almost like postal service
selective delivery of molecules in certain places depending upon how much they want to be sorbed.
Yeah, but I was more thinking you could also imagine that these binding molecules
in turn, for instance, set up a competitive relationship.
For instance, if you have a binding with a molecule of a certain kind that it
leads to, let's say, signaling systems becoming active.
That would change again probabilities to bind to other molecules or not.
Yeah, it's quite possible that there's some sort of interaction between those
at the receptors. I don't think it's really well understood.
And for instance, even antagonism is not understood at all well because the
simple-minded naive theory would be that an antagonist sort of fills the binding
site on a receptor so that another ligand with higher efficacy cannot bind in that same place.
But it seems quite likely that, in fact, there are more complicated mechanisms
which are called dimers, which effectively means that they may not fill the main site.
The main site may still be available for preferentially or high affinity binded
compounds, but that these antagonists may actually bind onto the receptor in different places.
And by doing that, actually change how the conformation of the protein.
So in extremely complex ways that the protein can change its folding dynamics
according to the binding to signal different stuff.
So there's all sorts of complexities there. But in terms of your point about the dynamics,
that's well taken because there's also this other group of compounds in the
mucosa called odor degrading enzymes because a very good question is how do
you get rid of these odors after binding?
There needs to be a whole set of other molecules out there that are effectively
basically removing these signals and terminating the signals.
So it's a very exquisite balance of all of these molecular processes going on
to both initiate the signal and then terminate it as well in a timely fashion.
It's actually, it's already amazing to see, right, that here we're looking at,
let's say, a very advanced variation on, in some sense, the most primitive form of sensation we know.
Because for single cellular organisms, you have, you know, mechanosensing or
chemical sensing, and that's what it started with, right?
Yeah. So, and if you just look at it already at the level of this mucus layer,
the very first point where olfactory release starts, the exquisite orchestration
of all these different pathways and interactions is very impressive.
But then now, of course, we want to go step up and start to think about,
okay, how does this in the end lead to the encoding and representations and
detection of odors in the environment?
And so the first thing, of course, we want to understand is what's really this molecular language?
How would an olfactory system in the end really, if you want, decompose molecules?
What are the key features of molecules our olfactory system is sensitive to?
Yeah, so there's some very nice data sets. I think finally olfaction is coming
into the 21st century of sort of data sharing and there's some very nice data
sets with many different odor conditions and optical imaging on the receptor surface.
Using these methods, we can directly see actually what the, for instance,
calcium levels of activity are, different portions of the receptor sheet.
And large data sets which have shown how different portions of the sheet are
responding in different ways to different groups of chemicals.
And it's very clear when you look at these sorts of databases that different
portions of the receptor sheet are responding to different groups of compounds.
So there are sort of, you can see common properties of molecules that may be
preferentially tuning or activating different parts of the receptor sheet.
And so this is
leading to the idea that there is a kind of chemotopic mapping
of the receptor sheet rather than the receptor sheet
being mapped onto physical space as
it is in say vision on the retina the
mapping seems to be a chemotopic mapping so we talk about different molecular
features being mapped to different areas and this may be for a couple of reasons
a number of reasons actually one reason is this sorption thing that I talked
about that compounds with higher sorption parameters,
so they like to be in the liquid phase, they're more hydrophilic.
Those will basically absorb into the earlier parts of the receptor sheet as you sniff.
So they'll be sort of more delivered to the front end of the receptors where they live in the mucosa.
And other compounds that are more hydrophobic would be delivered later,
further back in the receptor sheet as you sniff.
And there are other reasons is that this family of receptors that I talked about,
in fact, it turns out that there's at least four classes of these,
although this is currently under debate, but there's two very, very clear classes,
class 1 and class 2, which correspond to fishoid receptors, which in fact are
leftover that we have from when we were fish.
They're dedicated to detecting non-volatile compounds largely,
and they are exclusively expressed in certain zones of the receptor sheet.
And so, as you can imagine, this leads to another form of chemotopic mapping
where you get a preferential response.
And these FISH-derived receptors wouldn't go for the higher Dalton molecules
because these are the ones that are less volatile?
No, not necessarily because whether a compound is hydrophilic or hydrophobic
depends more on actually the charge on the molecule. So it's more to do with the sort of asymmetry.
No, but you said they went for the non-volatiles or the less volatile.
Yeah, yeah, yeah. So I was wondering then whether these fish-derived receptors
are also sensitive to molecules with higher dose. Well, there's two things that
determine sort of volatility.
One is the molecular mass, and the other thing is whether they like the air
phase or the liquid phase, which is the sorption profile. So it's really those
two things together that determine.
So in a fishoid receptor, they would be exclusively compounds that they have
sorption parameters that they can never make it into the air phase really at
room temperatures unless you started boiling them or had a lot higher energy.
Okay, but so now we have a zoning, if you want, of the epithelium.
And the zoning might correspond to different properties of molecules like their
cyclization, their carbon number, bond saturation, branching,
substitution pattern, functional groups.
So are these the key chemical features of these molecules that you would map onto these zones?
These are at least a number of key features. Those ones you've just mentioned are very important.
This has been another area of a crucial study.
There's databases. databases uh there's some very nice software that you can
download for public use called dragon,
and for pretty much any molecule you might care about you can go to this package
and it will give you something like um.
I think it's about 400 or 500 different exotic descriptors for a molecule,
branching, all sorts of things that you can't imagine.
So a huge number of possible descriptors, because, of course,
you can describe a molecular structure in an almost infinite set of ways that
you might choose to describe that, right?
So these databases and chemists
have been very carefully characterizing different descriptor
or what you might call molecular determinants and then
of course you can play very interesting games like um
look at all those odorous compounds and look
at all the descriptors are there are there certain descriptors that
may be more important for certain areas of the receptor sheet
and others that aren't and so on you can
play games like this and you find that um indeed there are
um but you also find that um all of
these descriptors are highly redundant and what i mean by that is that um you
know you don't find for instance that there's one and two one or two descriptors
that sort of uh that sort of tell you very orthogonal or decorrelated information
they all tend to be measuring a similar thing.
