 OK, good afternoon, everyone, and it's my great pleasure to open our number seven provost lecture series, and also our first lecture for FY 2023. So today, we are going to celebrate Izumi's well-deserved promotion to tenure, and so getting tenure, usually you have to demonstrate significant scholarship and mentoring and also service, you know, many different components. And so today, Professor Simone Pigalotti is going to chair the session. For those of you who are here the first time, I just want to just highlight the provost lecture series that we started last November, and so it has been going strong. As you can see, these are all the six past provost lecture series professors. And so also the provost lecture series, so we took a lot of effort and time and energy from people from different divisions within OIST, so we would like to thank them, especially people from the provost office and also people from the CPR. And so now today, so this is Izumi's talk. I also want to alert you that we have about 10 lectures tentatively planned for this fiscal year, so we're still trying to sort out schedule because summer is going to be somewhat challenging. Many people are going to travel, so we have Christine, Yabinchi, both of them got awards, so we also want to celebrate that. Professor Sasi, who was recently promoted to a full professor. And after this May and also later September's BOG meetings, so we're also expecting several other faculty members to be promoted to this tenure. So the list is growing, but currently we're projecting about 10 lectures within this fiscal year. And so with that, I will hand the microphone over to Simone. OK. So she has an interesting life history. She was born in Tokyo, but then she spent her young years in Malaysia. And then she moved to London for his university studies, where she started her academic career at University College London as a PhD student. Then she moved to the Max Planck Institute for Medical Research in Heidelberg as a postdoc. And then back to London with a career development fellowship at the Kriegs Institute. And then she came here twice, where she did her tenure track as assistant professor and recently was promoted as assistant professor, and that's why we are here today. So let's talk about her work at OIS. So she found the Sensory and Behavioral Neuroscience Unit, where she focuses on studying the olfactory system experimentally using the mouse as a model system. So this is a pretty complex system in biology because different odors from the environment have to be encoded in the brain via a complex pathway. And this also depends also on, for example, the context and environmental state. So I'm sure she will tell us all about this in her lecture. She is a very accomplished experimentalist. When she came here, she built quite sophisticated lab to study the systems, combining a bunch of state-of-the-art experimental techniques, ranging from electrophysiology to optogenetics and more. So I actually liked reading when I was preparing this introduction, this very nice interview to Izumi that was prepared by the prestigious journal Elife in occasion of her first paper as PI. And here Izumi talks about her research, but also about the challenges of being a junior PI in her typical humble style. And because Izumi is so humble, I think sometimes it's under-recognized how much she has been doing in these years for OIST and our community. And I think she's really the heart and soul of our university. And I'd like to mention a few examples of this. One maybe might sound a little bureaucratic, but it's in terms of her incredible dedication to academic service. So she did really a lot during her tenure track. She was a member of the faculty council. She was very active member of the strategic task force to discuss future strategy of OIST. In particular, she was really one of the proponents of this idea of research tags and so on. She was in charge of a target of opportunity faculty search, which was a pretty delicate and sensible process. She was responsible of setting up the neuroscience curriculum. You know that neuroscience is one of the largest group at OIST and making sure that everybody's on the same page for something like this is really a non-trivial task. She was part of the presidential search that gave us our new next president. And she gave countless constructive contributions to a large number of issues. I mean, this is really the ones I remember, so I'm sure there are many more. So I think this is pretty extraordinary for a person during her tenure track. And I think he witnessed the fact that she's really dedicated not only to science, but also to the benefit of our community. Beside the academic services, she did also many more things behind the scene. I like to mention one project, which is the campus wildlife garden. So she was concerned by the fact that we are cutting a lot of trees and started together with the Juanita Choa project to build a small plot of land close to the gardens with the saplings of local species and try to grow up trees there. And in typical Izumi style, this was not only a matter of obtaining a small ground and directing and leading this project, but also working in first person and just not being worried of doing their work