I have played a lot of Dragon Ball games on this channel, most of them being really good. Unfortunately today we are playing a Dragon Ball game that IGN itself gave a 3. That's right, today we're playing Dragon Ball GT Final Bout, a game that some consider to be the worst Dragon Ball game of all time. If you like these videos where I play through all Dragon Ball games, definitely make sure to like this video and subscribe to the channel because I got a ton more to do, but for now I want to see what Final Bout does so poorly that IGN decided to give it that little of a score. Oh, what is Cell doing here? Why was Bro stating in the limelight like that? Dude, I wish, we need to bring this back, man. Why, animated intros? Yes. Oh, why are you laying one out on Goku? Like Xenoverse hasn't animated intro, but it's like calling all proud warriors like, no, give me this. Damn, no, he just had the hands, there wasn't even any fight choreographies. Oh, that was a clean transition to Kid Buu, wait a minute. I was going to say, I was a clean transition to Piccolo. It was. I don't know what. I don't even think Piccolo is in this game. Wait, is he not? I don't think he is. I'm trying to remember if he is. He can't be in the intro, not in the game. All right, so all these villains around Vegeta decides to friendly fire on Goku.  I forgot this was a GT game. Dada, I'm just going to throw this out there. I now know where the budget went for this game. Actually, this probably was a lot. Yeah, dude. That was a long intro, man. That was a long inch. Damn, just for the game menu to look like this. Press select for sound menu. Can you go ahead and press select for me? It's not working. No, I got it. You want your background music on one or zero? Probably zero. Okay, that makes sense. So do you actually think Piccolo is not in the game? Oh, he went Super Saiyan. Whoa. This game is awful, man. Yes, it's look at this is like if I was a caveman, what I would have drawn on the walls. Yes, you are correct. You just want to battle first or do you want to do a tournament? Let's let's battle. All right.  This game, this game will put some real hair on your chest, fellas. What? This is the roster? Two of them are Goku. Oh, Piccolo is in the game. Okay, he made it. He made it. He made it. Well, I feel like I have to. So yeah, you did.  I guess you know what? I'll shock the world. Nobody expected I pick Frieza, but let's try to recreate a little bit of the Namek saga. You're Frieza.  Wow. They got at least they got unique dialogue.  I don't know. Oh, man.  Bro, I'm kicking. Stop. Stop, bro. Stop. Whoa, what? You're not a play. Take my death beam. So how do you move by the way?  Can you not dash? All of my moves are tick. Oh, wait, I can dash. Whoa, what? Bro knows how to play the game. Oh, that's my. Okay, so this is. This stage is sick, dada. Oh, wait, are you dead? Oh, wait, we need flashes. I can't see your key blasts. Wait, why? Oh, that's like a little subterfuge. They said it. Okay. I need to learn how to block. What do you? You're not ready for this one, dada. I probably am not. No. Well, I didn't even have a chance to dodge it. Just decided I was dead. Our jumping combos. Hail slide. Oh, wait, dada. Nope. There we go.  That's what I thought. Get back here. Come on. All right. So I'll admit, I probably don't know how to get up there. Yeah. Stay up there. See what happens. Am I flying? No, no, no, I'm not. How do you charge on it? You know, I don't know. Did you figure out? Oh, come on. Tell me.  Look how long this takes. Oh my God. 15 years later. Or tell me, bro. 20 years later. Oh, tell me, bro. How do I? Ah, what's up? Boom. Get back up in here. You want to help for me? Yeah. Yeah. Can I fly? Just press that one. Oh, I see. What kind of ultimate was that? How are you? Like, how have you not actually killed me? That's not what I want to know. Hey, are you going? Wait. Whoa. Is it like grenade? What? What? You killed me. Ignore the effect that my eyes are open. They just couldn't program that back into the speed, but there is not enough. What? What do you say? I like this piccolo. Oh, game over.  I was going to say, how do we turn that off? M-O on, off training. Okay. So I have to back us out all the way to the main menu. They didn't put sound and options. I can't go back to the main menu. All right. What are we doing, bro? You don't want to pick Vegeta just for the one time. You want me to pick Vegeta? Yeah. Yeah. Let's do a little go. I don't know why you want me to pick Vegeta. Why? Are you insinuating something about this done now? I maybe. I like, listen, even I don't super. This should be a good fight. I will have to. They have one guy voice act the entire game. Yeah, that's. Oh, they got me in my GT outfit. This must be GT final bout or something. What was that, bro? You don't even know how to fly. Neither do I to be honest. Whoa, you have to. Whoa, whoa, whoa, whoa, whoa. Bro, is that a play? I got the game FAQs, brother. You actually, you're on game facts. You're on game facts. I knew it. Boom. Jumping combo. Whoa. I see. I know. I know what you got. Apparently you do way more damage than me. I was going to say, I think, I think maybe my handicap is already up.  Why do you do so much more damage than me? I don't know. Do you think I have like baby mode on or something? Are you dizzy? What is MTO? What is that? I don't know. What's demo? I'm trying to do like a cool move, bro. I need to open up game facts.  The game facts if it's correct. Are you good? That is not final flash. That was like three things. That was final flash, dude. You missed, by the way. You missed. Damn. No, I didn't. Oh, we're both. Hey, Dotto, I'm going to say something incredibly controversial. So you might have to censor me for this. Okay, go ahead.  This game is trash. Why do you do so much more damage? Wait, what? Why'd you come up here? Damn. Hey, at least go close his eyes. Dotto, you want the game FAQs, bro? Yeah, I'm opening up game facts here. I'll give you the link. All right. I got game facts. You know what? They even have a cheat code here for all the characters. So I'm putting that in. I doubt it's all on you, man. So right, left, down, up. Right, left, down, up. Right, left, down, up. Right, left, down, up. It is. No. Later. There we go. We got it. Do it. All right. We're in. Wait, I enter the Konami. Whoa. Right. Whoa. Like tripled. Look at that. We got three more Goku's. All right. Now you seem happy. What? This is horrible. You know what? This is my Goku. Whoever this guy is, that's my Goku. That's your Goku? Okay. All right. Now watch this. Down, away, towards, triangle. Dodge. Oh, but can you dodge this? Yeah. Oh, wait, sorry. In fact, how about I hit you with this? Oh, wait. Oh, I'm walking up. Look at me sauntering.  Can you handle the times 10? That's what you want. You want a beam class? I'll give you a beam class. Do you think this game has that? Yeah, watch this. Let's beam class. I'm not going to get a chance to respond. I can't respond to this one. Damn. I'm up. I'm doing a more powerful version, too. Did you? And now I'm going to do it again. You're doing it again? Now I'm tired. But this game is trash. How do we respond? It comes up as a says counter, but what does it mean?  Why'd you throw it on your head? I'm back into you. Why do you use so much damage still? Yeah. What are we going to do? Shall we continue? You want to retry? Yeah, I do want to retry, Dotto. I do want to retry. Thank you very much. Downup circle. Damn. What a devastating chain. Oh, she got combos. It takes forever in a day to dash up. I'd rather just walk. What was that? What are you doing? Spirit sword. Okay. Oh, brother. How do you counter this? Counter it. Do it. I've tried a way down towards circle. Damn. Yeah. Damn. Yeah. No, I'm too. I'm too. Oh, that's one one. That's one one. That's one one. That's one one. That's one one.  Dragoball is crazy. No. What? If you're just gonna let me do it, then that's cool, too. I was trying to not let that happen. Dude, the Goku model might be one of the best in the game. They definitely went in on this model. Why am I? I'm trying to go down, but my toes are just pointing in and out. There we go. Finally. That took all of your time. How did you go up?  