and I believe we are officially good to go. Hello everybody and welcome to another session of our Sussex Vision Seminar Series, Always Within the Worldwide Neuroinitiative. I'm George Cafetsis, a former master's student in Tomas Oilers Lab and relatively speaking, newly arrived PhD student with Tom Badden. As your host for today, I would like to once again begin by thanking Tim Vogels and Panos Bozellos for putting forward this very initiative towards a greener and much more accessible seminar world. And of course, having said that, allow me to get back to the reason we all gathered here for today and introduce our guest from University of Sussex, Dr. Silvia Schroeder. Following a bachelor degree in cognitive sciences from University of Osnabruk, she then moved for her master's and PhD in Neural Systems and Biology to Etecha in Zurich where she worked with Kivan Martin on functionally characterizing neural responses in the primary visual cortex of cats. In 2014, she relocated to UCL in London where she joined the labs of Matteo Carandini and Kenneth Harris. And there, successfully, successfully funded by Marie Curie and the BBSRC grants, she investigated the effect of behavioral modulation on visual processing by recording either optically with two photomicroscopy or electrically with the famous neuro pixels, the neural activity at different visual areas. At the beginning of this very year, she moved to Sussex where she is currently setting up her own lab. And without any further ado from my side, it is with great pleasure that I'm leaving the stage for her, Dr. Schroeder, for a talk entitled Arousal Modulates Retinal Activity. This stage is all yours. Thank you very much, George. And thanks also for the invitation to talk here. It's really a big honor to share my results here and to this big audience. And I'm also hoping to get lots of feedback and comments if people have those. So let me share my screen. Yes, I hope you can see that. Yeah, good to go. Good to go. So yeah, today I will talk about how responses in the early visual system, including the output of the retina, are modulated by behavioral or the internal state of the animal. And this is actually what I mean by the term arousal. So it's nothing else, but kind of an alerted state or an also a behaviorally active state. And at the center of my talk will be these green fibers that you can see here. These are actually retinal axons coming from the retina to the rest of the brain. Here, for example, the LGN, I believe. But before I go into the results of my studies, I wanna provide you with some context. And one of the bigger questions that these results and these findings address is, do we see the world differently when we experience different internal states? So if you look, for example, at these two guys, I hope you can believe me that they're in very different states. So he seems to be quite amazed, surprised by what he is seeing, whatever he is seeing, whereas he seems to be relatively bored. And even if they're looking at the very same thing, the question is, do they see it differently? And what do I mean by this? I do not mean that emotions or internal states can lead to different perceptions or make you attend to different things. That's, of course, also true. But what I mean is, does the eye or other early visual brain areas send different signals to the brain in these two different states? So this idea is not very new, that the idea that internal states influence neural responses, even in the early visual system. So about more than 10 years ago now, the following was found in the visual neurons. If you have a rodent here on a running wheel, you can see when it's running, like here, also usually the pupil dilates at the same time. And here you can see the activity of multiple units and you can see that this increases when the animal starts running and also when the pupil dilates, okay? So these findings were originally made in an area called the primary visual cortex. And here just as a reminder, I'm showing you the early visual system. You have here the eye with a retina which projects directly to the thalamus, to the LGN, also to the superior click loss and then the thalamus projects to the visual cortex. So the initial findings that visual responses are modulated by internal state or by local motion were seen in primary visual cortex. Later on, in the last 10 years, people also saw that visual neurons in the superior click loss are modulated by internal states and arousal as well as neurons in the LGN. So areas where the retina directly projects to. So having seen all this, we were asking, is this modulation already happening in the retina? Okay, so this is what this talk is mainly about. Now the first problem is actually measuring retinal activity in an awake animal, in an awake mouse. So we came up with a trick. Instead of recording the activity in the eye from the retinal ganglion cells directly, which is difficult because the eyes are moving around, we instead record the activity of the axon endings in the rest of the brain, in this case, in the superior click loss. So we can basically image this area and see the activity of those axons. You may wonder whether it is a good choice to go to a superior click loss. And that's why I'm showing you this diagram here. All you have to take from this is that there are lots of cell types in the retina shown down here. And the retina projects a lot of areas in the brain. But here, this one shows you the superior click loss, SC. And you can see that most of the cell types and actually also most of the retinal ganglion cells, I think the estimation is around 80 to 90% of retinal ganglion cells are projecting to the superior click loss. So we can actually see a very good portion of that the retinal output in the superior click loss. Right. So how do we do this exactly? So we do inject a calcium indicator into the eye. This is called Psi G-Camp 6F. It works like G-Camp 6F. So basically it gets fluorescent when the neurons are active, but it is co-located to the synapses only. So you get the output, the fluorescence, mainly at the synapses. And then we have a window on top of the brain, of the mouse, and can image the activity. As you can see in this picture here, showing you the retinal terminals in the superior click loss, you can actually see that superior click loss is covered by the cortex. And that's true for most of superior click loss except the very posterior part. And this is actually the part we accessed because we did not want to disturb any other brain area. So we put our recording window right at the back of the cortex. And this is what we can see once we have this window in place. So here, anteriorly, you can see the superior click loss. So it's a very specific portion of the superior click loss. Here you can actually already see the inferior click loss. So once we have this in place, we can then image using two photon imaging, the activity of the retinal axons. And we do this by imaging multiple planes, as you can see here. And these planes, we positioned them very close to each other, about two microns. Because the problem is that the brain is moving, especially when the animal is moving, is running, for example. And the retinal butyls are very small structures. So it could be that the brain and the retinal axons are moving in and out of the imaging plane. So in order to track single retinal axons, we have this volume here. And if the brain is moving, we can then post-talk track the single axons through this volume, okay? Just to show you what the data looks like afterwards. So here, what we presented to the mouse are these kinds of grating stimuli. They're presented on three monitors. So this is what you can see here. And these gratings are drifting. And I will start this movie in a bit. So you will see single trial data in this movie. Whenever the grating is on, you will see an arrow appearing here in this corner and it will show you in which direction the grating is moving, okay? So let me start the video. I hope you can see this okay. So you can see often quite strong off responses of the boutons.  