And uh and so if you take for instance a pca
or principal component analysis of the contribution of all
of these discriminants to what you might think of as a perceptual description
of an odor you find that there are large numbers
you know because they're highly redundant so right exactly you
can no but so this is very interesting right because
so here we go if you want i
mean this is a little bit proto-science right where we just develop descriptors
of reality so the same holds for chemistry so here we are for this huge collection
of descriptors um but now on the other hand you'll see also you see two things
right so on the one there's a zoning a zoning in the olfactory system that would suggest,
that the properties of these molecules are in some sense also mapped onto this chemotopic map.
And that, of course, would also give you, let's say, this should give you some
sort of hierarchy of which descriptors actually are helpful or are,
from a biological perspective, relevant and which are indeed,
in that sense, redundant. That's one thing.
But then you showed, which is also very interesting, Interesting.
And if you analyze in more detail these descriptors and the odor space that
they define, that actually it's much lower dimensional than these descriptors would suggest.
Yeah, well, that's another very interesting story. Although that one that I
showed in the talk was actually about analyzing perceptual descriptors for a corresponding odor.
So you can do another thing, which is quite a lot of fun, which is to take a
large number of molecules, such as in the Dravnik's database,
where about 150 different molecules were taken.
And they then used a large pool of trained olfactory specialists,
and they asked those specialists to describe each of these 150 odors using a
panel of approximately 150 different descriptors,
such as musky,
grassy, nutty, so on.
So large numbers of these and then you can also look at the space for similarity
of those for different molecules so you can plot individual,
molecules in a high dimensional space and you can look how similar or dissimilar
are these discriminators so you can make a sort of odour map.
Of perceptual odor map in terms of
what you would expect is that you'd expect those points
uh close in this map to have similar perceptual
properties and therefore you'd expect them to have similar molecular properties
right so it enables you to play this nice game where
you can look at different portions of this map and see are
is there a nice continuum here in certain molecular
properties that are moving this perception in
this direction or whatever But what
you find which is very interesting when you do this Which was studied by Kolokoff
paper about three years ago Was
that when you analyse all of these 150 descriptors You
find that they're placed in a
much lower dimensional space
So it means that effectively they're highly
correlated right so and and um
they should they found that there was a uh at
least in a two-dimensional manifold was able
to explain something like um 60 or
70 percent of the variance of this of this 150 dimensional structure can actually
be explained quite adequately reasonably well in this two-dimensional manifold
which is spread it looks a bit like a potato chip sort of shape within this.
Higher dimensional space.
And so I think it's very exciting actually. I think we will find in the future,
olfactory studies, we're going to find that there are all sorts of these.
Lower dimensional manifolds, which is kind of the answer to solving the olfactory
code to find out there has to be some underlying similarities and structures in these things.
This is of course where we want to get to, right? One thing that's,
of course, interesting here is that now, with this last analysis,
you could argue, well, maybe human experience of olfaction is relatively low-dimensional.
But what's complex are all these descriptions we glue onto.
It's also the language, right? Because this is limited by language.
The language just complexifies the experience.
But what's amazing about olfaction is that we have no other way to describe
certain olfactory cues other than it's like other things.
So this is kind of the interesting restrictions in the language because we say
it's like grass or it's like this or like that.
It doesn't seem that we have any kind of independent, really independent descriptions
of owners other than just likening them to other things. Right.
So there are no intrinsic qualities that we can use. Well, it turns out when
you look at this map, because you can look at this manifold and then you can
look at what are the actual qualities and how does this manifold actually represent the odors.
When you do that, you find some interesting things. So you see that the main
dimension of this map very strongly correlates to pleasantness and unpleasantness.
So maybe this is the only true underlying parameter, whether we actually have
a hedonic or whatever the valence is for the actual. Exactly right, yes.
Which would be interesting because then you would have, let's say, valence and intensity.
And this would be very compatible with how we can describe the emotional states.
We would think of valence and arousal, something like this.
And then within such a two-dimensional space you can
find all sort of very complex emotional descriptors yeah but but so but the
point i want to get to is then um how what does this now mean for this idea
of zoning of of of the receptor neurons in the epithelium would that match would
it match in any way the structure of this descriptor space or not,
um yeah that's a really good question um.
Yes, I think it probably will. I mean, there are another approach to describing
the chemotopic mapping,
and this is work by Kaniezo Mori, who's been very influential in the field,
has made a very interesting argument that this space,
although it's a chemotopic space,
may also be structured behaviorally.
So certain behaviorally relevant compounds may be represented in certain parts
of the bulb and other differently behaviorally relevant compounds in other parts.
And you would expect those compounds to have similar perceptual kind of descriptions.
Right so it does seem that the
the current thinking of uh mapping of
the first stage of odor representation in the olfactory system comes down to
both chemotopic in terms of molecular properties uh and probably uh behaviorally
behavioral relevance in different ways uh which will then of course have some
type of relationship to perceptual quality right exactly,
and this is cool right so now we've had a bit an idea of the structuring of
this of this sort of The receptor layer, there's a structure.
And now if you measure, and these are classic experiments that you also showed,
if you measure the response of single receptor neurons to different kinds of
odorants, different kinds of molecules, you see that actually they're not so specifically tuned.
So what's the deal there exactly? Yeah, no, this is a very good point.
What you find when you look in detail at each one of these receptor types is
that they tend to have what we call broad tuning, as you say,
to large numbers of compounds.
But yet there are also a sort of smaller subset, I guess, which are more narrowly tuned.
So it seems that the current story is that in fact the olfactory system has
a variety of broadly tuned and more specific tuning.
And one of the things I showed in the talk, which was some work that I did with
Manuel Sanchez quite a while ago, actually 10 years,
where we applied an information theory approach to basically describe the accuracy
that a neuronal code at the epithelium would be able to reconstruct the original stimulus.
And when you do this for very high dimensional stimulus like we have here with
large numbers of compounds,
you find that in fact this is exactly the perfect strategy that you need because
you want to have certain receptors that are very broadly discriminating between
groups of compounds. So they're very broadly tuned.
And then you want other receptors which are doing more fine things.
Uh discriminations between you know more specific sets
of compounds and that if you have this
sort of two layers and some continuum between them uh
in information theory terms this gives you the best possible ability to reconstruct
the stimulus so you you get in a high dimensional space now if your problem
the reason why this is interesting is that uh if you restrict the problem to
a lower a dimensional space,
such as I know Bill Hanson, Peter Mombats were here last week.
And for instance, Bill will be working, or the animals that Bill studies will
be working in much more restricted dimensional space because ecologically, maybe there's fewer.