I just pressed up and I went. I like how that was a devastating combo is you like punching me in the stomach 70 times. You know what? I need a new character. Majino is not even strong on this game. Game over. Why did it unlock like every Super Saiyan of Goku? I'm not even sure what's different between those two, by the way, because I don't know. Is that two or one of those two? I don't know. I've never been good at telling Super Saiyan two and Super Saiyan apart. Try that one. The one on the left. This one looks more Super Saiyan two. Show me the results of your training. Oh, let's see charts. Excuse me, Goku. Excuse me, Goku. Wait, is it the same person? I think they had one guy do every single voice. There's somebody on this planet that can say they voiced every single person in Dragoball. All right. So I'm just Super Saiyan, Goku. Hold on. Let me pull up the fact sheet. Now watch this. That's pretty good. Oh, now we're getting some dynamic angles. I love it. I can't even see where you're at. It's so cool. Boom. Boom right in the sternum. Boom right in the sternum. Boom. Oh, I missed. Put that beam away. What are you doing? What is happening? Oh, I'm charging. Damn, are you good? You doing a fatality or something? Damn. So you found your... Oh, that was sick. Yeah, you thought... Wait, why did I go slow, Moe? We're finally final-bouting. Yeah, we're final-bouting. Oh, I just accidentally jump-kicked you. That was kind of... That was our best match so far, even if I spent... Damn. Whoa. Whoa. What was that? Why bro showing a little leg? Let me get a rematch. How many Goku's are actually in this game? Super Saiyan Goku? Little Goku? Normal Goku? Little Goku Super Saiyan? Damn. Bro, did a million damage. Bro, I wasn't even done yet. Oh, look at my dot. Okay, well, that was embarrassing. No. No. What is that? No. Final bout. Final bout. No, this can't be... That is the final bout. You're tougher than I thought. I shouldn't have been so careless. Dude, what is Trunks doing, bro? I shouldn't be so careless. Is he like, pick something up that he didn't drop? What is that? What is happening? Not only one of those keyblasts could ever hit. Why am I facing away from you? Why can't I hit you? Dude, this is not... Oh, no. Oh, no. Oh, no. It's a movement-based game. It's whoever has the momentum. I blocked it. Harry. Harry that. No. No. Harry that, Goku. I'm sorry. What is why he's throwing it back like that, Trunks? That's what I'm saying, dude. Look at me. Game over. All right. Give me one more first than two. I want... There's like a million Goku's in this game. There's two Trunks'? Wait, yeah. There's three Trunks. Who needs three Trunks? Where's the Modge of a Gena pick, man? All right. I got you with some variety here. Okay. A little variety hour. You know what? If you're doing... Honestly, either of those two work for this. Now, I learned my lesson. I'm going to go ahead and do a little research beforehand. There we go. I've got my one version of... You should know more than anyone else that you can't defeat me. Now, let me show you the force of my true power. Okay, wait. I like Cell's voice actually. I actually thought Gohan was going to say the other thing that he's famous for saying in that picture. Whoa, look at Gohan. Cell is like nine foot five. Jesus. Look at the way I walk back. I'm scared. I blocked that, by the way. That was too easy. You think I would walk this? I don't know what I do that makes it do this. I don't know either. What's... Wait, am I... Am I seeing my explosion? What are you doing? What are you doing? Wait, why Japanese? Wait, yeah. That was Japanese, right? Oh, wait. You can counter. Wait. Nita, look. Hit the counter button. Hit something during the counter window. Look, right there. I did. It didn't do anything. What does it mean counter? Is there a beam clash? Dude, look like a walk. Wait a second. Let me approach you. Hang on. Yeah. Wait. I know. Wait, wait, wait, wait. That's actually cool. Wait. Okay. Hold on. Hold on. Oh, no. Oh, Matt's trying to go to beam clash in final bow. Oh, no. Don't you ever do that to me again. Hey, I'm late. I'm different. Dotto, are you ready? Wait, are you going to fire a beam at me? Do it, Dotto. I'll tell you what, Nino.  Oh, what happened? Is there no counter window? Try me again. Try me again. Try me again. Oh, no. Why are you built, bitch, bro? We're trying to beam clash. We need a little more time if you power up. Damn. Bro, that's like a business man. I'll tell you what. You get back up and power up and you... Oh, that's the best way to create any Dragon Ball game I've ever seen, man. I know. I use a beam so I could show you what I'm capable of. You better get it. There it is. I got it. Surely I got it. Boom. Get that out of here. What? Yeah. I slapped that away, huh? You didn't know about me. Whoa. Wait, did you just... Is that 3D fit? What? Whoa. We were barely playing Final Battle for real. Now, granted, I don't know how I just did that because I can't repeat it. Oh, I just repeated it. How? It's R1 and X, but granted, I can't do it again. For some reason, I can't do it all the time. What are you doing it now? Whoa. Did you see how I dashed back from that? Oh, I'll tell you what. You want to punch? I'll punch. Oh, oh, oh. Give it to him. Boom. Now, counter this. I was just about to do something so cool, bro. Why are we facing? I don't know, but it looked kind of like an anime. I'll take it. Here we go. Do it. I think I got it. Surely I got it. There you go. Here's your chance if you want to try and stop my mesenko. Oh, Neto, you did it. Why? Why? Hey, you take no damage if that goes through, though. Do it again. Do it again. Do it again. Do it again. Me? You want me to do it again? Decide my fate right here. I'll do it. I'll do it. Oh, no. Watch out. I was too close. You sucker punched me, bro. That was my bad, bro. That was one-to-one, right? Yeah, that's one-one. This decides it all. Oh, I thought I grabbed your legs there for a second, but it was you doing it. You thought Final Bout was crazy. Oh, you just slapped. Okay, wait. Boom. There's really a game in here somewhere. Hang on. Counter it. I actually do want to see you slap it away again. Mesenko. Boom. Okay, okay. You think you did for, huh? Oh, we're cut. Why? Why are we? Why is there so much like awkward standing and stuff? Because most of the game is awkward standing. Oh, no. Oh, there's got to be a counter to this. Did you counter? I tried. Oh, God. Oh, I'm alive. Do that. I dare you to. No, bro. No. Bro, please. Bro, I do your explosive way. Do your explosive way. Do it. Are you for real? Yeah. Do your explosive way. I'm going to counter it. It's the top three buttons, right? Yes. You're done. You're done. You're done. You're done. All right, here we go. You're done. You're ready? You're getting real close, bro. You're getting real close. Are you so close? Oh. Bro! What changes it whether it does the close or not? I think it's the distance. Why does he sound like it? I don't know. I need a clash. I need a clash with that move. What if I also do an explosive way? Oh, I know. I know. I know. You tell me to get far away from you. Yeah, go ahead. Do it from there. And I'll mash the top three buttons. Oh, here we go. Here we go. Do it. I'm mashing. I'm mashing. I'm mashing. No, sale. What?  That's actually really cool. Okay. I won't even hold you. That's pretty sick. Okay. But if they wanted to make a really anime, they would have had me scream and do an explosive wave there. How do I, wait, do that again? I want to see what knocking that away looks like. It's, wait, what do I do to knock it away? It's this, right? Oh, hold on. I bet you won't do it again. I probably won't, man. I'm all out of key, bro. And it takes 10 years to charge anything up in this game. Why are you late on the ground, man? I'm chilling, bro. I'm not doing it to me again. Buried. Why do you have to be? That is so in the comments, definitely. I just know what demo and MTO meant, right? Oh, yeah. Somebody in the comments has definitely played final about all their life. I want to see what knocking this away looks like. Surely that's got to be sick. How do I knock away an explosive wave? You don't. Well, man, how'd you enjoy final bow? You know, I actually think with a little more time, I could really come to appreciate this game. Well, I, I sure hope you can, because I think we're stuck in final bow right now. Wait, what do you mean? No.