here. And beside this, she also has a lot of other talents that we learned to appreciate over the year. For example, she's an extremely skilled photographer. You might have noticed that on the top right of the page, there's often an impressive picture often taken for campus. And more often than not, these photos are from Izumi. These are almost all taken on campus by Izumi, except the queen that it's not on campus. But since it's her birth, I thought it was nice to show it. And she has a bunch of other hobbies that we learned about during the years. Many of them we have witnessed. There's one that I heard about, but I never witnessed. I hope this one day she will show us. She's apparently very good at writing the monocycle. And because of her dedication, I received a lot of thoughts from previous executives at OIST that I'm going to read. So Mary Collin's previous provost wrote that Izumi is a great neuroscientist, seamstress, chef, photographer, and plant's woman. So everybody's impressed by her multiple skills. I got also one from Anil Calder, my previous vice president for communication, who wrote, beyond her excellence in scientific research, Izumi has qualities that will perhaps be more important for her future career and indeed science itself. She loves a lot, she's kind, she notices things around her, whether a reflection in a droplet of water or the tiniest insect on the tip of a leaf, or the emotions of others. She's a musician, she's an effortlessly bilingual, her welcoming personality will draw people to OIST and indeed young people to science. She's the scientist of the future. And this is actually a movie. So this is what I wanted to say. Congratulations, Izumi, on your achievement, and I'll pass your button for your presentation. Please. Hi, everyone. Thanks very much, all of you, for coming. It's been really a pleasure working at OIST, building my career with really warm support from everybody. It's hard to follow that kind of introduction, but I'll try my best. So today I'm going to talk about the sense of smell, and this is a modality that is often said to be an undervalued or underappreciated modality, because when you think about thriving in this modern society, losing the sense of smell is really a lot less detrimental than losing the sense of the ability to see or the ability to hear things. But if you think about how much time we spend, some of us are thinking about, or obsessing over some good sources of odors, like fragrances and the flowers, you know, or flavors, the sense of smell really still is very much part of our lives. It really is an important part of an enrichment without which our lives would be quite a sad one. So, and now Simone has already shown this picture, but I wanted to introduce some of our local delights, which is actually Madagascar, I mean, if you're after some local sources of good odour, I would recommend you to stroll next month to Akama Sports Complex, which is just around the corner, where Madagascar, I mean, will be blooming. And, you know, it's just a source of very fresh, floral, distinct scent, will be a memorable experience for you. Now, I wanted to mention jasmine because it would be a nice way to introduce what it is that we are smelling. So this is a study by Ken Sakamori and Yoshihara's work from 1994, where they managed to take about 20, you know, different kinds of molecules that make up the sense of, you know, this jasmine scent. And one thing that, you know, is striking already from this analysis is that one, even for one scent, that makes up jasmine, there are many molecules that are involved. And indeed, you know, it's really difficult to realistically count just how many odours we are able to detect and discriminate between, right? So many, many volatile small compounds we are able to detect, and that's a source of information, right? So that's one challenge that the olfactory system of the brain faces. Another aspect that's interesting, looking at this, is that of all of these volatile compounds, somehow we can put a meaning to a selection of these. So, you know, for example, in this case, we somehow are able to detect this combination of 20 or so odd compounds and identify it as maybe a jasmine scent, right? So there is detection and also an analysis component to our sense of smell. So this is really fascinating. And this sort of mystery behind the chemical senses is one motivation for me to be studying this scent. But also, as a neuroscientist, I find this quite an important system because for, you know, a model organism that has been used widely in neuroscience, like this, the mouse is a nocturnal mouse. So the sense of smell really is a great driver for their behavior. So this makes it sort of an ideal system to study the neurons and how they function in their interactions also with each other, and especially in the behavioral context. So these are the motivations for why I choose to study the mouse olfactory system. So today, in this talk, I would like to briefly share with you how generally, at the superficial level, the olfactory system of the mouse is organized so that when I come to sharing why, I came to be interested in this flexible smelling. Perhaps I can communicate it better. And then later on, I'd like to share with