But if you look closer or at other boutons, you can also see that they are tuned to very certain directions of the stimulus, okay? So this is data, this is sped up 10 times. And it is kind of smooth in time and it shows you the data after aligning an X, Y, and Z. Okay, so in addition to this neural data, we then also recorded the behavior of the mouse or the arousal state. And we do this with two measures. One is the running speed. So as you can see here in our setups, the mouse, the mice are always head fixed, but they are sitting on either this ball that is floating on air or on a treadmill that can rotate into one direction. And yeah, the animals also surrounded by these three monitors. So we are just recording how quickly the mouse is running. And it's free to run in these, in our studies whenever it wants to. And as a second measure, we are looking at the pupil size. So we have a video of the eyes. You can see here quite clearly that the pupil is changing in size quite a bit. And if the pupil is large, we're taking this as a measure of high arousal when it's small. It shows that the animal is in a state of low arousal. Right, and now we are coming to the data. So let me just switch on the laser pointer again. Yeah, so these data, this data set here was collected in darkness. So the mouse is in complete darkness, but it is free to run. And here you can see this running trace going up and down. In this case, we could actually only measure the running and not the pupil because in darkness, the pupil in our case was fully dilated. So we couldn't actually track any changes in size. And here's what the recordings from the retinal axons look like. So here is one data set and each little row, each line of this plot here shows you the activity of one single bouton. And so we have about 300 or so boutons and they are sorted according to whether, yeah, how positively and how negatively they're correlated with running. So on top you see they're positively correlated with running at the bottom, they're negatively correlated. And here you just see two boutons that occurred in this data set just as a single trace. Okay, so this shows you that in darkness, the activity of the retinal boutons are modulated by the behavior. And this cannot be driven by any visual input because the animal is in darkness. So then we recorded from the same retinal boutons but showed them visual stimuli. So these same gratings that you've seen before, now we can also measure the pupil and you can see that the results are quite similar. So actually I should tell you that now we have here the gratings. You can see the onsets of the grating is marked by these little tick marks and that induces the strong visual response. So that's why you can see this kind of stripy pattern. It's very thin stripes, okay? So that's the visual response. But even on top of the strong visual response is the modulation by behavior. So again, when the mouse is running, these boutons are up-modulated. So the responses are stronger whereas the boutons down here have a reduced response. And again, these are two examples, the same boutons when we showed the gratings. So what you can take away from this that even when the boutons are strongly driven by visual input, you still have this a behavioral modulation. And now comes kind of the quantification of this using all the data we had recorded. And the first plots show you that the effects are heterogeneous, meaning a rousal induces positive correlations in about half of the boutons and negative correlations in the other half of the boutons. And it doesn't really matter whether this is in darkness or whether you show gratings, okay? You can see this here in this histogram. The black bars show you when the correlations were significant and the white bars when the correlations were insignificant. And the whole, this whole histogram, this whole distribution was significantly different from what you would expect by chance which is shown here by this gray shade. And the same basically you can see here for the gratings. Now you can, we can also compare this more directly. We can plot the correlation for each bouton during darkness here in the X axis and during gratings on the Y axis. And you see there is a strong correlation. It is not exactly the same. So there are some things are different but the main message here is that the effects are very similar whether you have whether you do have visual stimulation or you don't have visual stimulation like in darkness. And you probably already realize that I'm kind of switching between running and pupil size. We kind of treat it as more or less one in the same measure of a rouser. This is because these two measures are highly correlated as shown here. So there is a very strong relation between pupil size here on the X axis and running speed on the Y axis. Now if you look at the correlation values of the single boutons like here on the X axis you can see correlation with running on the Y axis correlation with pupil. This relationship is seems even stronger. So the similarity of these correlation measures is very strong even stronger maybe than the similarity between pupil and running. Okay, so that's why we didn't try and figure out what the difference exactly is between running and pupil size. Right, so this was kind of a first result showing that there is some modulation and now we're looking a bit closer at what the effects are for different stimuli, particularly for different movements of directions for the gratings. So here we took the pupil size as one measure and we divided the data into those where the pupil was small and those where the pupil was large and here you can see tuning curves for one single retinal bouton. In black you can see the direction tuning curve for small pupil and in red for large pupil and you can see for this bouton that the response decreased when arousal was high or when the pupil was larger. The same or for this bouton it was similar so for the preferred direction the responses went down with arousal but for other stimuli, so for those that were non-preferred responses went actually up. So it shows you that there seems to be some kind of selectivity or difference between stimuli. So the effect of arousal can be different depending on the stimulus. But overall we also see that the preferred direction is not affected. So here we have preferred direction when the pupil is small, when the pupil is large on the y-axis and you see most of the dots following the diagonal showing that this is not changed. But if we then focus on the responses at the preferred direction and look at how the response is modulated during arousal which is shown here, you see the modulation is mostly negative meaning for most of the boutons, not all, but for most of them the response goes down, okay? So at the preferred direction mostly responses are suppressed. And this was very surprising to us mostly because in other visual areas specifically primary visual cortex people mostly saw that the responses go up with arousal or at least it was kind of 50-50. But we were not the only ones seeing this. There was another group who published this last year having a very similar setup but recording the axons of the retina in the LGN, okay? So they also had the mouse here head fixed and running on a ball showing them gratings but now they recorded activity in the LGN so they had to take off the cortex and then they could image this and this is kind of an example image that you can see when you express like we did cheek and 6F in the retinal axons. And kind of summarizing one of their main results is that if you change, well first of all if you compare the activity during low arousal states with those in high arousal states you see mostly the activity goes down. So this was similar to us, to our findings but also very interesting is if you change the spatial frequency so basically how thin or thick the stripes are of the grating you see that the suppression is different. So for low spatial frequencies the suppression is much stronger than for higher spatial frequencies, okay? So yeah, now we've seen, I've shown you that the activity of retinal boutons is modulated. Now we were asking ourselves what could be the possible mechanisms behind this, okay? And we came up basically with two main hypotheses. One is, and we hadn't considered this when we started the recordings but you have to think about what you're actually measuring, right? You measure or we measure the activity of retinal axons in the superior click loss. So you're not only seeing the firing rate of the retinal ganglion cells but this is modulated in addition by presynaptic neuromotulation on those axons, okay? So what we could have seen is that there are neuromotulators, for example, serotonin, acting on those retinal axons and this is actually what we are seeing, okay? This is how retinal axons get modulated. This is one hypothesis. The other one is of course that the modulation is happening in the eye so that somehow information about the behavioral state of the animal goes back to the eye and modulates the activity, the firing rate of the retinal ganglion cells in the retina more directly. So one way is by axons coming back from the brain, there are very few of those, but there are some. So this is one possibility. This information could reach the retina, okay? So then we went on and tried to figure out which hypothesis it is and we tried to test the second hypothesis and for this, we now switch to ETHIS. So we use neuro-pixels probes to record from retinal axons. So again, we couldn't figure out how to measure directly from the eye in an awake mouse. So we thought the best next thing is trying to record from the axons which we think should not be affected by neuromotulation happening much later at the pre-synaptic site in the superior clickers, for example, right? So this is our assumption that the activity in those axons is not modulated further. So basically what we can see is the firing rate of the retinal ganglion cells without any later neuromotulation. And these are results from two of these axons. So again, we recorded this in complete darkness and the mouse was free to run whenever it wanted to. So here in this first trace, you see the running speed of the mouse over I think 40 minutes or so. And at the same time here, you can see the firing rate recorded from one single axon in the optic tract. And you can see