Okay, and, hello everyone. So after a short break, we are back. So we are back for another episode of the Sussex Vision series. I know that for some it will sound a bit redundant, but let us have a quick word to remind you that this weekly series of talks about vision and visual neuroscience are part of the world right now in the city. WWM is a platform for neuroscientists in various fields to exchange more recent work. And I have to say that up until now, it has been a great way to exchange with our peers while traveling to conferences was still difficult. So we hope that this kind of talks will remain a regular practice in the future. I mean, only if the future will tell. Anyway, I'm Maxime from a PhD in top evidence lab. And today, I'm very glad to receive Maxime and Josh from IST Austria, the Institute of Science and Technology in Vienna. Maxime started studying astronomy at the Ponticica University at Catalega de Santiago in Chile. And later he moved to Tumigen and Munich to finish a degree in biochemistry. He then obtained his PhD in a laboratory of Alexander Bosch at the Max Plant Institute for Neurobiology in Munich. And then he worked as a post-doctoral fellow with Marc-Hübsmeister and Joshua Sense at Harvard University. Now, Maxime is an assistant professor at the Institute for Science and Technology in Austria. And there he leads a research team focused on understanding the neuronal basis of visual transformations and the role in his 14 innate behavior. So we're very happy to receive him today. And we are really looking forward to hear about his parametric view on vision. Hello, Maxime. How are you doing today? Hello, Maxime. Thank you very much for inviting me and giving me the opportunity to give this talk. First, shall I share my screen or how shall we start? Don't use yours. You can go ahead. Next, let's share the screen. Can you tell me if you see everything fine? I see your presenter mode. Okay, it should be a one-screen. So, hello, everybody. Oh, I don't see your full screen. You don't see my full screen? Let me see your presenter mode. Let me see. Again. What about now? Better. Thank you. So, hello, everybody. It's difficult always. I'm still not used to talk to a non-audience. But I have to start by saying a few words on something that's completely unrelated to the work that we're doing in the lab. And it's because of what's happening on a world stage and I know from many people that are affected, have the families, including in my lab, where the morning discussions is, how is your family doing? And they're still alive. So, with that, I would just say that please support everybody who is now in going through this hardship. And let's, as somebody who is much smarter than me once said, let's hope for genuine peace and the kind of peace that makes life on earth worth living. And yeah, I just wanted to say those words because they were important to me. Okay, let's go jump from here on to science and I will try to be as excited as I can given the situation of something that I find really neat that we've been doing in the lab in the past couple of years. And it's a study that combines theory and experiments and some new low-tech development that might be useful for the community. And so the story that I'm going to tell you today is about basically reflected in this picture. And I chose this picture, one, because I think it's beautiful. Second, because it's my dog.  So you can clearly see the horizon. You can see the difference in statistics on the sky and the ground and the luminance. And so the entire talk today will be to try to address to what extent these features, these statistical features might have shaped the efficiency or the way our visual system might encode our panoramic surround. And I'm going to start first by introducing the team who did the work. This is Vivianc. Let me see. A pointer, please. What? Vivianc, Gupta, Victor Miljasi, Olga Simonova, and Jan Sva. This work is just recently published in Bayer Kai. So if you're interested, you can look at all the details over there. I know that this lab is well aware of how the retina works, but I don't know if all of the audience is. So I want to start giving a very quick introduction on which part of the visual system we're working on, which is the retina. And so the vertebrate retina is divided into, let's say, three gross stages. One would be the photoreceptors. You have three types of photoreceptors in the mouse retina that sent light and sent this information to a very complex circuitry of hundreds of different cells. But roughly you can think of it as they have some direct bipolar cells that excite the retinal gangon cells and a whole set of different amicron cells that are inhibitory and sculpture the information that comes from the photoreceptors to send a particular information to the brain via these retinal gangon cells. The study today will focus on the response properties of these retina gangon cells in the retina, basically the message that is being sent from the eye to the brain. Why? Because we think this is a good relay where we can perhaps start looking at differences and adaptations to the statistics I was mentioning to you at the beginning. And so what about this retinal gangon cells? What do we know about this? Well, there are a lot of different types of gangon cells. I see a very beautifully shown in this study by their morphologists. And so you have like the small ones, the bigger ones with asymmetries. You have very big ones and so on. Each of those can be genetically defined, you can also physiologically find them by the response properties to light stimulus. For example, this would be a local edge detector. This might be more sensitive to color, opponents, others might be sensitive for motion, et cetera, luminance, whatever you think might be important to basically divide the visual channels into how people think of it streams of information. This idea, in addition to the fact that each of those independently piled retinas, you can depict here in one beautiful review from Rich Maslant, is that each of those perhaps this small led cells will basically have the receptor, the genetic fields organized in such a way that the uniform basically sample across the retina space. And you have that also for the bigger ones as here. Let's say this would be the blue ones. And they're also basically on the genetic sample. And the idea is that overall, these cells will send each spot and position in space will send information to the brain having all possible streams of information there to be read out. So this is one way of seeing this entire story. But as I was telling you before, there are strong asymmetries in the statistics of natocene. These were studied in more detail recently last year in some work from the oil lab on from Hiroki Asari where they tried to basically match how the mouse and now all the work we're doing in mouse, but exactly how the mouse would perceive the world and how the statistic will change. And as the image that I was showing you before, what you see there is that there's a strong change in luminance if you're looking at the ground. There's a strong change of a gradient of luminance if you look at the sky as depicted over here or over there. And there is this very crisp line on average that you can find on the horizon. So we said if the system should efficiently encode the natural work, perhaps it takes this into account to shape the way the retinal ganglion cells might be relaying information to the brain. So the question we were asking right now is how all of those channels would be modified such that you efficiently basically match those statistics. But this is kind of a challenge because I was telling you that each of those different ganglion cell types, for example, the small ones might be important for edge detectors. This one for direction selectivity and so on. But we don't know this is basically matching some functional response properties of a particular type. So we said, is there any, let's say, more general way of describing it? And let's go a little bit old-time about how people were describing those cells and they were describing by some aspect which would be their center-surround receptive field. What is that? What is the center-surround receptive field? Well, you can see it over here. The idea of it is that the center of the receptive field will be the input that it gets directly to the energetic field, whereas the surround, which is basically depicted here by the bigger donut, will be the information that these cells integrate via this amachrine inhibitory surrounds, shaping the way these cells respond. The most classical way of doing it, this would be one example of one cell. This would be an off-center depicted here if you stimulate on the energetic field. It spikes, whereas it has a very strong antagonistic surround. When you have an annulus, basically the only exciting of here it will spike when it's on. A different way of describing this phenomenon would be using spatial filters. And that's what you see here. Basically it's for the same cell. You have here the blue part, which would be blue, meaning it spikes when the light goes off and has a particular shape. Whereas the surround of this would be red and that would be depicting when the light goes on, will be driving the cell most strong. And we will try to go use this feature of each of the same ganglion cells that we have measured to basically ask to what extent they may match the statistics of natural systems. And this