you some of little discoveries. My journey so far, I'm in this quest. So let's start at the beginning. So the sense of smell really starts in the nose, which is not really a surprise. We know that. But so it starts at this structure called the nasal epithelium, which is located at the back of the nose in the nasal cavity. It's convoluted structure. Now, so if you were to then cut across this slightly bony structure, what you see is this convoluted structure. And inside, there is this lumen. And this is the airway, right? So the air, as animals breathe, passes through. And now, and this sort of cavity is lined with a sheet of cells. And if you were to zoom in in this illustration from Linda Buck and Richard Axel's paper, and you can see there is a mixture of neurons. And in particular, the neurons of interest today, in particular, are these what we call bipolar neurons. So neurons with two poles. And these are the ones that are doing the hard work of converting molecules, detection of molecules, into the electrical activities, which are the languages that the brain can understand. So these neurons, sensory neurons, extend this knob into this airway. They have this hair-like structures called cilia. That's impregnated in this sticky fluid that you have in your nose, mucus, which allows molecules to easily diffuse into the solution and ultimately into the membrane, where the sensors or the receptors are located. And it was Linda Buck and Richard Axel's work in 1991 that led to the discovery of these receptors for which they won a Nobel Prize. And their insight was to identify this seven transmembrane protein, and that comprises really a large family. So this is key because we need somehow sensors to be able to detect and discriminate so many different molecules. So they were looking for, well, they didn't know what they were looking for. They knew that they were looking for something like this, but they were able to identify something that comprises a large family. And all of these black dots indicate the amino acid residues that can be highly viable. And because of this pioneering work that led to the identification now, we can look for something like this with similar sequences in our human genome, for example, and identify, for example, there are about 400 functional olfactory receptor genes present in our genome, right? So that's quite a lot, except that more superior animals like mice have more than 1,000 different kind of genes to use. So that gives it better resolution for them to be dealing with the chemical stimuli. Okay, so one thing that we take from this is that, yes, we have a lot of different types of receptors, but in addition, if one type of receptor was dedicated only to one type of molecule, then we quickly run into trouble, right? Because if the adjustment send already has 20, then it's not quite possible to represent so many different molecules. And the trick that the system uses is to instead use a combinatorial representation. So instead of one molecule representing one odorant, it's thought that these receptors are tuned to one particular feature of a molecule. So this could be like a functional group or so. And by using a combination of the receptors to represent an odorant, you can imagine then the system is able to represent far more types of molecules than is possible with a number of receptor types. So because of these pioneering works, we understand why it is that, you know, we can detect and discriminate between so many molecules. Now, but as I have said already, the detection part is just a start and there's a whole lot that goes on, which is to do with analysis. And this is something that occupies our mind quite a lot. And in particular, we study this structure called the olfactory bulb. So this is the first relay station or the smell center, if you'd like, of the brain that is getting input directly from those olfactory sensory neurons in the nose. So I'd like to talk a little bit about organization here because I'll be talking a lot about this area. So how is the input to this region organized? So in this image, we can see from Peter Monber's and others work, you can see the epithelium and in connection with the olfactory bulb. And now what they have done is to stain for sensory neurons that they're expressing a particular type of receptor called P2 receptor. And what you can see is that these neurons, right, are kind of scattered the same randomly in the epithelium, but as their input or output projects towards the olfactory bulb, they somehow come together, they converge and terminate in this really tiny, discrete spot. And this is called Gomerula. So you can imagine then if you have more than 1,000 different types, you'll have a surface map of these olfactory receptors. So that's quite a nice spatial transformation, incredible. Now let's go a little bit deeper into this structure if you were to then make a cut here and stain for where the cells are located. What it reveals is a very beautiful three-layer structure and this outer layer is where the olfactory sensory neuron terminates. And you can already see this sort of circular, if the lighting allows, I think I hope you can see it, these circular structures, these are Gomerula and as I have said, this is where the