Compounds which are more relevant to, you know, ethologically and ecologically for the animal.
Then in those types of olfactory systems
you tend to find that there are more specific receptors
because you don't get this advantage of
having lots of broad tune because you don't have the similar very
high dimensionality so your your prediction is that
the ratio of broad or specifically tuned
receptor neurons would scale with the outer space
you have to classify absolutely and the complexity of
the world within you within which you have to operate right because that this
Just coming back to the point I made at the beginning that if you need this
very xenobiotic property where you have to be able to basically associate any
potential molecule out there in the world with a particular behavior or whatever,
then you very much need this ability to deal with very high dimensional stimuli.
Because if you're an insect, there's probably no real requirement for these
sorts of things in certain settings. Right.
But now, let's go back to this theoretical study you just described.
And sometimes you have a fairly linear decomposition of the olfactory system.
A perception problem, right? Because you basically, you assumed,
look, I have a bunch of, I have a stimulus, stimulus comes in,
I have my receptor neurons, they feed directly into some sort of response.
Presumably in the glomeruli or the mitral cells.
And then I'm going to use some estimator. You don't need to make any commitments
where this estimator resides in the brain because now we're doing it for an
information theory game.
And now I get an estimated stimulus.
And then the game is that you really want to understand the parameters under
which this estimated stimulus is as equal or similar as possible to the real
stimulus, so you have a minimum. The error is minimal. Exactly. All right?
So then, of course, the usual game is, well, the response will have some noise,
and this noise will have certain properties.
So because of this noise, I cannot really inversely map anymore my response
to the stimulus. Exactly.
Right? But okay, when the noise is big. It corrupts the original. Exactly.
So now you use this Fisher information approach to decompose this problem and
understand what's the optimal strategy.
Exactly. So how does this Fisher information approach help you with this?
Well, Fisher information effectively characterizes and quantifies very precisely
two things, which is both your sensitivity to a stimulus.
So the sensitivity that a receptor
has to a stimulus and also how that sensitivity is corrupted by noise.
And then it also gives you another very nice thing, which is how does that add across a population?
Because you might know that for one receptor, but how do you know how all that
sort of information adds when you have a population? And so Fischer information
precisely quantifies that effectively.
So for an array of receptors, it quantifies very precisely for each receptor
what the contribution is to the sensitivity for each one of the dimensions and
also how that is corrupted by its own noise.
And effectively, when you characterize that, there are some very nice results,
particularly by Kramer-Rayo, which tells you that the inverse of this matrix,
which contains all of this information about the noise and the sensitivities,
basically the inverse of this directly tells you what the best estimator can
do in terms of being able to,
reconstruct the original stimulus.
And so therefore it tells you that it doesn't matter what the biological system
is, it will not be able to exceed this limit and so it's nice very nice I think in the sense that it,
provides a very defined limit on
a psychophysical test that you might do to tell
you for instance if you had a stimulus and you changed it a small amount so
the just noticeable difference the just noticeable difference or the jnd in
the psychophysics experiment would relate directly to this uh inverse or this
kramer around which comes so it makes a very nice firm prediction
for the architecture of a neural system and uh and and what the limits would
be for an animal right have you been able to to validate any of this any of these predictions.
Um well in terms of relating it to psychophysics
um that's something
that's something we should we should push forward but
in this experiment we're only really doing it as a game rather than because
it doesn't really matter what the precise value is because all we were really
interested to know is what are the actual features of the receptors which either
give you a good or bad ability to do this.
Right, but now you do assume that in this case you only have a linear interaction
between your receptor and your actual linear combination, right?
And is that not a very strong assumption?
It is quite a strong assumption. I mean, what you're saying is that a receptor
is giving a linear response to a set of combinations.
So, for instance, things like antagonism are kind of out. As we just discussed, right?
Yeah. So, you have to go to a sort of second order. Yeah.
You could extend it to a second order, which we haven't done.
But really it was a quick study to just show what the features are.
And I think the main sort of interest in it is that the simple properties of
the simple-minded approach is that you get this sort of mixture of diversity
in the outputs together with specific receptors that very clearly matches what
you see in all experiments from insects and mammals.
In some sense, you try to make sense of this idea of population coding with
broadly tuned receptor neurons that have, again, variability in this tuning.
This is the key point of this study.
Exactly. It's just to show that what the biology is doing is a sensible thing
in terms of when you're facing a very high-dimensional input.
Right, which is interesting, right? Because very often you hear people making
these wild claims about how suboptimal, inefficient natural systems are.
But I think, certainly with this example, you'll be hard-pressed to do it better.
Would you agree with that? Yeah, within your space, within your defined space
of the problem you're dealing with.
Right, exactly. What would be interesting to compare, which we haven't done,
is to then compare these measures maybe across animals, right?
Sure. To show that different levels of specificity,
like we were discussing, processing may be more or less relevant to
the dimensionality of what you're dealing with in your in your environment
but now also the other thing though with your with your
model are there two things on the one hand you do assume that
after let's say one stage of of processing
one of transformation you can reconstruct your stimulus right that's your quality
measure well we don't assume that it actually does that all we're saying is
that this places a bound on what any subsequent neural processing would not
be able to go beyond this effectively any estimators.
Provided that any subsequence step is fully isolated from any preceding step.
It has to be a cleanly modular system.
No, I mean it just provides a limit on that. But what it's really saying is
it provides a limit given the noise that was introduced.
So it's based on the assumption of what the noise was at the periphery.
But for instance, if I would have a subset of projections that would also percolate
up this processing hierarchy, bypassing noise stages or following a different kind of noise dynamics.
Oh, okay. Well, the noise we're thinking about is only at this stage the noise
at the receptors, because all we're characterizing is the receptor population.
We're not saying anything about subsequent processing. So all we're really saying
is the question we wanted to answer was what should the tuning of receptors be,
not necessarily the subsequent neural processing, which is
a a different story the reason why you can't really do that with
this measure is that the subsequent neural processing has all
sorts of complex time dynamics as you say quite rightly
there's all sorts of maybe noise properties there in complex
ways there's all sorts of attentional processing and
so on so uh at least in
this formulation of uh let's say
thinking about the fisher information it would not make a
whole lot of sense to okay try to extend it beyond
uh subsequent neural processing thing we're just
really trying to limit it to what the receptors themselves
are doing right but now what so what's the signal to
noise level you find in these receptors signal to
noise um uh well obviously it's proson we find proson firing uh quite close
uh to sort of cortex uh sort of proson i mean as you know in cortex i think
the fano factor is slightly greater than in Poisson, so they show more variability.