is something that has been done previously. And all our work is basically inspired by this work of predictive coding done by Srinivasan, Laughlin, and Dubs. And we implemented this normative model and expanded it such that we can actually ask the questions. I'm going to first describe how we did it and what it can. So perhaps a way of thinking of our work would be a refreshed view on inhibition on the red. And to start describing this model, I want to first describe the fishing coding theory. An efficient coding as formalized by Laughlin in the 80s is an intuitive description on how the stimulus has to match a response. And assuming here this particular box that you see over there, it will have a distribution of intensities. And so assuming now stimulus independent additive noise, an efficient way of neural coding is to maximize the stimulus response relationship that makes each of the possible response equally like basically if you think here, if this would be the all intensity and this would be the response properties, you would like to have the sigmoidal curve so that it is efficiently relaying the information sensory neurons to the brain. One implementation of this very classical idea would be the predictive coding idea. And as Barlow said, this perhaps this is a very nice way of putting it. This could be a neat packaging of information. It's like it's the best way of how the system should be encoding information such that you use the resources efficiently. And so this is basically the essence that the first step an implementation of that would be the predictive coding idea. And let's see, this would be basically the same thing I was telling you before you have the box, you have the distribution, it matches the stimulus response curve. Now imagine you have a different point where and this will elicit a different distribution of inputs. And here the stimulus response curve is not neatly representing the stimulus distribution. And what we would like with the predictive coding hypothesis to match is that we shift this distribution such that it matches again, the intensity and the stimulus response curve to be more efficient. How would you implement something like this? Well, first you need to know that natural scenes have spatial correlations meaning that the statistics at nearby points in space are similar. Then you could implement it with spatial filters. For example, in this center, another center surround filter, in this example the center would encode for the luminance of the scene whereas the surround will encode for subtractive prediction of the spatial correlation that you would expect to see based on natural statistics. And so one subtracted by the other would be basically one, the measurement subtracted by the prediction would be the predictive error and that's what basically we would like to relay on. And one important point here is that all of that is dependent on the signal to noise ratio. So if you want to tune it, you have to shift the signal to noise ratio and it will become from one to the other such that it becomes more efficient to subtract. And so one way of formalizing this would be having a very simple encoding model of a retina ganglia cell. This is probably the most simple way of representing what happens in the retina. You have an image sent by photoreceptors, you add noise, convolve it with a spatial filter and you see what is the output. And now the whole game that we're playing here is to optimize numerically such that we reduce a cost function would be basically reducing the fine rate. So I was telling you that noise is an important aspect of this and that by changing the noise you would change the properties of the filter if you optimize. So what about noise in Natus? One important aspect is to know is that there's a ton of intrinsic noise in cone photoreceptors. So for topic levels, it seems to be that is three orders of magnitude higher than what you would expect from broad vision. And so you can think also of the change of natural statistics here on azimuth elevation of luminance and contrast also some sort of signal to noise ratio across the natural panoramic scenery. And so this is basically the value that we're going to use to optimize the filters. In addition, we realize that there is this very clear and sharp change at the horizon which makes a very clear asymmetry which might also affect the way those receptive fields might be shaped. When you run the simulations what you can see is that three aspects work. One, if you see here if you change the signal to noise ratio what you see is that the center surround relationship the surround changes. The stronger the signal to noise the stronger the surround. Another aspect is that the center size will change. The stronger the signal to noise the smaller the center. And another prediction is the symmetry. Actually, the stronger the asymmetry you will have the more asymmetric the receptive fields will become. So assuming that this predictive coating hypothesis is implemented we should be able to see some of these aspects in the written response. And just to summarize the same finding or the same hypothesis of prediction in a simpler way would be to think of it as ground the sky the higher the cells are looking like or the most ventral cells that look at the most higher point in space should have a stronger surround whereas the surround should decrease and the centers are increased whereas at some point in the horizon you should see that this asymmetry. So now we have a model we have a prediction but what would we need to basically test it and for tested we would like in one particular retina to sample as many possible cells in ideally the entire retina. And we didn't have a two-photo microscope to use for these recordings. MEAs have different issues if you want to exactly know what you're recording from. So we thought perhaps we can we had a neat idea perhaps we can use it to solve this this issue. And these need ideas extremely simple.  So if you think of what has been done before it's particularly here by Tom and during his postdoc and a beautiful work where you can see character as response probably across thousands of different cells with two-photo microscope imaging and some issues on wrinkles and so on there were scanning a little position in space across a retina. And so required different retinas to have let's say an overall picture. Other people have done it with a little bit more with bigger areas this also works but it was still small if you were aiming to image let's say 40-50% of entire retina. And so short story long story short basically we could figure out a very simple way such that we can start imaging across large regions of the retina. And what you see here is just the response of particular retina which we can later tie on and what the size of this is around 1.4 millimeters. And so what is the trick? Why can we do this so easily? And the trick is remarkable simple. And so it's based on that most of the photoreceptors in the cone photoreceptors of the mouse are EV sensitive. And they have the absorption spectrum around basically 300, 370, 380. Normally when we're using two-photomicroscopy we use a very long wavelength to excite the system. However, it's so much energy that even the exponential decay that you don't see here will also activate. And so it's like if we're already activating why not just using a red indicator and shift the wavelength such that we can start imaging at longer wavelengths with epifuricens? And what we just do is a very simple epifuricens microscope which allows us to basically record for very long periods of time very large fields of view. And I'm going to try to convince you that for ganglion cell recordings stimulating UV light it makes it is a great plus and it's very easy to adapt. So how do we approach this? So we used a line that is a genetic line expressed in gearchable specifically in ganglion cells. So all of the cells that we expressed here are ganglion cells using the V-glute-3 line, V-glute-2. And we basically know that all of the cells are RB-PMS phosphorus meaning that they are ganglion cells. But this line even though just because of genetics laves 40% of ganglion cells in a very specific way. So for example, this is a staining for alpha cells. Just one type of alpha cells seem to be in our line. And we can image those across a very big field of view. So this would be for example, one example, Retina what you see here depicted by this orange box would be one of the fields of view that I was showing you before. And we can basically tile image here. One, the other one too. The third one would be here in the middle. And then we can basically have a very large perspective on retina coding. And