olfactory sensory neurons express in the common receptor type converge. And this is where the olfactory information is then passed on to the neurons of the brain. And I have drawn a cartoon of this here, but in reality they look a lot more beautiful than that. So this type of cell is called mitre cell because the cell body looks like mitre, you know the head gear worn by bishops. And then you can see this single thick process that comes out and a tuft and this is the one that goes into this little circle. So this is the input structure and there are some sideways structures and then that's the output. So that can extend in millimeters. This is the cable that's transmitting the output into other parts of the brain. So there is input and then there's output. So a reasonable question one can ask is then, so is the output the same as the input? Well, that would be kind of a useless thing but it's a reasonable question to ask. And I think it's an important question to ask because understanding how the signal is transformed here is like, you know, one way to understand how the brain analyzes chemical information. So this is the kind of question that I started to get interested in as a postdoc in Andrea Schaefer's lab. And to get to this, I'm conducting a lot of electrical recordings using a technique called whole cell patch clamp recordings in vivo. So the general technique of this patch clamp recording was pioneered by scientists Erwin Neher and Bert Sackmann, also for which they won Nobel Prize. But in this whole cell mode, what you get is, you know, so you reach, so you have this glass capillary that makes this high resistance seal. So just the membrane sticks to this glass and then you rupture the membrane and you can gain an electrical access to the inside of the cell. So what this allows you to do is to then measure the voltage across the membrane. And so I try to see what's going on, you know, what kind of electrical signal that the cell is using. So when you make a successful recording like this, in this case an anesthetized mouse, what you get is something like this. So in vivo, there's kind of an ongoing fluctuation in the membrane potential. We call upward events excitatory events and downward ones inhibitory events. And if the excitatory input is large enough, at one point it crosses the threshold and you can see, you know, it leads to generation of this very characteristic large short events called action potentials. So these are the ones that are carried by this output structure that goes out of the brain. Now I'm showing this signal next to the chest extension signal. So this is the breathing signal, right? Because this slow oscillation that's ongoing happens in sync with, you know, the animal's breathing. And this happens really in the absence of odors as well. So why should there be ongoing slow oscillations when there's no odor? What it turns out that the bipolar neuron, the sensory neuron that I talked about already, they're also mechanosensing. So when there's no odor, they're kind of taking information about airflow and sending subtle, you know, rhythmic information to the brain, to the olfactory valve. Which is why if you record from these neurons you get what we call sniff locking, so rhythmic activity that oscillates in sync with breathing. And typically, if you study a given neuron, then the peak happens consistently in one part of the phase. And it happened that every time I record it from these large, deep types of mitral cells, the peak happened to be kind of at the end of the sniff cycle. And curiously, if I record it from, you know, this smaller and superficially located neuron, suddenly what we saw was a peak that happened earlier in the sniff cycle. So this is curious because, you know, there's only one rhythm that's related to breathing. But somehow in the output, there is a bit of a phase shift, right? And it happened that these superficially located tufted cells are oscillating in sync with the input from the nodes. So there is no phase shift for those neurons and it's these deep ones that are phase shifted. So they're locking antiphasically with respect to the input. Now, why am I mentioning this? So this baseline phase shift leads to kind of an interesting consequence on how these neurons encode others. And it's because, you know, so if you were to look at these neurons that oscillates in sync with the breathing when there is an excitatory input coming in when there is a favorite odor, then what happens is that these neurons generates more and more of these action potentials, the output, but without appreciable change in the timing. So these neurons tend to use a firing rate to encode the presence of odors, whereas these deep neurons, because of this baseline shift, when, you know, to encode odors, there is some kind of rate code as well. But the major thing that happens with this was a change in the timing of action potential generation. So, you know, so there's some kind of a divergence in the way that these neurons encode odors, right? So, and I mentioned this just, you know, the way an example of some kind of transformation going on. And it's important with this phase shift, for example, we think that it's representing different kind of information that's integrated by