it's hard to see by eye but if you look at the cross correlogram of this data, you can see that there is a positive correlation between the running speed and the firing rate of this axon. And it is actually higher, more positive than expected by chance, which is shown by this red shade here. And here you see a second axon which was recorded. Again, you see the running speed of the animal and the firing rate. And again, this one shows a positive correlation which is significant, so more than you would expect by chance, okay? Now we managed to record from a total of 25 axons, which is really not a lot, given that we use the neuro-pixels probes. But yeah, you have to remember that the neural... Sorry. The optic tract is a very thin structure and it's also very deep in the brain. So it's quite difficult to hit it. And even if you get there with your probe, you will not record from lots of axons because again, axons are very thin and it's kind of hard to record from them. It's also very hard to keep a stable recording over time. So we had very stringent, how do you say? Measures to ensure that we have a very stable recording and that we are recording from retinal axons rather than from anything else. So that's why this number is relatively low, okay? But what you can see here on the x-axis is the correlation of each axon with running and on the y-axis is the p-value. So every dot that is above this, 0.5, a 0.05 p-value is significant, okay? And these were, I mean, like, I forgot, nine or so. Now I said it wrong, of course. So of course, you have to look at the dots below there. Yes, I get this plot is somewhat confusing. So we have swapped the y-axis here. So the smaller values are at the top of the y-axis. So whatever is on top of this dotted line has a very small p-value and is therefore significant. So we found that nine of 25 retinal axons have a significant correlation with running in darkness, okay? So this shows you that the activity of retinal ganglion cells is apparently some of the retinal ganglion cells are modulated by running, okay? So I want to just emphasize that this does support our second hypothesis that somehow information gets to the eye and therefore modulates the activity in the retina directly, but it does not exclude the other hypothesis. So in top of this, you can still have modulation at the pre-synaptic site in the superior colliculus. And I think this is very likely to happen anyway because there are receptors at the pre-synaptic site and there are neuromotivators projecting in the superior colliculus. I think it's very likely that both things are happening. Right, so after this, we thought, well, how does this affect the neurons that are listening to the retina so that get input from the retina? Namely, what is happening to the neurons in the superior colliculus post-synaptically? So now we did the same thing, but instead of imaging retinal vultures as in the beginning, we now image the activity from neurons in the superior colliculus, which you can see here. And here's again the results from one data set. You'll see pupil size in green and the running speed in yellow and several hundreds of neurons. And you clearly see that when we showed the gratings, there's visual input, but on top of this is the modulation by behavior. So again, these neurons were sorted by the strength of correlation with running speed. And this is basically the histogram or the cumulative histogram of correlation with pupil. In black, you can see those for the superior colliculus neurons in the superficial layer, so the visual neurons. And in brown, you see the correlation or the distribution for the boutons, but you can hardly see the brown line because they're basically on top of each other, meaning the distribution of correlations is very, very similar. Now, again, we looked at how the tuning curves of these neurons are affected. And you can see very similar things as to the retinal boutons, but now we also see quite a few neurons whose activity is up-modulated. So in red, you see the activity during higher levels of arousals. On this neuron, it is up-modulated. And here you can see the distribution of this response modulation to the preferred direction. And you see for the black line, which is for the neurons that about half of the neurons are up-modulated and half are down-modulated. Whereas for the boutons, there is a bias towards a decrease, as I have said before. So there are strong similarities, also some differences in how neurons and boutons, retinal boutons are affected. So it doesn't prove that the superior colliculus neurons inherit this modulation by the boutons, but it does show that it's very similar and probably the effect that we see in the neuron, that we see in the boutons, also affect the responses in the neurons, okay? One thing that might be slightly confusing is here, especially for the boutons we saw and that the correlations are kind of 50-50, positive-negative, whereas here, response modulation is mostly negative. So it's relatively simple here. When we look at the correlation, we really look at the activity along the whole time, right? And how is this correlated? Whereas here, this response modulation only looks at the preferred direction. So the response, so the preferred direction of motion, okay? So we do not look, we kind of ignore what's happening with the responses to other stimuli. That's why you can actually have this difference, okay? So overall, we think the modulation of retinal boutons and superior colliculus neurons are relatively similar, but the other input that the neurons in superior colliculus get is from primary visual cortex and we already know that these neurons are also heavily modulated by behavior. So now we wondered, is there also an effect from V1 in superior colliculus? Okay? That's why we, again, recorded now using electrophysiology from superior colliculus, but in one condition, we did nothing to primary visual cortex and in the other condition, we inactivated primary visual cortex and we did this using optogenetics. So we expressed an opsin in inhibitory neurons and PV neurons, and then when you shine the blue light on primary visual cortex, these inhibitory neurons get active and therefore decrease the activity in the projection neurons that project to the superior colliculus, okay? So here is now the activity of one example neuron in from the superior colliculus. Here is in the control condition. So you see it's nicely modulated by arousal. The activity goes up. Now, if we inactivate V1, generally the response goes down by quite a bit in this neuron, but the effect from arousal is still there. So still the response is up modulated, okay? And actually the relative amount of the up modulation is very similar in both cases. So therefore we concluded that this modulation is not inherited from V1. And here's just the quantification for all neurons that we recorded. Here again, I showed this response modulation showing what happens to the neurons, to the responses to the preferred direction. So here you see many neurons are up modulated, some are down modulated, and the same is true even if you inactivate V1. And you can also compare this directly. So here is response modulation during the control condition and here is when V1 is inactivated. Most of the dots kind of fall on the diagonals, so there's not much difference. Only the black dots actually show a significant difference. However, there are also many black dots when V1 is inactivated. So actually showing that the modulation was bigger when V1 was activated, okay? So overall I think it shows that V1, the input from V1 can not explain the modulation by arousal in superior clickers. All right, that's my time, we have 10 minutes. So at this point I've shown you that responses of the retina and also of superior click loads are modulated by these states, locomotion and arousal. And you may wonder, well, what else does the retina or its output know about? How much else does it know? And what I want to tell you now does, it does not seem to have access to spatial information which occurs first in primary visual cortex, okay? So this was a study led by Aman Salim. He had a mouse running through a virtual reality, basically through a corridor, which you can see here. And this corridor had four different landmarks, okay? Basically the first half of the corridor looked exactly the same as the second. So this landmark and the third one were exactly the same and the same one. Also the second and the fourth landmark looked exactly the same. So if a visual neuron purely cared about visual input, the responses of this neuron should look the same during the first and the second half, okay? Now what he found looking, recording from V1 is that some neurons, for some neurons, that is the case. So here the solid gray line shows you the response of one V1 neuron and the dotted line is just a copy of the response from the other corridor, basically. And you see for this neuron, the responses look very similar for first and second half. Whereas for this neuron, it's very different. It responds much more in the second half of the corridor. And in this neuron it's exactly the opposite. It's response much more in the first half. So somehow these two neurons care about the spatial