that's what we need because the difference that we are looking at are spatially at a very large extent. So can we look at response properties? Of course, we can basically look at single cells. We see the response properties. The other interesting thing is that we can record for very, very long periods of time if you record on one field of view. So we have recorded up to an hour and a half if I'm incorrectly. I mean, there's some bleaching coming up but the responses are still there. And in all of that it is roughly 20 times cheaper than a classical two-photo microscope and it spits at least 50 times more data for retina recordings. So if you make the calculation it's perhaps worth trying it out. So what about response properties? Here's some classical response properties I've been seeing. Like this is recorded for over here. You see a direction selective cell simulated with a bar, a bright bar. You see the on-off responsiveness. This is basically like in alpha cells responding disregarding the orientation of the stimulus. We can basically map direction selectivity and ask which are direction selectivities that we see and we basically see the cardinal directions. But interesting, we can in one retina reproduce some very neat findings that have been done in the past. For example, this work by Savite All where they've been looking at how the direction selectivity is mapped and this is just looking across the retina. And this is looking at the same data in one of the retina beta where only we're stimulating this just to see if our system allows us to reproduce prior results. And this is not a quantitative statement but it's more a quality statement that it seems to be if you look at the crosses here and the crosses here that they were changing the orientation depending on where they are in the retina. So it seems to be that this at least allows us to reproduce this type of data. And for chirp responses, of course, this is also a classical way of looking at how the system is representing how much the system representing cell that and even though we have 40% of the cells and we believe that they're always the same 40% and genetically find once we can very easily classify them based on these response properties only. So we have on, off transient, on transient we have suppressed by contrast in even we see some of the some of responses that we haven't been able to match with prior results that have been published. One thing that is a beauty error here if you like to compare and we realize after the fact normally people use the frequency chirp first and then the intensity one such this is basically inverted. Okay, so I hope that I can convince you that at least for the classical response but if we can match with this very simple approach redefine or reconstruct not reconstruct reproduce previous results but we're not interested here in studying chirp responses or direction selectivity per se we're interested in looking at receptive fields and by looking at receptive fields we also need to have a good assessment of the center response and the surround which is tricky in a way that is parametrizable. And so what normally people do is basically use white noise stimulus to basically reconstruct those receptive fields. The issue with white noise stimulus is that if you have big checker you don't have very good spatial resolution but you have a very strong response then if you lose small checkers you might have very good spatial responses but many of the cells don't respond at all because you need to record for a very prolonged period of time. So basically we said why not combining both ideas and having a big checker that's shifting in space and that does that if you are if yourself receptive field if your checkers are similarly the centers around the same time by shifting them sometimes it will be exciting the center sometimes then surround and on average you will have a nice representation and that worked remarkably well. So now we have a method with our system to start reconstructing spatial temporal receptive fields in a very simple way and so it doesn't matter where exactly the the checkers are at the beginning because they will be shifting randomly such that at each point they will have they will be exactly on top of the center and the size of this of one of those checkers was decided such that too much roughly the size of the smallest retinal ganglion cells. Okay using this approach we actually could reconstruct a few of receptive fields. We basically image and we can tire retinas let's say nine we have around 30,000 spatial temporal receptive fields and in here you can basically see some have this time of I don't know double or like some others don't have surround so now those here have a very strong surround and some of that might have an asymmetrics around you have whatever you want you will find there. The nice thing is that the signal to noise is good enough such that we can use very simple difference of gaussians to fit them and parameterize them because after when when you have 30,000 cells it's it's it's you need to have some unbiased approach. And so over top you see basically some example data receptive receptive for from some of the cells that we recorded and in the lower part you will see the fit. And if we make an R2 if you basically try to see what's the error it is and very much most of the cells seem to have a very good fit. And and such that we can start analyzing using this data set to analyze and ask if theoretical predictions actually hold. And so let me remind you what the theoretical predictions were. And so we said that the relative surround will depend on a relative center to surround strengths will depend on the position on the dorsal ventral axis in such a way that the more ventral you go the stronger the surround is. And and then the other prediction would be that the center strengths will be diminished so the more ventral you go the smaller the the sizes of the center will be. And the other prediction that we have is that there must be a particular position that is exactly there where the horizon should be where on top of the horizon you will have more signal to noise on the bottom less. And so you will have a very strong asymmetry path. So let's see what the data says. So this was our prediction. We use this receptive field parameterization. And now we can look at the responses that we measure. And so this is on top. You will see all cells from nine retinas basically aligned into the same coordinate axis. And what you can see is a gradient from ventral to dorsal basically I'm sorry for the for the relish surround strengths and what you can basically mimic here from a little more purple purple to green is that the strengths will diminish with respect to the position. So the more dorsal you come the less surround you will see. The sizes also for all cells here start to be bigger. The the more dorsal you become you come in this dorsal ventral axis. And when you ask for the surround how strongly asymmetric to surround is you will see a very very clear stripe at this position. If this would be the optic nerve these are the cells that we recorded. You will see a very strong asymmetric streak. And this is if you plot this in the not dorsal ventral axis but nasal in basically on the horizon you don't see any correlation. So it beautifully you can basically see that relationship in one single retina. And this is what we did here. We basically cluster each of the receptive fields of all cells in a particular bin and inverted the on one because we just want to see the relative change from from centers around. So on off spatial filters are all grouped here at the same in the same retina. So this is the response of one retina. And what you can clearly see is that there is a strong surrounds in the upper visual field. When you go dorsally if you perhaps you can see it the the centers get bigger. And over here you have the streak of very strong surrounds that are asymmetric. When we look at this we basically stained by by the S option. And then we can actually see exactly where we are in the retina. And we make a little box and ask how asymmetric these cells are. You will see how how strongly the effect is though I'm sorry. So this is exactly where the streak is. And then most and if this would be this the number of cells that are basically pointing ventrally and would be here strongly asymmetric where if you go a little lower it's become less asymmetric if you go higher in this in the cells that are in this box. Basically, of course, you might have some bias there depending on how you're sampling but overall there is this this this directionality of asymmetry is gone. So if we reconstruct basically this streak and try to