these neurons. So clearly, the output is a little bit different from the input. So why would we expect, or why should the output be different from the input in this structure? Well, we know, well, there are many reasons, but one big contributor is the presence of these, what we call inhibitory interneurons. So these are neurons that don't project out of the structure, but they mainly connect with neurons inside this structure. And these types are inhibitory because their input to these neurons drives membrane potential away from this threshold for action potential. And it's important because in this structure, particularly in the factory above, 80% or so of the neurons are this inhibitory interneuron type. So there are many, quite a large portion of the circuits, these inhibitory ones. And we think this is really important for the function of the factory above, in the same way as subtraction and division are very important operations for any computations that you might carry out. So we think that they're important, but you might also notice that these are contacting the input structure and these deeper types of interneurons are contacting the lower part, so the side dendrites or lateral dendrites of the mitral cells. So it suggests to us that perhaps they have different kind of functions when it comes to signal transformation. So this is the kind of thing that I asked as a postdoc and we had just the right tool to prove whether these populations of interneurons are contributing differently. And that technique we used was optogenetics. So this is a technique in particular where we can shine light and silence the neurons that express these options. So in other words, we can shine light and remove these inhibitory neurons from the equation. And then we can target this optogenetic silencing to these deeper classes of neurons versus those that are located more superficially. And what we found was that for the transformation that I talked about, the mitral cell phase shift, turns out that these superficial ones that are inhibiting where the input comes in, this is important. And actually for this paper, I looked at all sorts of phenomena, all the responses, et cetera. And for all of the things that I looked at, these were providing really strong influence on the output, really potent effects on the firing rates. And if you think about it, it's quite important because these are the ones that can potentially change the activity patterns that are going out of the olfactory bulb, right? Whereas these deeper ones were kind of, it contributed in a really subtle way if you really looked closely. So, but barely detectable. Now this was really a surprise for us. And why was this surprising for us? Is because if you look at this section again, these deep ones are really occupying this huge volume in this structure. And it's estimated that there are 600,000 to one million of them, right? So it's quite a lot of investment for barely detectable signal transformation. So this is where this flexible smelling idea comes from. I think this is important for understanding how these are contributing to signal transformation. And this is something that I would like to explain in the next couple of slides. So why do I think that flexible smelling is important for understanding the signal transformation fully? Well, the sensory systems in the brain are not existing in isolation. It's part of what we call sensory motor transformation, so as signal, sensory signals are processed more and more, it starts to kind of take characters more like a decision variable. And ultimately it starts to look more like behavior output or kind of resembling commands that you might send to your muscles. So there's some kind of transformation. So, and one thing that you might know from your own experience is that these things are quite dynamic. You don't always respond in the same way to a given order, and you don't evaluate the orders in the same way all the time. So it's quite dynamic. And just to give you an example, different ways of enjoying red wine, a glass of red wine, so you can enjoy it against something like cheese. It's quite a nice way to enjoy red wine, I'm sure. But, you know, we have some idea, right? A categorical idea about what red wines smell like, right? So that's one way in which we smell red wines. Another way we can enjoy red wine is to really compare the subtle, you know, variants, you know, for example, like paying attention to some fruitiness or, you know, some spiciness in aromas emitted by the red wines. So in other words, we do different things even for the same olfactory stimuli. And this actually poses a challenge for the system, why? It's because the optimal representation really depends then on the behavioral context. So if you want to categorize something, you know, if you want, you know, red wines, all of the different wines to kind of just smell like red wines, then you might want to represent all these patterns more similarly to each other. Whereas when you're discriminating, right, you really want to accentuate this sort of pineapple odor, whatever, and then as a sensory system, you might want to represent these patterns more separately. So, you know, there isn't one solution fits all, really, that suits all behavioral