location of the mouse, right? Whether it's early in the corridor or late in the corridor. Later on a study spearheaded by Mika Diamanti looked at LGN boutons that are coming from LGN two primary visual cortex and she imaged those boutons. And you can see here the activity of three different LGN boutons. And what you can see, hopefully, is that the responses here for all of these example boutons and also in the rest of the population, they were very similar in the first and the second half, okay? So it seems like any kind of spatial information, first is represented in primary visual cortex and not before. All right, so that just as a side story, now I want to summarize my results. I've shown you that arousal does modulate retinal output. It can or does differ across retinal terminals because some were positively, some were negatively influenced. It's also stimulus dependent, okay? So for some directions, you saw that the response can go down and for others it can go up even in the same retinal bouton. These effects are even observable in the firing rates of the retinal ganglion cells which speaks for a modulation at the site of the retina. And arousal also has very similar effects in the post-synaptic neurons, okay? So these are the findings as we have them now. I think, oops, sorry. The big open question for me is what is the computational advantage of this behavioral modulation as early as in the retinal output? And I just want to kind of show you two things I'm kind of thinking about. One is kind of along this idea of predictive coding and retina already does predictive coding amazingly. So what do I mean by this? So if you look at natural stimuli like this one, and you can also imagine how this moves along, there are lots of correlations in space and in time. And the retina is clever enough to compress this information by encoding only those features that are unexpected. So somehow it knows of this stimulus statistics of natural stimuli and kind of gets rid of everything that's boring and expected, okay? So that's one way of predictive coding that's happening in the retina. And then there's also even a dynamic version of this.  And if you adapt the retina to these kinds of stimuli and then measure the receptor fields, it might look like this, so typical centers around. But then if you show lots of vertical stimuli to the retina, you see that the receptor field changes so that it responds less to verticals of the expected stimulus and responds more to horizontal unexpected stimuli and the other way around, okay? So this whole predictive coding can happen also on a relatively short time scale, shorter than life. So I think it could also be good if, you know, if the, assume that the stimulus statistics changes depending on behavioral state, for example, running or not running. If the retina knew about this, it could already change its receptor fields or its response properties so that it adapts to those expected stimulus statistics. Okay? So this could be one way why this could be good thing to do to integrate or be modulated by behavioral state. And the other one is, well, being in different behavioral states could mean that different things are relevant to your behavior and you may want to enhance or suppress specific stimulus features depending on which state you're in. So for example, you might want to increase stimulus sensitivity during choir sense. So you are alerted to, for example, dangerous stimuli even if you're kind of dozing away. Whereas you might want to increase stimulus selectivity when you're alert and really looking for a very specific thing. Okay? So these are kind of the ideas of why this might be good. And in the future, I want to look at kind of three, three different questions or approaches I want to take is looking at the much larger variety of racial stimuli. I think that's pretty straightforward. So in this study, I only use gratings and vary them in their direction of movement. But of course, there's lots more parameters we can and should probe to see where which parameters are modulated or responses to which parameters are modulated and how is the modulation happening in which way basically. And also looking kind of similarly, looking at different functional cell types to see which cell type is modulated in which direction and seeing what different functions those cells have could give us a clue of which functions are enhanced and which ones are decreased. And of course, also the third point is looking at mechanisms. So how is this coming about? How do retinal boutons and superior click-flash neurons know about behavioral states? And I think as I mentioned before, neuromotulation is a big candidate. So looking into this, I think will give us a clue how this could be happening and also give us more details in what is possible to be modulated. All right, and at last, I want to thank co-authors on my paper and of course my collaborators these people here in my previous lab and also in different labs now. And I want to thank my funding bodies, the EU Welcome Trust, BBSSC. And yeah, it was mentioned at the beginning that I just started my lab last month in the University of Sussex. I'm still looking for a PhD student. So if you're interested in these kinds of questions, please get in touch with me. And yeah, also again, thanking my funding bodies here that make it possible for me to do future research on this topic. Yeah, that's it. Thank you very much. And I hope you have lots of questions. Thank you very much, Silvia. Indeed, a very interesting topic, UNTALK. Lots to unpack and definitely of crucial nature, the recordings you actually did of the RGC action tracks. There are already a couple of questions in the chat and just to remind our audience, like both regular and newcomers that there will be a short discussion like with the questions you post on the chat. And then we will invite you in the very Zoom room that we are currently sitting in for a post-talk informal conversation. So I will start with the questions like one is from Luisa Ramirez but you have already or maybe at least partially addressed I don't know if you want to add anything to that but the question was biologically is there a motivation for having inhibition directly in the eye? It was one of the first outlook points that you made like why we would already have this effect in the retina. I don't know if you want to add anything to that if not I will proceed with the next. Yeah, yeah, I can't add a lot because it's just guesswork at this point. But yeah, I agree that this is the most one of the most important questions why is this happening? And I'm very, very curious myself and specifically because we have the opposite mostly the opposite behavior later on in the hierarchy, right? In the primary visual cortex it seems like that the neurons are enhanced during arousal or the responses are enhanced. So yeah, it's a very big question. And I think we can only answer this with the kind of approaches that I showed by looking because maybe, yeah, maybe we were unlucky we are biased by the stimuli that we've shown and actually for other stimuli it could be different. So this has to be kept in mind and we should also not forget that some of those retinal butans there was an increase in activity and I think the most important step now is also looking at different cell types and seeing which ones were actually up modulated and which were down modulated. And yeah, then I think we have to look at the different stages along the hierarchy and seeing how is the effect of the retina actually inherited by superior clickers. So now I did these measurements in two different animal sets of animals basically. So I think, for example, recording at the same time and figuring out what all the different inputs to the neurons actually do is getting, will get us one step closer to the answer of this question. But yeah, for now I don't know. Yeah, thank you for that. Next one up is Grace Lindsay and she asks the following, the correlation between modulation from running and pupil size is surprising as you'd expect a change in pupil size to impact firing rates on its own. Is the impact of pupil size on activity small? I'm not sure I got the question correct. So she thinks that the running speed basically causes the fluctuations. So she claims that it's surprising that you have a correlation between the pupil size and the running speed. Does that imply that the effect from pupil size on the activity is small? No, it doesn't. I mean, it could be both of these things, right? So how I think about this is there is some brain state which we call arousal which is affecting the running speed and it shows up by the changing pupil diameter and also affects the neural activity, right? So there's, yeah, this vague or this third player called arousal and we just measure it by running speed and pupil. And yeah, it happens that these two measures, running speed and pupil are highly correlated which one affects activity more? I don't know. And maybe it's not one of them directly. I don't think pupil diameter itself causes retinal activity to change. I mean, it is important when you have light conditions. So when we show the gratings because of the changing pupil diameter there's more or less light coming into the