put it back into visual coordinates and ask all the cells that are actually very strongly asymmetric where the position and where they're looking at you basically seek the horizon as what we would have expected from before. So now I can sell you that on population of cells we can match our predictions. So the surrounds get bigger the more dorsal you get the strength of the surround sorry the center scope bigger the more dorsal you get the surrounds got stronger the more ventral you get and you have an asymmetric scope. So then we would like to once to ask so is it just because of particular neurons that are much more asymmetric it's like a population of cells that might be up here and exactly on the horizon or some cells that for whatever reason have have a very strong surround that is varying and that will basically overtake the entire responses. And so we said okay what way could be functionally classified as cells to ask if it's across cell types or is it just property of a particular subclass and so using the temporal filters that we had we started saying okay perhaps we can use the central response the center temporal response to classify cells and we did so and we basically classified them into 10 different types which you can see here on the left here is time in color code would be basically the temporal filters and you see that there are different groups of responses some are off some are by physics some are on some are more sluggish some are faster and if we use those groups and ask where the receptive fields are it seems to be that so for some of them they tile more or less I mean at least from how we sampled it so there are not much overlap between those cells types of this particular cloud and whereas by others you have a strong overlap and for example number eight seems to be having many more cells and this is something as what we would expect but it cleans indeed a little bit and we can start asking okay for all those groups some of them might be cell types that are specific some of what we mixture of cell types how would those properties change across the dorsal ventral axis and what you see here is basically the same thing as we saw in entire population again for the relative surround strengths it becomes weaker the doors will be good the surround become bigger for I'm sorry the the centers become bigger for all cells and you have this very neat peak for most of the cells of the symmetry at the horizon so basically now I've been telling you three things first that we have a model that has certain predictions we establish a system to ask if these predictions hold and then we we tested them until now at least for for the problems we have used we see a very clear match positive match so you might ask what are the mechanisms that could be happening to make this gradient or made this differences so apparent and one thing that we've been thinking of we don't know if this is true is basically the the gradient of options so in the mouse visual system and in muskulus visual system you have three options rods that are uniformly distributed and you have S-opsins cones that are basically more strongly rendered there's a gradient on them as depicted here and you will have also some green options which are more more prevalent in the in the door cell retina and so given this basically gradient that you will see on the S-opsins it could be that for whatever reason you need more drive in these cells and to excite this around such that you can start seeing the differences across across elevation and so why is this interesting I think this is interesting because of the because it would be evolutionary a very simple way to adapt your visual system to the constraints of natural systems and there is evidence that something might actually happen and this is some old work and done by I don't know remember his first name but title slap and where he basically the look at the option distribution across different mice and interesting mice that live more in the prairie and they're basically a more open field which would be most musculos or this other field mice they have this very strong gradient of S-opsin but if you look at mice that are that that live in in dense forests these screens are basically either gone they have because there is no S-opsin or because they are as options uniformly and distributed and perhaps that could be a mechanism to tune the the visual system such that you match the response repeat in a way to efficiently encode the natural surround and one thing that I would like to discuss is that this will have also repercussions to how these cells might actually encode information and I was telling you at the beginning that we we as a field think very much of each of those cell types as ways of relaying a particular type of information would it be color oponency direction selectivity and so on and so this is basically perhaps nicely described with a cell that I've been working during my poster for a long period of time GRDC and now I inverted everything such that ground is in the bottom and top is would be the sky and these cells have been described to have an asymmetric surround and this asymmetric surround basically makes them become direction selective to very particular set of stimuli of course there are all the others other properties them that happen around but each asymmetric cell will have a particular set of parameters where you stimulate them and you will elicit some direction selected response and so now if you look at the responses of all of those cells suddenly all of the guidance cells that we measured will have a particular industry some particular symmetry and so here we clustered all the all the asymmetries for all cells so we're the pointing towards and what you see in collared would be the cells that are very strongly asymmetric and in gray these are not as symmetric but our match are basically our parameterization makes everybody have some asymmetry but but there might be a very little because there are two Gaussians so it might be very little asymmetric but all the color ones are where the symmetry is very strong it's just like what you expect from your GCs and interestingly in the ventral retina you see some peaks of asymmetry too but these are matching to all four cardinal directions and just for the efficiencies and don't seem to be direction selected cells whereas for the dorsal retina you have this huge peeking over here basically this is the streak that I was telling you before and so somehow we can think about this as okay this is important such that you you somehow compensate for changes in the statistics on natural scene or you have some new emergent properties and I have no idea how to interpret that but for myself it makes me wonder to what extent and this would be nice to have a discussion this global changes in asymmetry that seem to match some some efficient coding framework might have influence in how these cells actually improve information and last but not least some of the early works of responsibility in the superior colliculus that we have been trying to match basically from dragon who will basically say that you would have very strong upward bias in in in the superior collicus these are cells that were actually recorded from from inputs that are located positioned towards the horizon so these cells that are responsible because of how they were doing the experiments so perhaps you have this very strong bias because you also have this very strong asymmetry and in the visual system such that when you record for that you're all in and you reconstruct the spatial temporary filters of critical neurons in that position you see something very similar you see this asymmetry that is more on the upper field of view so to summarize what I've been telling you so far is that the global architecture of receptive fields in retina matches what we expect from some efficient from our efficient coding framework basically depending on what you expect from the signal to noise level and natural statistic at photopic levels you will see some differences in the receptive field structure you can see that in the model and you can see that in view and this is basically what I told you so far we in this work Victor Milanski implemented this normative model and expanded on the predictive coding framework such that we could have three different predictions of center sizes centers around the relative strength of centers around and also on on the asymmetry on the horizon we develop a new method that I think would be very useful for many questions it is very cheap the first time I implemented it cost me 500 bucks and if you want to do it fancy it will cost you let's say an order of an order of magnitude a little