context. So how does the olfactory system in the brain deal with this? Well, we don't know exactly yet, but one really attractive idea is that there are all sorts of other connections that can come back to the sensory system. So, you know, the behavioral context can influence how it is that the sensory system is processing the sensory stimulus. So, one way is to have a feedback, right, that provides moment to moment information about the behavioral context. And, you know, what's very unique about this structure, olfactory bulb is that even though it's located very peripherally, it is a recipient of quite a lot of such context, you know, or feedback and other neuromodulatory inputs. So, this could be key, and we can even visualize how these feedback fibers look like. So, this is a study by Lyssen-Boys and others from Jeff Isaacson's lab where they injected this labeling agent. So, this is an associated virus so that neurons in this region of the brain can express fluorescent protein. And once these neurons express fluorescent protein, we can then follow their feedback fiber back into the olfactory bulb. And then if you were to then make a section here in the middle and to look where these fibers are and you can see this middle part where the granule cells are located really light up. So, somehow these deep, you know, interneurons, granule cells, you know, it's a major target of these feedback fibers. So, potentially, behavioral context is key to understanding how these are contributing to the signal transformation in olfactory bulb. So, this is something that we really wanted to get into. So, then the first question we really wanted to ask was, you know, does it matter for, you know, does a moment to moment change in the behavioral context matter for olfactory processing in olfactory bulb? And, you know, up to that point, we knew that if you train a mouse on a task for a long time, then there are long-term changes, you know, so there's some kind of a memory related to the task. So, you know, long-term things in olfactory bulb changed, but we didn't know whether moment to moment the way that olfactory bulb processes information can be subject to behavioral context. So, this is something that we wanted to understand. And so, we used two tasks, the discrimination and generalization, that the two tasks are already introduced. And how do we do this in a laboratory? So, we generate olfactory stimuli. So, we have this quite messy setup. It's kind of grown organically, so there are a lot of wires, but we are quite confident that we're generating quite good quality stimuli. And once we have generated olfactory stimuli, we can use them to train mice on olfactory tasks and also study and perturb a neural activity while mice are performing the olfactory task. So, that's the idea. So, what kind of olfactory stimuli do we generate for fine discrimination task? Well, we can mix odors. So, we generated, for this particular study, we generated a binary mixture. So, a mixture is containing two odorants. So, ether butyrate, that smells like pineapple. And eugenol, that smells like clove. We mix it in different ratios. So, one mixture had more of ether butyrate, the second mixture had more of eugenol. And the task for the mice is then discriminate between these perceptually similar mixtures and associate this mixture with more ether butyrate with a reward and the second mixture with more eugenol and expect no reward. So, that's the fine discrimination task that we implemented. And the second task, so this is our implementation of the generalization. So, either of the isobutyrate eugenol mixtures were the rewarded odors. And then, the task of the mice was to discriminate against something entirely different, a mixture of methyl salicylate that smells like wintergreen, a methyl tiglet that smells like ethereal rum and expect no reward. So, these are the two tasks. And the point is then to train mice to switch quite rapidly between this fine discrimination and the generalization task. And once we achieve that, what we can then ask is whether the representation of this, the same odor, you know, depends on the behavioral context. And this we could do using a two photon microscope. So, you know, we could then observe the activity of neurons in olfactory bulb as mice behaved because we had an optical window that allowed activities to be measured. So, how did the result look like? So, as I said, you know, odors are represented as, maybe as a vector, right? So, by a number of neurons. So, we can reduce the dimensionality by using conventional technique like principal component analysis. So, you know, you can think of this as activity pattern developing over time that's derived from activity measured in olfactory bulb. And what you see is, you know, it's like you to pay attention to these three colored trajectories. So, these are all corresponding to responses to the same odor, but in different contexts. And what you can see is that responses when the mice are doing this generalization task is located, you know, in a different location than when mice are performing the fine discrimination task. In other words, context does change the way that response, you know, neurons in olfactory bulb respond to odors. So, and why should there