eye and therefore this could trivially explain changes in responses. But that's why we did also the recordings during darkness where you don't have this confound basically. But yeah, as I also mentioned in the talk we didn't really try to separate between the effects of running and pupil dilation yet because they're so highly correlated. So it's an open question which whether there is a difference between the effect of running speed and pupil. Next one is from Michael Reiser. The balance of modulatory effects is very interesting. Could it be that the retinal modulation is mainly positive correlation while the effect of arousal modulation in the superior colliculus is mainly suppressive? So this is about comparing the E-Phys results versus imaging, right? In the E-Phys we had these nine axons and most of them were actually had a positive correlation only two had a negative. So I guess the question is, is there now a difference because from the E-Phys results we would expect more positive correlations whereas imaging we've got like a 50-50 correlation. I think the numbers are just too small to make any meaningful statement about this. So I think what we can conclude from the E-Phys results so from the recordings of the axons is simply that something is happening. So somehow the activity in the axons and presumably in the cell bodies of the retinal then get information about running speed, okay? But I wouldn't claim anything about numbers because it's just too, too little. And I think that if you do any kind of test significance tests, it wouldn't hold up to really compare differences in distribution from axons and imaging. So yeah, I wouldn't read too much into the exact distribution of correlations. Thank you very much. The last one appearing on the chat is from Hervik Bayer. Hi Sylvia, nice talk. You may want to look at Cortisol as a modulator. There is evidence from Zebraphys that glucocorticoid receptor activation can alter retinal physiology. It's more of a comment and less of a question. That's it, yeah, okay. So this is at the side of the retina and the cortisol would come from the bloodstream or? Unfortunately, I will be stepping on thin ground if I attempt to clarify that but I'm pretty sure that he can join us in the discussion, Maxim already posted this room link in the chat. So I will keep the live broadcasting for a couple of minutes more as people start already to join us here in the room. I have one question myself because you also mentioned simultaneously recording from the boutons or from the post synaptic cells and the axonal tracts. Couldn't you use a neuropixel probe properly tilted so you can simultaneously record both from the axons and the superior colliculus or that is impossible to assume? Well, okay, first of, I guess the answer to your question is yes, you could do this very easily with two probes, right? So the problem is not getting superior colliculus at the same time. It is much more of hitting the optic tract. So I even had two probes kind of trying to hit the left and the right optic tract at the same time just to increase my numbers, but I wasn't super successful even then. So that's not a problem. The problem is also even if you hit both that you don't know which of the superior colliculus cells is actually getting the retinal input, right? So you still don't have this one-to-one relationship. So I think this is really the big advantage of the imaging. So what we're actually planning to do is having dual color imaging of the retinal boutons at the same time as with the different colors of the neurons. Of course, it's still a problem to really know where the synaptic input, which axon is talking to which neuron, but I think it will give us a better clue of what is happening at least at the same time and in the same vicinity at least of the retinal input or output in the neurons that are listening to it. Thank you very much for that. I see there is a clarification from Grace. She says, sorry, I meant that when you change pupil size, you change the light that enters the retina which would change activity even without top-down modulation. Exactly. So yes, that's also what we thought. So we actually did those recordings first. So we recorded first under light conditions. So we recorded the responses to the ratings when this could be a problem, right? And therefore, we then decided to introduce the complete darkness condition, where you do not change the visual input anymore. So also I said during complete darkness, the pupil size does not change anymore. It's fully dilated. So you're getting around this and we still see even in darkness that the retinal boutons are modulated. So I don't think it's the whole... It's not the whole explanation. Now, yes, I think I would just repeat what I said before. I don't think there is running speed and pupil that are affecting these responses, but it's some other third component arousal which just shows up by these measures, running speed and pupil, yeah. Great. Yeah, I think that pretty much sums it up from the audience side. I would just like to remind everyone that you can join us in the Zoom room we are currently sitting in. And with that, I would like to thank Sylvia for honoring us and giving a talk about behavioral modulation of visual processing. And officially now, like before I terminate the live broadcasting, so I will give another couple of minutes so people can join. I would like to wave my rights. As moderator, so people who join can freely ask their questions without waiting for my green light, let's say. Yeah, thanks again for having me. It's really a great seminar series, yeah. Thanks. So if somebody doesn't want to go ahead with a question, I think I have another one, probably quite naive, I would say. Because we study, like you study, the effect of behavioral modulation on visual processing, but then again, you do not try to influence arousal per se, right? So I was wondering if you can play some more exciting stimuli for the behavioral arousal state to see if there is like an effect of vision on the arousal that comes back to vision again. Ooh. Like similar to the most exciting images approach that they have, like that they play different images and they try to figure which one triggers the higher responses and then they replay this again. So in a similar context, but behaviorally modulating, including. Maybe it's really naive and stupid, I don't know.  I think the, so from my view, I think this would cause several problems though, because then it's kind of this loop and you don't really know anymore what's causing what. So is it the visual stimulus itself or is it kind of the excitement content in the stimulus that is acting on the neural response? So I'm always trying to separate visual stimulus from behavior. So having the same stimulus under different behavioral conditions or the other way around, right? So I think having these stimuli that always induce a specific behavioral state that gets you into problems. But yes, I am thinking about controlling the behavior better by simple things, like for example, having a motorized wheel. So yeah, you can kind of induce running or stop running in the animal. And yeah, I also want to look at kind of behaviorally relevant stimuli. For example, those that look more like a predator, the typical looming stimuli that everyone's using now or also stimuli that look like food for the mouse. So it would induce kind of approaching behavior. I think that's very interesting, but maybe has already similar problems that I just alluded to. So if you see, of course, if the mouse sees a predator, it probably gets very excited. So yeah, I don't know. Yeah, kind of interesting to see what would happen then. So yeah, I see Simon is here with his microphone on. I don't know Simon, do you want to go ahead with a question? Yeah, I've got a question for Sylvia. It was a really good talk. And I'm very excited to see that people are getting responses from ganglion cells in intact behaving animals. Because as far as I understand it, you might be able to correct me, but as far as I understand it, almost all of the work on the coding of natural images by ganglion cells is done on unnatural ganglion cells because they're in isolated retinas or in their isolated eyes. And so my question is, do you see from these very early recordings of yours that they're the first of their type, I think, are they? Are these? Well, there is one group that managed to record using a kind of electrode array, putting it behind the eye. They were able to record from retinal ganglion cells, but I don't think they, yeah, I don't know how stable this. So it's kind of a similar measurement with a different approach, which came up around the same time. So my question is, is there anything in your data or in your observations that suggests that the idea is about there being a very sparse code and very low firing rates when gang, average mean firing rates when ganglion cells are coding natural scenes? Is there anything that contradicts that? Because some of your spike rates seem quite high and sustained even in the dark. Yes, I was very, I mean, I'm not the retina person by training. So I didn't know much, but I was very surprised to see those high firing rates myself during darkness. Yeah.