The story with the variability of firing, you sort of see that also in receptors.
So you see large amount of variability in the firing, beyond,
slightly beyond Poisson firing.
Okay, but the other thing. That's the noise, as far as the receptor population's
concerned, it's both the noise and the firing. Right, exactly.
But now the other thing is that in this case,
This is under, I assume, a constant drive with a single molecule.
But the real system will be operating in the face of a continuous fluctuating
stream of molecules bombarding these different receptors with varying binding kinetics and so on.
So would this statement on this upper bound of processing capability also hold
if we start to generalize to this more realistic input condition?
Sure i mean it may not uh there's all
sorts of complex dynamical processes going on there and
the fact that it's a first order description is is
just the simplest minded thing you can do in order to right in
order to uh just look at the properties of
the system so i don't think we make any claim that um that
maybe it has a great expectation
to match very precisely with the
psychophysics right because also you don't
know how much uh the information either gets
selected or not selected downstream right because
you have all sorts of selection processes as well right so when you come to
perceptual processes um you've got all sorts of selection of the information
going on so um yeah and you don't have complete information at the periphery
to to be able to say the exact state of the system to ever really be able to test very precisely
how exactly accurately this would be characterized.
Right. But then the other issue that actually we haven't touched upon at all
is that, okay, we have sort of talked about these molecules.
The ability to detect them, the reliability of the system.
But actually a key issue for olfaction is concentration, right?
So how does an olfactory system really deal with varying levels of concentration?
I mean, it can go from really minute, that's a homeopathic quantity, to full saturation.
So it has to operate in a huge dynamic range. So how does it manage to do that?
And what kind of sensitivity do we get?
Yeah, I think there's a number of tricks for this.
This uh the the system as you say is in a it's about the most remarkable i think in the,
in the nervous system and having to deal with such incredible uh dynamic range
you've got about probably at least 10 orders of magnitude in concentration which
is phenomenal phenomenal uh.
Uh degree of variability uh and
it's pretty clear that the olfactory system has to use all sorts
of tricks to deal with this one of the tricks is going
back to the original point if you have a very wide variety of affinity distributions
then if you do this um you can still have some receptors binding um or their
binding is changing even when the concentration is very high because you might
Even though you probably have, for a given odor,
you'll have some high affinity receptors, which will get bound very quickly, lower concentrations,
and they will quickly become saturated in their response, which doesn't inform
you anymore and get any more information out of those because they're just saturated.
But then, if you've also got this population with lower affinity receptors,
then even though the concentration is increasing a lot, you're still getting
some information from those lower affinity receptors.
So by using this very wide range of receptor affinities, you can still get information
through to the system that there's a change,
which you can, that then gives you choices in terms of the coding to still be
able to at least receive the information over a large range.
Now that's one strategy.
Our study also shows by looking at these antagonisms, and we made a,
again with Manuel, this was work with Manuel in Madrid,
we looked at these receptor antagonisms.
And there are some very nice and simple
pharmacological equations that we could apply to olfaction which hasn't really
been done at all before using these efficacy models and there's something called
an operational model in pharmacology which is used effectively to describe drug
binding on cells and so on.
So in these studies with Manuel Sanchez-Montanés you started to develop more
this notion of a ratio code.
Yeah, exactly. So what does it exactly mean? So what you find is when you apply
these very simple description.
Then what you get is that you get effectively two parameters for each ligand,
both an efficacy, which tells you how much it contributes to a cellular response,
and its affinity of binding.
And when you operate in this way or think about olfaction in this way,
you get this nice property that something with a low efficacy could effectively
block a space and you can get, when the receptors start to become filled,
you can get effectively a competitive binding regime.
Where you get certain sites are being filled but not contributing,
for instance, or other sites that are filled and contributing a lot.
And what you find when you look at the equation is that it very simply drops
out that instead of the response of the cell being determined by the distribution
of the concentrations of the different ligands within a mixture.
In the high regime when there's this competition, it actually turns out that
it then becomes depending upon the ratio of the concentration.
So it's no longer concentration-dependent, and
it's in this mode that we call ratio mode operation of the neuron when it's
operating in this high occupancy regime that you lose the concentration dependence
and you gain a sort of concentration invariance property,
which we've shown holds over very large numbers, orders, and magnitudes.
So although you might think it needs some very complex neural processing to
maybe solve this problem,
it's kind of a nice result in the sense that maybe just directly at the periphery
in terms of competing for sites may actually solve this problem for you almost for free, right?
Well, nothing for free, right? Well, for free in terms of the neural processing, right?
Because you don't have to. Okay, that's fair enough, but this works under certain
assumptions of binding and receptor distribution and so on, right?
So you could also imagine that in these highly saturated regimes,
you actually have so few unoccupied receptors left that the probability to actually
exploit this invariant or concentration invariant coding, that probability is then very low.
Um how do you mean exploit well that
so you're saying look the trick is
that um at some point you have saturated your
receptor sheet but still there are few receptors available that you can get
you can use yeah and those can then tell you uh about the presence or absence
of a certain odor independent of the concentration exactly right but now i'm
saying well that's that's fine But if I have saturated my receptor sheet for, let's say, 99%,
then the probability for these unbound molecules to still hit those receptors
might be so low that actually I will not be able to exploit my concentration invariant encoding.
Yeah, no, that's a good point. But you have to keep in mind that you have a
very wide variety of affinities as well.
So whilst there may be a subpopulation of receptors that are forced into this high occupancy regime,
gene it's quite likely that it's the
case it's probably only a small subpopulation out of
the total and that there will be large
numbers of other receptors with lower affinities that
will not therefore be in a they will be in a relatively low.
Occupancy and so they are still very much sensitive
to chemical changes so if this.
Is true and we're still look we've fitted it to the data.
That we've received and it matches the data that we've received.