more than that and it's very robust and allows you to have a lot of data for the effort of doing it and we use that system and verify our model and basically it seems to be that qualitatively all prediction matches and at the end we see that also that this goes through all ganglion cell types at least as we cluster them and here I would just like to open the discussion of what it actually means if you have gradients of the change and in a way that might influence the way they are encoding the natural scene and the take home summary is basically that the retina implemented some graded sunglasses such that in the upper piece of view you have a stronger surrounding basically whereas in the lower field of view you don't and the last thing I have to thank many people that have worked in this project particularly I mean and Vivianca Olga Victor and Jan who did the work the entire lab and the old members of the lab we've been here the lab is now five years old and now it's starting to be not to to to give real outputs I hope you like the work we're doing and of course thank you for all the people that supported us financially ISD and many other granting and foundations so they said thank you very much for listening thank you a lot Maximilian that was very interesting technique and reasons you got here particularly interesting this trick you got from the on a vertical axis and I just like to remind audience that if they want to join us on this room and interact ask questions possibly discussions the link is is on the chat so actually I have a couple of questions sorry you had a lot of spam today let me just give me a minute to find it I saw a discussion about UV cones between Marla Feller and Ana Vlasit hello YouTube by the way yeah so we're talking about UV cones so your stimulation about UV cones so Ana was saying that she was curious about whether you have thought about whether UV only stimulus affects the properties of the receptive field you collect if we use only UV symbols if it would change the properties then if it would be using green stimulus green that's the question I think so of course I mean some of some of the properties will will will change that's for clear I mean the best example is GERGC where you have let's say the greens around and UV center in some part of the receptive field so this is not the ultimate truth of how the receptive field will look like and it will change depending on the light level and and spectrum that you use unfortunately we cannot use green stimulus right now we we're testing with synthetic dyes to shift it a little bit more to the red and in theory we could actually see green in our calculation the problem is that it's a it's a stimulus contrast issue so depending on the light that we use the stimulus contrast is too weak and if we increase the stimulus contrast we have too much bleed through just the technicality but I think that in general yeah I think that in general I was saying that okay I think that in general the predictions that we did we also did for the UV channel so basically but for green that would be very similar and it could be that on on in general terms I mean if you think of GRDCs they're they're surround would be asymmetric and would be also positioned eventually so there's an indication at least some of the cells will have similar properties in the symmetry and pointing contrary to what you would expect and from from the distribution of cones green cones and and if green cones and rods would be simultaneously active which there is evidence for that perhaps it is a method where the UV will give you like an actual DC sort of like green even if if green would be symmetric if you only have symmetric receptive field with green stimulus you will add the the asymmetry that comes from the UV and perhaps that will make the difference I don't know if I'm explaining myself well but basically I don't know to what extent at this level it will change my impression is that it will but everything points towards that for for what we how we think about it it might not matter much for our conclusions well I think that answers the follow-up question from Anna I mean we have Marla and Hannah on the chat if you want to follow up on this question sorry there's a little bit of background noise but I I could follow up a little bit sorry there was a time difference so I missed the beginning of your answers the question but my I guess I'm wondering because there is a lot of work showing that the receptive fields can be color specific in different regions of the retina and so if we're in the dorsal retina where green is the dominant photoreceptor and then also you're not really stimulating the rods very strongly with a stimulus how can you say that the properties of the receptive fields that you find are sort of the the overall receptive field property but no no no I'm not I mean I'm first it's one of the pictures that we so first I don't think I did the work where I basically show that it is important to look at the difference between green and UV and that that might change the receptive field that's for sure I cannot I mean this this is clear this is not the ultimate truth because we don't have green and there is it is impossible to do this work while having the green right now I mean we would love to do it but at the scale that we're doing it it's not possible I think that generally if you think of a system where green and rods green cones and rods will be active if you bunch them together you will have basically a uniform input across the across the retina so one way of thinking of it is that even if there is no asymmetry difference because now we think of the mechanism of being the UV cones if it would be basically uniformly spread across entire retina assuming that this is the case and assuming that that will have as an effect that most of the green the response would be symmetric the UV might break that symmetry and the UV might make it make it still stronger and weaker to to the to the UV channel and everything we optimize is to to our UV to the UV input that people have measured again if you use UV or if you use green it's basically the same so I would love to know but that is technically not possible right now yeah it's really cool data so to see especially seeing the like the DS tuning across the whole distribution just in one retina that was really neat we have Michael who would like to ask a question if you want to go ahead can you hear me yeah yeah hey max it was very clever and totally convincing is very impressive so I have a I don't really I have a hard time thinking about the trade-offs for the actual animal right so as you know eyes can move the head can move and as far as I know the world is not flat right and so you could imagine that like overplaying this optimality for the average case is actually maladaptive right so I'm wondering if your analysis one question is do you find the animals are actually or the retinas are not fully optimized right as your theory predicts and they sit kind of one step below right really pushing for this global maximum alternatively um you know maybe you could speculate about some kind of dynamic compensation like do you think you have to re-weight all the outputs of RGCs if you tip your head a little bit or if you're on a crooked surface that would be fascinating what is going on so if there's a new paper from the Care Lab that came out just recently I think it's an e-life and they're basically looking at how the animal stabilizes the eyes and it's remarkable how well the horizon is basically stabilized all over right so in the lab it's in the lab right it's just like on hunting hunting it's in the lab it's while hunting but about but I think there are at least mechanisms to compensate I don't say that eye movements are are there to compensate such the horizon stays at the same position I don't know but there is a lot of stabilization going on there and as far as I think if on average the horizon is basically kept there you don't need to have a perfect match to to increase optimality but this is a very difficult question to ask because we don't know we haven't looked at when this would break and of course at some point it will it will not be optimal anymore but if the horizon stays seeing what the animal does and at least in this lab conditions with the cameras on the head so you can basically see what the eyes are doing and it seems to be that the movements of the eye with respect of how the animal shakes and runs around they're highly stabilizing them such that they are looking at basically a particular position which would improve at least such an efficiency constrained that we are imposing I don't feel that okay no it's just it's interesting right