Thank you for staying with us. You're still watching The Breakfast on Plus TV Africa and right now it's time for our next hot topic So we're talking about navigating the dollar inflation with the right software for your business and joining us I'm to have a conversation and this is Kendi Ogundari. He's the country manager Zoho Nigeria. Good morning, Kendi Lovely to have you again. Thank you very much for having me. Why do I even always think about Zoho when I hear Zoho? She should be bringing us some drinks to drink. Well, maybe we'll consider that. Yeah, that would be nice. Alright, so we're talking about navigating the whole dollar inflation. I'm sure dollar inflation is crazy for a lot of businesses out there because you try to buy one thing today and tomorrow you're going to replace that same product and because of the dollar inflation the price hike is ridiculous So now let's talk about navigating this and using the right software for your business. So how does technology help in all of this? And then what kind of software should you be looking at that could just help you a little bit? Fine, it might not be, it's not going to reduce the price but it would just still help in a certain way. So just walk us through that. Alright, thank you very much for having me again. Okay, so when you talk about technology as a means to reduce or mitigate the effect of the dollar inflation in Nigeria what I normally tell people is you have to look at for three things when you are trying to select the right software. The first one is the security and privacy they provide that have in place so that when you're putting your data on that particular software you are very sure that your business is secure nobody is going to breach that contract or anything. The second one is the affordability because when you're using software it tends to get expensive at some point. So how do you now manage that? You don't look at the cost Number one, secondly, with the current inflation rate in Nigeria and the dollar exchange rate I will advise businesses to probably look for vendors that actually sell in local currency because you know, $20, $5 is going to remain $5 from January to December. However, our own experience in Nigeria is that your $5 in January is not the same thing when you convert it to Naira in February or March. Two months ago we didn't even know what, I mean maybe it was less than $1,000 now it's about $1,000 today or $1,400. Now imagine you have a budget in January you will not be able to sustain your business true for the entire year with that budget if you're building, you're being built in dollars and if you make your money in Naira, why are you paying in dollars? Is that a question? Exactly and the third one is public scalability because every business owner's dream is to grow your business and at some point you will get to a point where by your business is grow and large enough is the current software you're trying to adopt does it have the capability to scale with you? So you won't be faced with the issue of maybe moving out of that software then embracing another one and so it's going to be too stressful. So three things security and privacy, the price factor and the third one is scalability of that software it will help businesses shape the way they do their business and also mitigate the effect of these exchange rates inflation in Nigeria when you plan ahead properly.  Because when the name and just here is that you have, you use the software it will help you go through these you know you may not understand what is he talking about that's why I said that. So what do these softwares actually do that will help you in this regard? Okay fantastic. I'm going to take about 30 departments from any business. I believe for every business owner there are three core departments in your organization for you to scale. The first one is your marketing. You want people to know what you're selling. The second one is your sales. After shouting about your brand there must be somebody that will say okay you know the price is too narrow and the third one is finance. When the price is too narrow how do you account for the money coming in? You know so these are three major departments in any business and look to any shop today any small shop big shop they must have this thing in place. Maybe word of mouth marketing. Now how does software help you? Now you can actually inculcate or try to bring software into your marketing department or we can probably go for social media marketing department digital marketing right or you can even do e-marketing e-mail marketing. I believe in newsletters. I believe in sending emails regular emails to your clients updating them about what you're doing what is up next what is going on with your business. So when you think of buying a particular product the one that would drop in their mind is your brand. So digital marketing is very very essential and you need to have tools to manage them. Currently there are about seven to eight platforms for social media right now. Can you imagine you're moving from one to the other one to the other one but then can you imagine what it's going to do for you when you have a platform that brings all of them together and you can actually see the report on the same dashboard. So that is the way you can manage that number one. Secondly for your sales right I believe that for sales you need a CRM and the CRM means customer relationship management software right. So you want to have the database of your existing clients at the same time you want to be able to nurture new leads and turn them to sales. So when you have a CRM that can automate this process for you it will reduce a whole lot of your stress. Sending emails scheduling meetings are signing these to other clients you know scheduling meetings to meet up showing them demos you know or even sending them samples. So you will be able to manage your entire sales process with a CRM tool and finally you need the finance application. So when you're getting money how much do you have outside who are you owing who is owing you you know so you get to see your profit and loss you get to see your trial balance you know you just want to be able to receive money and make payments easily and also have a track record of your finances and also honestly because as a small business or medium business you will not be able to even access some grants from the government unless you have a complete bookkeeping of your business. The worst for that record. Maybe not for your tax you know your tax file. Clearance and so many yes. So you won't be able to keep track of that unless you have a software that you are using to do that. Imagine you go for your audit and you are going with a pile of books okay this one has not paid but you have a story that will remind you that we're going to send a reminder to your client that you've not paid Kenny you have to pay him two million or three million you know.  So very important for you to have marketing sales and finance software to run your business and all you know you can also now incorporate maybe productivity tool right so then you can allow your staff to work from home. Hybrid right to remote job. I really be two days at work three days at home or completely remote job. These days you will be able to manage their productivity with every software they are using. Okay so because that brings me to my next question I think that kind of just segues into it because you talked about you've talked about your finance you've talked about marketing you've talked about sales. I wanted to talk about human resources as well you know are there softwares that actually help you know to to start for you to even know what your what your staff is doing to actually measure their performances and productivity so are there softwares that could actually help with that because we can focus on you know sales and marketing I mean that's the that's the lifeline of the business that's the blood of the business you need money coming in or else you are not in business but you need human capital for you to ensure that the money is coming in. So what softwares do you have to look out for when you need to measure their productivity. Fantastic and even in my own organization we have the same. There are people who say customers are always right but I bet to differ I believe that when your employee is happy they will make your customer happy and then your customer will spend more happily and your business will grow. True. You understand. So yes these days we have software for example in my organization we have Zoho people which is an human resource management software so with that application you can mark their attendance they can apply for leave they can do their appraisal cycle they can do the performance index they can do their KPS through the software they can even do peer-to-peer review so you know for example manager is just the manager you know you cannot probably see or have personality with everybody but within the team itself they know who is the go-to guy that okay for any problem I should go to this guy he was going to help me but the manager might not see that maybe that guy is very quiet but he's the backbone of the entire team so when you allow your team members to rate them so peer-to-peer reviews in more things you can do also remote access you want to see what they are doing I mean right now if you're working remotely you can check in from there then also we have software that will give you the live web live cam so if I'm sitting in front of my desk they can actually see what is around me but I think we can't accept that word right and it's easier for people you know you are more productive I can sleep on my baby debt to five minutes to my meeting just wear my jacket hi good morning everyone you know you can sleep rest properly and be more productive with your time okay I like homegrown solutions do we have homegrown solutions to all these ones or it has to be imported technology oh we have homegrown solutions we have a couple of them in Nigeria but also to imported technology is not bad because if they are building local currency that is where we come in right because if the company is mindful of the economy they will try to relate with the local people in my organization we call something transnationalism that is when we try to be present in the market we are currently engaging so that is why our pricing is in local currency we have um indigenous people working for us we employ a lot of people empower them and you do a lot of things so homegrown is really really good because in the long run it's going to help us build the economy at the same time we cannot dismiss the fact that we still need in process technology to enhance whatever we have in ours oh wait sorry when you talk about the fact that it can be expensive sometimes yeah a lot of people cannot relate because when you're talking about apps you just think about going to Google Play Store so what's the difference between these are the ordinary apps that we have okay so the mobile app is just the social media platform I mean for example just download on your mobile app and do well when it comes to business application this is where it gets serious because when you're doing business it's a serious business right you have to be tailored to you to your business um not necessarily so if I'm going to use my company for example we have um a comprehensive suite of applications okay and then when you approach us what we do is we understand your need and then we now customize the software to fit that purpose so that's the kind of tailored to your needs so you can customize it so it's not like one size fits all because everybody can be the same line of business but their processes might be different you know so you can customize the system flexibility exactly that's good and then the cost is not so much because most of the people think technology you will never say is much compared to what you're going to lose if you don't have it yes yeah but the truth is even the cost is not expensive trust me if I'm talking about for my own company because that is one thing about my general market that is enticing to us we try to actually relate with the people so the cost is nominal compared to what you will pay you probably paid about 50 to 40 percent less than what you would pay our counterparts interesting all right thank you so much this is where we have to wrap it up sadly but you know we'll just come back in and just take a deep dive on all of this whole technology thing but yes thank you so much for joining us thank you for having me we really appreciate you all right we've been speaking with um our guy from Zoro we're just talking about navigating the dull inflation with the right software for your business he always says pay Kenny you know he said it last time he said it out here but I'm sure everybody else will also want to be paid you know that's where we're talking about this no matter how small your business is yeah it's a possibility you can grow all right we've been picking to our guy Kenny Ogundari the country manager from Zoro Nigeria so I want to say thank you once again thank you for having me and this is where we wrap it up on the show today we have to go but we'll see you again tomorrow my name is Romare Paulson and I am Nyam Gul Agadji let's return tomorrow for another edition bye for now all right


Back in the day they used to go to this thing called an office. An office? Like OFF ICE?  But to get to the office, people have their own cars. I mean this is like before AI took over transportation. Or they would take something called a bus which is like packs them all in. I was just my my oxygen.   It was like the wild west.