On antagonism for instance from tahara and an earlier
paper by anderson on insect receptors
all the data we fitted works but if
it's true it predicts that there's a
very strong prediction that there would be likely for any
natural odors at
naturally relevant or behaviorally relevant
conditions there will be a subpopulation in
this high occupancy regime that that
will give you uh if you like the relational features
between a complex odor so for instance in a coffee uh
they'll be you you know the quality of the coffee is very much
about what is the ratio of of
this peak of this compound compared to another compound as
to whether that's a pleasant coffee for you and you don't
necessarily need to need a certain concentration to
know that that sort of that works across many orders of
concentration but on the other hand you
want probably other parts of the olfactory system that are able
to tell you about concentration right and it's already been demonstrated that
in fact the olfactory system this is one of
its amazing properties right that in fact that you can separate um you can separate
both concentration information for certain ligands and also it's sort of what
we call configurable properties which is basically the sort of combined percepts
so So on the one hand, you've got to sort of gestalt.
Sort of uh odor or flavor which is
to do with all these sort of ratio properties which are invariant across
concentrations and on the other hand you've got this sort
of opposite of gestalt which is able to sort of tease them
apart and look at the separate component concentrations and
with this mechanism you you then have a population when you combine this mechanism
with a wide range of affinities which we know is in the olfactory system you
have this very nice feature that you You would have these sort of subpopulations
doing different things to either support either gestalt processing or non-gestalt processing.
But now, with that separation, would you imagine that there is a possibility
for the brain to control this in a more top-down fashion, so you get something
like an olfactory attention?
This is a very good point. I think probably not at the binding level, right? Right.
But we do know that certainly the feed forward gain in these different pathways
from the different receptors, we know very well that this is...
That's a level of receptor neurons though.
Not necessarily the receptor. Well, there is some evidence that there's even
feedback actually to the receptors.
Okay. It's still not really well studied, but we know very, very surely that
the readout of the glomeruli themselves,
which is effectively at an early stage of that processing, is reflecting the
excitatory drive of the common set of olfactory receptors.
We know that those are under very complex inhibitory control and gain control,
top-down imposed from the piriform cortex and through centrifugal feedback coming.
I mean, coming back to the system, we know that that has a very strong influence
on the type of codes that go forward to the higher level.
So it's very likely that there's an additional sorts of then selection mechanisms.
If you could imagine there are subsets of these different receptors,
you could imagine that then downstream you could select between those that are
either in a ratio mode or in a concentration mode, for instance.
So that might be another way to deal with that problem that's not only feed
forward and dependent on the receptors themselves. So this could be synergistic mechanisms.
All right. So now we have a bit of an idea how we get our first responses in
and then in the modeling work that you have been doing.
You try to also get a clear idea about the actual encoding of now these odors
at the level of olfactory bulb.
So what can you tell us about the encoding that then happens of these odors?
Sure, yeah. So the main feature in the olfactory bulb, which is really the first
site of neuronal processing,
of all this stuff coming in from 10 million receptors in the olfactory mucosa,
as it comes through the skull,
the cribriform plate, to make contact with these glomeruli.
And the first thing that happens is they converge into these glomeruli structures,
and they are innervated by these mitral tufted cells.
And basically the story is that one single mitral tufted cell is innervating a single glomerulus.
So they're acting as a readout, if you like, of a single chemotopic feature,
because they're only innervating those common GPCR or receptor types. Okay.
And then all of those mitral receptor population,
which acting as the main readout of the bulb to the higher centers like piriform
cortex and so on, are under very quite complex lateral control.
So for instance, a mitral cell is under control from what are called granule
cells, which make lateral connections across the bulb.
And it's even been demonstrated that granule cells may even connect across the
whole length of the bulb, with quite specific connections, in fact.
So there's this network of
quite specific connections that are going laterally they're
forcing an inhibitory drive on the mitral cells
and there's at least two effects of this well three really one effect is that
it's been demonstrated quite clearly that this lateral inhibition has a sort
of on-center-off-surround type property So because it's also,
it's a complex type of inhibition and it's a unique synapse called a dendro
dendritic synapse, which is actually pretty unique in the whole brain in these granule cells.
You don't really see it anywhere else. And the effect of this dendrodendritic
synapse is that a mitral cell will activate one of these lateral granule cells,
which will then in turn inhibit a distant mitral cell target,
but it will also excite itself back because it's a two-way process.
Didactic synapse and the
effect of all of this anyway is that you get a sort of on center
of surround type property because it's sort of inhibiting with
a sustain with some nice time
dynamics exactly and it's been demonstrated that
this very nicely decorrelates and sharpens the representation
of these odors at this stage in
the olfactory bulb so that's one clear thing that happens another clear
thing that happens is because of this um very nice
balance of excitation drive coming in from
the receptors via the glomeruli with this
lateral inhibition that you're getting some very complex
dynamics right so in fact the olfactory bulb was the i think it's fair to say
actually the very first neuronal recordings in in the brain by lord adrian at
cambridge measured also the first oscillations in the olfactory bulb.
And oscillations are the very key feature and they come about due to this balance
of lateral inhibition and excitatory drive.
So there's these very complex sort of dynamics going on together with these
sort of sharpening things.
And the third feature is that we, as we mentioned before, that these
granule cells also act as targets for the centrifugal feedback coming back from
the higher centers to selectively change this lateral inhibitory mechanism to
effectively selectively change
the gain in different channels so that you may listen, for instance,
more to some types of chemical information and suppress others, right?
Right, exactly. So this has already been shown to be very important.
But now, do you see these mitral cells and linked to this complex network of
granule cells and they are sampling the glomeruli,
are they just adding up activity and emitting spikes or is the encoding of these odors more involved?
Like, for instance, does it depend on population responses? Does it depend on
the temporal structuring of these responses?
Yeah, I think evidence shows, for instance, Detlef Schildert-Göttingen has shown
that the latency of the mitral cell responses is very crucial to different compounds,
which you might expect right because if you've got
this dendro dendritic thing you can you have
these periods of inhibition so what can
happen is that some mitral cells can selectively shut
down other mitral cells in these channels for certain periods of time and so
what you seem to find is that if you look at the population of um olfactory
bulb mitral cell responses that you get these different latencies in the different
channels and it's been demonstrated that these latencies are very important
for coding the different odors, right?
Do these latencies also express the binding kinetics?
You could imagine that a receptor with high affinity to a ligand will trigger
a short latency response in its target.
Yeah, I think that's quite possible, but I think it's also reflecting the timing
of these lateral inhibitions and the timing of the dendritic synapses,
so I think it's probably both of those together.