be, you know, so where does this difference come from? So, we analyzed this, and what we found, and so, you know, the analysis we did was to compare the amplitude of individual neurons responding to that mixture one in the generalization task and fine discrimination task. So, if there is no change, then the responses lie along this diagonal. And I looked at, so, we looked at responses for cells that were selective for the second odor. So, really particularly tuned for the unrewarded odor. And we saw there was really no difference with the behavioral context. Now, when we looked at the cells selective for the first mixture, rewarded mixture, then what we saw was like, you know, generally speaking, there was an enhancement in responses when mice were doing this difficult discrimination. So, there is some kind of an odor selective modulation by change, you know, with behavioral task. So, what does this mean? It means that the task relevant features were somehow enhanced when mice were doing this difficult task. And we confirm with the decoder analysis, for example, that this amounted to, you know, making two similar representations a little bit more separable from each other. So, an attractive idea then, you know, something that we really want to test is whether this feedback is contributing, right, to a task selective modulation that depends on the behavioral context and whether this is coming via these deep-located interneurons. This is really the next big step that we want to take. But we have a little bit of a challenge. And the challenge is, as I have mentioned already, the output of the factory valve is carried by two types of neurons, right? So, the superficial small ones and the mitral cells. And I have already said that the way that they transform signals is a little bit different. It also turns out that the way that they're modulated by behavioral context is also different. And this poses a challenge when studying this because there are also, you know, a partner with different populations of granular cells. So, you know, these deep interneurons that are located here, right? Some of them contact only the superficial ones. Some of them contact only the deep ones. But they're all intermixed together. So, right now, it's really, there's no easy way to distinguish if you look at neurons here, which ones are contacting this type and which ones are contacting the deep ones. So, it's all, you know, intricate, all mixed together. So, we need really a better tool. And this is something that we spent some time developing with Angelica Koldeva, who was actually an intern. But she had studied statistics before she came to OIST and she's now a PhD student with Simone. And what she could do was look at, you know, quite a large data set that was publicly available. So, single cell RNA-seq data. So, this contains the gene expression patterns from single neurons, from many, many neurons from the olfactory bulb. And what she could then find was, you know, what is different about these neurons, molecularly speaking, from these? And once we could identify a mitral selective marker, what you can do is then to generate a transgenic mouse strain, mouse strain. So, ultimately, we can then label and manipulate these ones without manipulating these ones. We didn't have this tool before. And this is what we did in collaboration with Satoru Takahashi's laboratory at Tsukuba University. A long story short, we're now able to selectively label these deep populations, whereas before, you know, we couldn't differentiate these two types. And what this allows us to do, really, is to, for example, look at, you know, these deep granules cells that only contact these deep lying, right, mitral cells, and really study what's special about them, especially when the behavioral context changes. So, this is where really we want to go. We want to understand, you know, how it is that the behavioral context is getting into the olfactory circuitry and how it is that they're contributing to flexible decision-making, flexible smelling. So, to summarize, I have shared with you today that the olfactory system has, you know, quite a lot of the specializations that allows them to deal with a large variety of volatile chemicals. And this information is analyzed in multiple stages, involving, you know, quite a lot of brain regions. And, but really to understand mechanistically, you know, to understand how this analysis is implemented in real neurons and their interactions, inhibitory neurons are quite key. But, you know, to really reveal their potential, we believe that the behavioral context is quite important. And I hope to make some more advances and maybe next time I can share a little bit more. And with this, I'd like to thank all of my unit members for their hard work and inspirations. I would also like to thank all of the research support division for their dedicated work, you know, animal resource section, imaging section, engineering section and the basic love support for really enabling research. I'd like to thank my former supervisor, a postdoc supervisor, Andres Schaefer and his colleagues at the Francis Crick Institute and the collaborators. And of course, I'd like to thank OIST and all of your support for keeping me sane. So, thank you.