What I did at the start of this race is probably something that backwards hat dylan would make a joke about but it wasn't a joke and I actually did it at one of the most important races of my season. Welcome back to the channel. This video is fueled by the feed. Those of you who follow this channel know that my big goal for the season is the lifetime Grand Prix which has quickly become the pinnacle of professional off-road endurance racing in the US. Featuring three mountain bike races and four gravel races throughout the season including iconic races such as the Unbound Gravel 200 and the Leadville 100 mile mountain bike race. The Grand Prix kicked off in Monterey, California with the Seattle Classic. There are a lot of races over the course of the Seattle Weekend but the Grand Prix race is the 100 kilometer mountain bike race or at least it was advertised as 100 kilometers. The actual race distance for the two lap race was closer to 110 kilometers or 68 miles. Hopefully racers were aware of that before they started the race. This is about 17 miles or 27 kilometers longer than last year's course. The terrain is a bit more aggressive than rolling hills with most of the climbs lasting five to ten minutes although they tend to be more punchy rather than a steady grade. While not overly technical for a mountain bike race, the course was a bit rutted out and bumpy after some recent rain. Because of this, I opted for the Factor Lando full suspension bike as opposed to the Hardtail. If you want to see a full bike check, Adam Raberjneid did one over on his channel and I left a link for that in the description. Feel how easy it is to push this down. Yeah, you're right. It's like, it's really wet and not, it's really well greased. Personally, I thought a full suspension was the right call and a lot of the riders who were on Hardtails last year made the jump over to a full suspension after seeing the course this year. Although admittedly, I don't think there are a lot of courses out there that I would choose a Hardtail for. But interestingly enough, most of the mountain bike races in the Grand Prix are pretty tame, at least technically speaking. And it actually makes it an unusually difficult call between a Hardtail and a full suspension. I'll keep you updated as the year goes on on what bike I choose for each race. All right, let's get into the actual race itself, starting with perhaps the dumbest thing that I've done at a bike race in a very long time. What I did at the start of this race is probably something that backwards hat Dylan would make a joke about, but it wasn't a joke and I actually did it at one of the most important races of my season. Basically, I was off the front and leading the race for the first minute. I really wish somebody had gotten video of this or at the very least a picture or something, but I haven't seen any, so I guess I'll just tell you about it. What are you talking about, bro?  I always make sure there's a race photographer present if I'm going to attack at the start. Okay, here's the context. The race starts on the Laguna Seca Raceway and there is about a two minute climb on the racetrack before you go straight into Singletrack. Last year, we sprinted as hard as we could from the time the gun went off until we got into the Singletrack, and I probably got into the Singletrack around 20th position. After this, there's 15 minutes where passing is nearly impossible. While this is a longer race and pacing yourself appropriately is usually important, getting as good a start position as possible is crucial. Because there is actually a lot of drafting on this course because a lot of the course is either wide open fast trail section or just straight up gravel road. The start will for the most part determine what group you'll be working with that day and obviously you want the group you end up with to be as strong as possible. Knowing this, I had been psyching myself up all week to put down my two minute max power at the start of this race. When I was warming up, I kept saying to myself, you've got to give it everything you've got on this start. And that's exactly what I did. I didn't get the memo that the rest of the field decided not to do that this year for some reason. So within about 15 seconds of starting, I found myself in the lead. I mean, literally first place and I actually got a gap over the rest of the field over the next 45 seconds. This isn't because I was stronger than everyone else, not by a long shot, but just because everyone else decided to go at a much calmer pace off the line this year. I'm honestly still scratching my head as to why that is. But this would come back to bite me real quick. With about 30 seconds of the climb remaining, that good old burning sensation in the legs really started kicking in. And I got passed by probably 40 or 50 riders in this very short stretch before we hit the trail. Yeah, not great. Don't beat yourself up, man. Now you can say you were beating Keegan Swensen in the lifetime Grand Prix at one point this season. You just got to come up with a couple of excuses for why you lost the title. Here are the numbers from the start. During that first two minute climb, I averaged 534 watts and had a max power of 950. But in that first initial surge, I did 760 watts for 30 seconds and 593 watts for a minute. And that's what really screwed me, because as the Peloton of riders caught me in the second half of the climb, I was only able to average 487 watts for the second minute of the climb, while the other riders were probably easily doing 100 watts more than that. And the average speed going up this climb was 18 miles per hour. So yes, sitting in the pack and drafting would have been the much better call. I got to be honest, I was pretty pissed at myself at this point. Obviously, if I had just stayed in the pack and started sprinting later like everyone else did, I would be in a much better starting position. When we exited the single track, both the lead group and the first chase group, and maybe the second chase group too for all I know, were well out of sight, which is a far worse position than I was in last year. If I had to guess, I'd say I was probably around 40th or 50th place at this point. It's in moments like these where it can be very easy to give up mentally. But I had to remind myself that this is a long race and there's still over 60 miles to go. In my early years of racing, this might have been something that would derail my race. Something wouldn't go my way and then either consciously or unconsciously, I wouldn't push as hard. Around my early 20s though, I had a bit of a mindset shift when it came to racing. Instead of putting too much emphasis on the result, I focused on simply getting to the finish line as fast as possible, no matter what happened and regardless of what place I was in. This seemed to be a good mental strategy for overcoming adversity in a race because no matter what happened, the goal was always the same. And interestingly enough, focusing less on the result actually ended up improving my results in races. This is probably a less applicable strategy to road racing because what the group does dictates how you race and conserving energy is important, but it seems to work well in mountain biking and I actually often use it in gravel races too. If you take Unbound for example, you are simply not going to have a perfect day. Bad things will happen, but how you deal with mid-race setbacks is a major factor in your performance at that race. Back to Seattle though, my goal at this point needs to become getting to the finish line as fast as possible despite my bad start and picking off as many riders as I can in the next four hours. I quickly found myself with a couple of riders and as we started catching guys, the group got bigger. I think at one point we had over 10 riders in it. This was a good thing because maybe with this kind of firepower on these open gravel sections, we'd be able to reel riders back in. And that's exactly what we did. One by one, we would see riders in the distance and then pull ourselves up to them. We ended up catching Brennan Wertz, a big rider with a massive engine and I knew that he would be helpful in the flats. With him, Justin Martin, Jack O'Dron, Steven DeVouse and a couple of others, we worked well together to keep the average speed as high as we could. I would say every 10 minutes or so we would catch another rider and this helped to keep the morale high. Towards the end of the lap, we caught Jeff Kabush who was actually on a gravel bike and while he did not have aero bars on his bike, the superior aerodynamics of the bike was apparent on the wide open sections, which are more prevalent in the second half of the course and letting him pull on those sections helped a lot. This first lap took two hours and seven minutes with a normalized power of 315 watts and an average speed of 16.2 miles per hour.  