The other very interesting feature, which was shown very nicely by Rainer Freydig.
Was to show that the spike timing of individual mitral cells in this oscillation
in the olfactory bulb may well be very important for coding different aspects of a complex odor.
So, for instance, if you're firing early on in the cycle with another set of
receptors, this may have one meaning.
And if you're firing with another subnetwork within this olfactory bulb space
later on in the oscillatory cycle, this may mean another thing.
And so he was crucial in introducing this idea of multiplexed odor codes for mixtures.
So maybe you have a mixture where you have different synchronous firing between
different subpopulations at different points in time.
And that these may well be telling higher centers all sorts of information about
grouping or binding of different chemical properties to tell you about,
you know, much more complex chemical stimuli that we know after all is like
coffee with 400 compounds or whatever.
Okay, so now we have a bit of an idea of the biology of olfaction.
In sort of a parallel existence, you also worry a lot about building artificial
olfaction, artificial noses, olfactory machines, olfactory robots,
and so on. So what are the challenges there exactly?
Yeah, so it's a kind of parallel life to kind of build technology.
I mean, similar to you, actually, that I think we both follow this idea that
if we really understand it, we can build it, right?
So let's go and see the proof by, can we take these principles,
put them into some type of operational system, test what they do.
And it introduces a whole bunch of challenges that a lot of them actually have
very little to do with the biology.
Because, for instance, just getting a good set of sensory signals to start with
and reliable sensory responses is already a big challenge in chemical sensing.
The idea of so-called building chemical analysis systems along the lines of
following the biological system is not a new idea.
There was a paper in Nature in 1982 by Krishna Persaud and George Dodd,
who introduced this idea of putting together groups of different tuning chemical
sensors in arrays and to look at population codes of these responses,
which is clearly similar to how this is solved in the biological system.
System um and you know
so then what you need to do is you need to you know you face a challenge of
finding different chemical technologies that give you nicely orthogonal and
decorrelated sort of sensor responses um and that are sort of robust and give
you reliable signals um that have sort of have reasonable noise properties,
can be easily deposited.
Can maybe have a wide range of different molecular interactions and have sort
of broad tunings to different things.
Maybe reversible responses.
So you don't really necessarily want sensors that don't ever recover from an interaction, right?
So there's a whole bunch of issues with chemical sensors.
And I And I think whilst things are improving, there's actually a massive range
of chemical sensor technologies you can use.
There are clearly many issues. For instance, one of the main issues,
I think, in chemical sensors is this concept of sensor drift.
So we know that pretty much any chemical sensor, its properties will be quite
non-stationary over time.
So it may have one set of tunings at one particular point in time.
You may come back there in a month's time and have a completely different set
of tunings, baseline parameters and so on.
These can change in very complex and difficult to predict ways. Uh-huh.
On one level, okay, so this can be quite frustrating perhaps when you try and build these systems.
But on another level, we also have to appreciate that every individual person
has pretty much a unique, we haven't discussed this, but we all have a unique
genetic fingerprint of the subset of thousand olfactory receptors that we express individually.
So coffee actually smells completely different to you than it does to me.
Well, but maybe not because earlier
you said that there's actually a low dimensional manifold. default.
Maybe we map it onto something that we can describe in similar terms,
but what you can guarantee is that the receptor information that comes into
your system is completely different to that that comes into mine, right?
But you're right that this has to be somehow mapped.
That's kind of my point really, is that somehow the olfactory system has to
take care of this and map it all onto a stable representation of something that
means coffee. And one that's It's unitary, of which you can say that's coffee,
not that you say, oh, it's 400 compounds.
Yeah, exactly. And the other point is that actually in a month's time,
because these olfactory receptors are basically the only receptors that are
in direct contact with the outside world,
they're really at the tough end of things, unlike most neurons,
which are very nicely protected in the neural systems, that they basically suffer damage.
And so in a month's time, you and I will both have a completely different set
of olfactory receptors than those that we have now.
Now they'll be similarly genetically determined, we'll have a sort of similar
subset of the total, because that's fairly determined, even that may change,
but somehow the olfactory system has to deal with all of this,
and in a sense that's kind of like drift, right?
Because we already know that, for instance, as the olfactory receptors are developing,
that their tuning is changing.
They actually become more broadly tuned over time. They start more specific.
They become more broadly tuned. And so there's a whole drift process actually
going on in the receptors as well.
And, okay, that drift process is probably very different from that that we see in the receptors.
But it's still a challenge that the neuromorphic system might nicely be able
to solve. Because if you look at a classical engineering solutions to this,
then it hasn't quite totally solved all of these issues.
Tell me, what are the different approaches people have taken to solve this problem, technically?
Of drift? No, and chemical sensing. Let's start with that. Okay.
Okay, well, yeah, I mean, that's a very broad question, right?
You can go all the way from developing biosensors through to developing highly
specific sensors for particular ligands that you're interested in with a problem,
all the way through to developing sort of electronic noses, using,
you know, arrays of broadly tuned compounds, compounds
through to using sort
of mass spectrometry ideas where you're trying to
basically measure specific molecular features directly like we were talking
about originally there might be mass features or through to using gas chromatography
effects where you're trying to select between different molecules due to how
much they sorb into different materials or not.
There's a whole bunch of different strategies uh so
what's the sensitivity of these systems compared to the biological system
this is a great question so um well actually
we did a study right a long time ago which we're together remember that um we
we we we had this question in mind uh when we first came to this nice artificial
moth project that we ran that we were wondering well okay so we know that the
moth is very sensitive overall it's like the world specialist in olfactory detection,
might we expect that, in fact, its receptors are, you know, orders of magnitude
more sensitive than, say, for instance, a metal oxide sensor?
Like a commercial one you just buy. Yeah, which is the most common one that
tends to be used in these kinds of arrays.
And indeed, what we found when we took those measurements were that,
in fact, no, you know, that these sensitivities were kind of more comparable
than you might expect. So that was kind of a surprise.
But you also, of course, in doing any comparison like that, you have to take
into account that we're not necessarily giving that receptor its ideal ligand.
We may be particularly one extreme end of the affinity for that particular ligand or efficacy. Yeah.
Parameter you know it may have other ones that it's much more sensitive to
but it's very difficult to tell unless you measure everything which
is impossible no but wait but but there's an important point here
right that sort of intuitively you would say like well you know if i have to
increase the sensitivity i should be just optimizing at the sensor front end
of this whole system if i just have high affinities there i'll be just fine
in my detection but maybe but maybe that's not the right philosophy no you really
can't do that because then everything binds to everything.
Right and then you don't have any information anymore so
already in the story that we saw with the antagonism as
we talked about before is that it really to get this
ability to do this over a range and to
to also be able to get the combination in a
single olfactory system of sensitivity together
with high specificity to get those
two things together and to get them over a
broad dynamic range you cannot just be sensitive to everything
right you have to you have to have this
range of binding so that you can basically you
have to you have to have it so that you can have a kind of attentional mechanism
to right to be able to focus in particular but it's not respect that the point
is maybe if you want to get a highly sensitive olfactory system the real tricks
sit more at the processing end of things than at the sense of content yeah i
would I would agree with that.
I mean, it's certainly clear that there's so many tricks that are played,
you know, by the biology in terms of getting a sensitive, specific.
Robust, you know, signal representation out of this that the more I look at
every level of the system, it's completely optimized to achieving just that.
But now the amazing thing is that in one of your projects with your collaborators.
You built an artificial nose where you specifically looked at the impact of
the mucus layer in processing of olfaction.
So why did you add that feature to an artificial chemical sensor?
Yeah, it's a good question. Well, in existing electronic noses up to now,
really there are two forms of information that have been exploited.
Uh, the first, uh, mechanism is this, uh, population code.
So for any particular complex or simple molecule, you've got a particular fingerprint
of, um, receptor responses. Mm-hmm.
This is clearly, this is what you would call a spatial code of receptor population
tuning that give you stimulus-dependent information, enable you to tell what's out there in the world.
You've got a second mechanism, clearly, which is that each of those receptors
or sensors is giving you, in itself, specific temporal information.
So this gives you sort of, en masse, gives you a sort of temporal code,
temporal population code as you might call it.
But then there's this very much less studied aspect of olfaction,
which is that not only are the sensors themselves maybe giving out temporal
information, depending upon what they're binding with.
That in fact the stimulus delivery itself may have some nice temporal properties.
And so we went about building what
we called an artificial mucosa and this was with collaborators at
university of warwick including julian gardner who build microsystems so i don't
build any sensors myself i build these sort of strategies for processing architectural
ideas for these types of systems and so we specifically you know we it was a
very nice example of where you see a few neuroscience papers,
and this basically inspires you to then go and build a technology that you think
can do better than what we have at the moment.
And what we ended up building was effectively a microchannel or an artificial
mucosa where you have a sort of sniff.
So rather than in many electronic noses, you're sampling for long periods of
time with very controlled flow rates.
This is a very different strategy, where you just effectively have a pulse of
odour that flows through a microchannel.
And then what you have inside that microchannel, similar to as we talked about
in the nose, is you have a thin mucosal layer, maybe a few hundred microns thick.
And this can be of different materials, but you can take ideas,
for instance, from gas chromatography that use what are called stationary phases.
And these stationary phases are specific materials you can go and buy off the shelf.
And they have very beautiful selective properties partitioning
properties of certain groups of compounds such as hydrophobic or hydrophilic
compounds would like to be in those stationary phases or like that they would
like to stay away from them and so what you find is that as you have an odor
pulse going through a column with these kind of materials you get very beautiful temporal
profiles in the stationary phases at different points in time that depend in
very complex ways on these different sorption properties and other properties
of the molecules that impose an additional time dynamics on the stimulus.
And particularly when you've got a complex mixture, like a coffee,
then imagine you've got, what you've really got is
you've got 400 of these pulses going through simultaneously and you
get a beautiful sort of spectrum you can think of it like a kind of
prism system where you take in a complex move and it
splits it in some way right exactly in a spatial temporal
code and it imposes this upon the receptor
population and what you find when you look at the receptor sensor responses
when you distribute them across this type of physical arrangement is that you
get absolutely beautiful and stunning very complex extemporal information that's
exquisitely stimulus dependent, right?
Because it depends upon exactly where that molecule was at a particular point
in time. With how much did this improve?
So basically you translate this whole idea of zoning of the receptor sheet and
a mucus layer that again controls the binding dynamics of your ligands.
So you translate that to a technology also basically to test its impact on processing,
but what was really the improvement in classification that you now observed?
Yeah, I mean, what we found was very interesting. So if you had,
it's kind of an interesting result.
We found in all cases, this obviously improved discrimination.
Purely because you've got three mechanisms in total, and the correct comparison
to do is to compare it to just when you have the first two mechanisms without
this nice, funky, spatiotemporal delivery concept.
So you do the direct comparison of just saying, well, when you deliver the molecules
directly onto the senses without any spatiotemporal sorting of any type,
how does your information compare?
And so again with Manuel, who very cleverly extended the Fisher information
formalism to include time so that we can actually now see not only when different
stimulus dependent features and the noise properties are telling you a particular point in time,
you can actually accumulate that information over time to tell you within particular
time windows how much information you've collected about the stimulus and how
accurate your reconstruction is of it.
You can actually use this formulism then to compare in a very precise way,
in a very quantitative way,
how you would have fared if you tried to discriminate just from the first two
mechanisms compared to when you also use this additional sorting.
And what you find, in fact, there's a theorem in our paper that shows that there's
no situation ever, there's no possibility that you can ever exceed the three
mechanisms with the first two.
So there's no situation in which you can ever do better than the three,
which is kind of common sense, right?
Because if you're adding more stimulus-dependent information.
So you can never do worse. So that's already good news.
It's more like a lower bound. It's like a lower bound. So what's your upper bound?
So the other very interesting point is how, so then the other question is how
much better you do totally depends on the complexity of the task.
So if you set your olfactory system, then a very simple task,
which is maybe, I don't know, imagine a very, very simple task of you just have
a single component and you just at one point in time, you push that through
your microchannel, you get a set of receptor responses and you discriminate that as one odor.
Then you separately do that for a different odor. This is a very simple discrimination
task with two classes. So you've got a 50% chance.
And even the array by itself, depending upon the chemicals you use,
will trivially separate this, then you'll find that by adding that space to
the temporal thing, you're not really going to improve it, right?
Because you already almost certainly probably had 100% success anyway, right?