It's not something that I would normally go for, but if you remember from my previous video on testing gravel equipment in the wind tunnel, the pack surprisingly improved my aerodynamics and I have to say it did make hydration a lot easier out on course as I didn't have to reach down for a bottle. So why didn't I start with the pack on? Well, I wanted to be as light as possible for that opening climb and we all know how that went. I digress into the second lap at this point. The group that I was with that had been riding strong in the first lap was really starting to fade. I'll admit I was starting to fade at this point too, but not quite as much as the group was probably a quarter of the way through the second lap. I dropped the remaining riders in the group in the single track and I found myself solo. Despite this being a mountain bike race, being alone on this course is not optimal. As I said, there's a lot of drafting and at this point it was just me breaking all of the wind. It didn't help that I started to feel cramps coming on at this point as well. A quick note on cramping. It's a pretty commonly held belief that cramping is due to an electrolyte imbalance. Well, that may be a factor. A lot of recent research on cramping shows that it's actually due to fatigue. The answer that people don't want to hear is that if you want to stop cramping, there isn't a quick fix. You need to get fitter. Now, there is a large genetic component as well, but being in shape is the best remedy. I personally don't cramp too much, but if I do, it's usually in the first few races of the season and this makes sense because I'm pushing my body harder than it's been pushed in a few months. This was the first race of the Grand Prix and only my third race this season. So the cramps made sense, but fortunately they never got so bad that I had to slow down significantly. I was catching fewer riders in the second lap than I did in the first lap, but I was still moving up in the field as opposed to moving back. The last climb of the day is by far the longest and hardest of the race at a little over three and a half miles and taking almost 20 minutes. Now, it isn't uphill for the entire time. There are some short descents in there, but you do gain 700 feet of elevation at the bottom of the climb. I saw two riders off the trail and I was determined to catch them about halfway up. I made contact with them and saw that it was Sam Andrews and the current U.S. Cyclocross National Champion, Curtis White. I took a moment to catch my breath and then made another move and was able to get a small gap. They managed to claw their way back to me though. And now with the finish line nearly within sight, clearly they had more left in their legs than I gave them credit for. On the final pitch of the climb, Sam launched an attack and Curtis followed and I simply had no response as I was on the ragged edge of cramping at this point. We crested the climb and I still had them in sight, but with just a short descent to the finish line, catching them was going to be impossible at this point. So they ended up getting the two spots ahead of me. On that final climb, I had a normalized power of 294 watts for 20 minutes and 337 watts for two and a half minutes on that final pitch. That entire second lap was quite a bit slower than the first at two hours and 21 minutes, a normalized power of 270 and an average speed of 14.7 miles per hour. Part of the reason for this is simply the nature of the race. The start matters a lot. So a lot of riders just plan to go very hard at the start and then slow down a little bit throughout the race, although hopefully not slow down too much. This was my plan and it's in start contrast to the plan that I used at the Unbound 200 last year. The difference with this race is that being that it's a mountain bike race, pacing yourself appropriately at the start would mean that you get caught in bottlenecks in the single track sections and lose massive time. However, as I said in the second lap, I was still catching riders. So even though I was fading, I wasn't fading quite as much as the riders around me. In the end, I ended up finishing in 30th place out of the 84 riders in the pro men's field and 22nd out of 35 in the lifetime Grand Prix. This is not the start to the series I was hoping for. Last year, Seattle was actually my best placing in a Grand Prix race with a 17th overall in the race and 13th out of the Grand Prix riders. So I had high hopes, but I think this is where it's important to have a little bit of perspective before just writing it off as a bad race. Last year on the shorter course, my normalized power was 306 watts for the whole race, which took three hours and four minutes. If we look at the first three hours and four minutes of this year's race, my normalized power was only two watts lower at 304 MP and I still had well over an hour left to race. Given this had the race been the same distance this year, it's very likely that I would have had a higher normalized power this year than I did last year. My normalized power for the entire race ended up being 294 watts for four hours and 28 minutes with an average speed of 15.4 MP. This is nothing new for me. My career has fallen during a period of massive growth in U.S. off-road racing, particularly on the gravel side. And while this isn't a gravel race, it is part of the lifetime Grand Prix, which has brought out the best riders in the U.S. and international riders as well and absolutely raised the level of competition. It really does seem like every race I compete in now, I'll do a higher power output, a higher average speed, a faster finishing time and a worse placing and I'll be honest. It can be frustrating, but I'm grateful to be part of this period of growth in U.S. cycling and I'm determined to be competitive. I want to take my audience along for the ride this year in the lifetime Grand Prix, whether it's a good race or a bad race or a good race for me, but not a great race on the results sheet, which is kind of what this one is. I had great legs and great power numbers and with the exception of that start, I thought I rode a good race and at the end of the day, I think that's all we can ask for. I do think that when evaluating a race performance, too many racers judge the success of the race based on how they did relative to others as opposed to relative to their past self. Of course, the goal in a race where you're racing other people is to win or to place high, but you should also strive to ride better than you have previously. If you're doing that, then you're succeeding. All right, we get it. Jeez, man. Had I known this video was going to be so freaking sappy, I would have just turned on the notebook instead. Thanks for watching. If you want to follow my racing closer, be sure to check me out on Instagram. If you enjoyed this video, be sure to give it a like, subscribe and share it with your cycling friends. I'll see you in the next one.