So what we found in our study was that when you push it more and more,
then the effect of this third mechanism makes more and more of a difference.
So for instance, if you give it a very complex task, which you could imagine
one of the hardest tasks might be put through the system of,
a coffee mixture with 500 compounds simultaneously.
And the task of the chemical receptor array and the subsequent readout is to
detect the presence or not the presence of one of those compounds in the 500, right?
Right. When there's a massive overlapping spectra of all of these.
This is a phenomenally difficult problem because you've got all of these receptors,
responses are convolved over 500 components in a relatively short period of
time of responses over just a few seconds and.
Somehow over this relatively small sensor population you may I think in our
micro I think we only had 30 sensors right now.
We only have 30 channels compared to 10 million in there in the in the biological
system Out of all of this you've got to somehow Detect one of these components.
This is a very challenging problem and what
you find when you apply this
separation at the front end with this third mechanism is that
you find that this third mechanism becomes
more and more important so we found
that at least one or two orders of magnitude improvement in
the classification and the error reduction in
the error was two orders of magnitude and we
i think we only tested at a couple of
either an easy and a complex task and i
think there's a lot of interesting ideas
to push that to harder and harder tasks for instance attentional tasks
where maybe the target odor is changing over time things like this okay but
now compared compared to let's say a standard sensor like like a like a cmos
based sensor so what what's your performance if i if i go classify of our coffee
with a standard off-the-shelf sensor,
and now this one with its artificial mucus layer.
Yeah, I mean, you know, we've got in our first paper just to show that when
you do it with and without, you get an improved performance.
And we have this other theoretical study to show that in certain situations
you do about two orders of magnitude better in terms of the error.
And that's the sort of level of what we've quantified so far.
So in terms of practical… So that still has to happen. But how do you fabricate this mucus?
Uh yeah so in fact it's quite a challenge
because a lot of these stationary phase materials are quite poisonous so
they had a big challenge in the lab of how to get these stably inside a microchannel
array um the warwick people made a very beautiful there effectively that used
a more advanced micro lithography method of a 3d printer effectively to grow
grow a microchannel as you can
imagine we have a great interest in say also growing
a rat nose structure so maybe of course maybe there are
well it's almost certain actually that there are uh very
beautiful aspects of the elaborate structure of
the nasal turbinates of a rat nose which
give even more tricks in there for for instance how it controls turbulence and
laminar flow and all sorts of other stuff that i haven't even talked about right
that can be stuff in the future perhaps but right now Now you just basically
grow in a 3D printer a very long microchannel.
And you introduce this material under high pressure to get it to uniformly pass
along this microchannel to deposit as a thin layer.
Have you considered also to use mucus from biological systems?
We haven't yet, but of course this is a fascinating idea, right?
Because they already have things like OBPs in there. Exactly right.
And so on, and other things, and odor-degrading enzymes. times but
of course they're also not necessarily particularly stable right so these
are other things to take into account but yeah it's
a fascinating area of all sorts of different materials that you can potentially
put in there probably anything you can use pretty much any chemical material
with a liquid aspect will have some sort of selective properties for groups
of compounds and they will change the sort of array receptor properties in different
ways right so there's all sorts of possibilities to have sort of parallel versions
of these noses right It doesn't have to be one nose.
You can have arrays of these noses with, I don't know, peanut butter coatings,
snot coatings, various different stationary phases from GC columns,
God knows what, right? And they're all giving you different selective answers.
You can think of it as different windows on the chemical world. Right, exactly.
Sort of seeing a unique little portion of a chemical world.
Right. But now, do you believe, I mean, you foresee also the robots of the future sure.
Largely will be equipped with with artificial noses yeah
it's a good question there's been a lot
of efforts actually i mean not just on robots so for at
least 20 years people have talked about things like will there be smell sensors
on your mobile phone and uh in fact
at some point there was a lot of talk about that particularly in japan people were
very keen to know you know how is my uh breath smelling
today you know maybe there's a need for having
these kind of very cheap sensors in a in a in a mobile phone um there's as you
know very well there's there's uh all sorts of domains for this in terms of
robot robots for environmental sensing and so on and um,
uh there are all sorts of aspects in terms of human
emotion that are obviously important in in
odors so if you're going to ever going to
ever make a robot that understands humans probably
needs to understand odor as well because there's some sort of estimate of something
like 95 or 99 percent of our um uh olfactory uh consciousness is subconscious
so it has all of this sort of background biasing on our state of mind at a particular point in time,
probably a robot would need to know about this right if it needed to understand humans.
Um and there's all sorts of applications in flavor
industries and so on so there are
clearly a lot of applications but i i think
there it's always going to be a niche okay so to to finish up i mean so you're
you're you have the one foot in the biology of affection the other one in in
technology of affection and also you use the technology to understand the biology
and the biology to advance the technology so it's a very unique position you're in so in
the study of the brain and in our in our attempts to synthesize
brains what should be tim's law that we have
to follow uh well
it may be a bit obvious but uh to construct is
to prove so i think um looking at
the biology uh and making making sense of
those principles by having them in reality in
front of you operating in concrete ways
uh is is
crucial because uh i know too many models of too many different phenomena that
uh you never know how robust they are going to be in a realistic setting or
whatever so that would always be my my advice okay and the another thing So five years from now,
I'm going to come to Leicester to visit you in your lab,
and I'm going to confront you with the hypothesis you're going to generate today.
So the question is really, what's the one hypothesis you really want to commit yourself to today?
And five years from now, I can come and see if you actually were able to validate
it and what the outcome was.
Yeah i i think an under an underrepresented aspect uh for the future is to prove that um,
attentional processing in olfaction is
very important so at the moment we don't really
know much about attentional processing we don't we don't really know about it
much in biology and we know even less about it in say machines or deploying
this and so the prediction i would I would hope that in five years I could take
you to my lab and prove to you,
demonstrate that I can make olfactory machines that attend to different parts
of this beautifully complex molecular world that we have.
And that depending upon operational demands at particular points in time,
it would be able to give you, say, unique reports or windows on this very complex universe.
And I would like to be able to prove to you that it's not just making it up,
but it's looking at different facets of a very complex signal.
And that hopefully this could be done in a responsive sort of way to a particular task.
And that doesn't really exist at the moment. Exactly. All right,
Tim Pearce, thank you very much for this conversation. Thanks a lot.
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