because it just means that you can't ignore the behavioral side of it right that the that the behavior have to sort of clamp the visual system in a way that you can actually take advantage of all of these trade-offs right yes and I mean to what extent that actually improves let's say because all efficiency arguments are energetic cost arguments and so to what extent actually the system is improving calories or something like that it's a very difficult question to ask and and then you I don't know we can estimate it but I think it would be kind of like I'm not really accurate thanks Michael okay let's continue we have quite a few interesting questions so I will jump to the next one I have one for Minesh I mean she's here with us if you want to ask it yourself yes hi thanks for a nice talk Max I want to ask and thanks for let me ask myself I was a question about so you looked at a lot of cells and presumably a lot of stimuli as well because one hour and a half of stimulation do any properties of the spatial or temporal properties in the centers around that you've observed do they predict any specific tuning property to certain stimuli you know I mean and I know now I see that you only look at UV but that would be good so I mean there's a very nice questions and there is a master's student which is starting we'll use the data to see to what extent we can predict with this spatial temporal receptive field any of the response properties and no we haven't and we don't record for one hour and a half this was just like at the beginning when we're trying to see like push the system and we could record for an hour an hour and a half maximum of one region but normally record let's say 20 minutes because let's say 15 minutes of white noise and then some chirp responses on and then we move to the next the next area because we want to tile the red night so it's a good system but we cannot it has its limits also in how long we can record and but yeah it's a very interesting question and we would right now try to exactly ask that see we have I don't know a few thousand chirp responses we have for all of them receptive fields to what extent what what should we do to basically be able to predict one from the other and to what extent we can do that hopefully we will soon yeah it's interesting very interesting things Good morning Max Hello Why I'm good, how are you? Good, have fun, go ahead Sorry, I'm just waking up and groggy and can't speak and but while going through the part of the talk I was wondering you talked emphasise the spatial filters whatever the temporal filters do they change in the same way and we they don't at least the centres and no not really they don't change in the same way otherwise we wouldn't have been able to use them to cluster them so we have if you look at although our temporal clusters they're somehow uniformly represented across elevation and so we don't see that they adapt So is that expected or not based on your predictive coding hypothesis? We didn't we didn't we didn't ask that particular specifically but you would expect that there will also be some adaptations into temporal domain and at least if I remember correctly yes we don't see that but we also didn't look at it in in in much detail so far I mean mechanistically you might expect those to be linked right or not or maybe it'll give you insights into the mechanism that are generating those surrounds I mean if there are I mean normally things and as you tighten in space you'll tighten in time as well right or maybe maybe Tom you can or anyone else the flying guys can tell us what happens elsewhere perhaps if we if we add the green then they tighten in time I don't have an answer I have a question actually for retina people speaking of mechanisms of centers around receptive fields is there a consensus like that actually accounts for a proper sort of circuit architecture do we have good evidence that really explains how a center surround receptive field comes about or I mean I've seen the cartoons I just mean how does the data and the connectomic work and the neural anatomy actually line up with that I mean yes and no so yes because we know horizontal cells are useful and I'm a green cells are useful and it all makes sense but what hasn't really been addressed is the fact that you're going to get layers of centers around right so it's kind of you putting rings into rings into rings and how that is a good thing or a bad thing I don't think has been looked at much I don't know are you guys going to any thoughts on that in there and this is what I was alluding to with those different mechanisms that surround some might aim the the temporal and some might in the spatial parts of it and maybe you can disentangle it that great because to me it's surprising otherwise that you can have you know this gradient of spatial filters and not change the temporal tuning properties but again so one other thing you said we're looking at calcium no and so it could be that there is ah yeah yeah yeah to be that calcium calcium responses are not even though yeah yeah yeah we have relatively fast filters that we won't be able to see it because the change is not very much bigger yeah I think that's possible but I I bet you won't see it right because if you if you measure a fast slow fast process with a long pass filter if you measure it nice enough it'll just you know in a in a systematic manner go longer that makes sense so you know like we can tell a part of fast from a slow set even at calcium quite happily right it's just both of them are really slow it's just one is even slower than the other right but you tell a gradient across the retina might be pretty challenging of a certain type right maybe maybe actually you might see it in the chirps before you see it in the kernels no but otherwise it's an amazing technique actually and we've been Victor have been looking at this in a very systematic way and he sees and very when you're trying to cluster the chirps based on position and based on response property together he sees suddenly systematic changes that you won't see if you let came in cluster because they're very very slight perhaps exactly that what we're seeing in the rental retina that might be might be a little faster I have a little this component but if you average in the it's very if you don't take the spatial contribution at the component into account it's very difficult to to put them together perhaps we already seen that so we're in tight schedule let's try to follow up I was about to ask Marion's question but she just joined us yeah I decided to come online as well thanks Max thanks Marion I mean it's related to the conversation and that you guys were already having if there are other properties of receptive fields that change across space especially because there were so many receptive fields that you showed in this like zoo of receptive fields that really burn centers around right no no there are some that look basically a perfect abore and reproducible it's not just one cell so there are a lot of them and so yes I mean there is there's a lot of things to unpack here but you do one unpacking after the other and and to what extent you can use basically the entire spatial temporal receptive field somehow to to define that would be perhaps the next question to ask and in there is a lot of things happening in there and it's now we're basically looking at the static view of the basically spatial filter but of course it's also dynamic view which were the surround might come in move in one direction the other and others might be more static where surrounds always staying in one particular space and all that we see and these the ball like receptive fields is this something that you specifically pull out with the stimulus that you are using to map them no I mean you can you can see that I mean I'm I'm I'm still presenting my what to be sure I have to be sure okay let me see if I go back to this slide so if you look over here number two it somehow looks like that but with the bigger receptive fields and then we look at the small receptive fields like classically you you see something and it becomes much much nicer when you use the new stimulus which works very nicely and actually I have to say that yesterday I saw paper published describing completely separately to us but more from a theoretical perspective this receptive fields method or they call it super resolution mapping of receptive fields and and basically it really works it also works for spherical eucalyptus and cortex and whatever you want okay cool thanks yeah I think they've also implemented this in in time so you kind of shift the temporal properties of the stimulus and the measure of the response and you can get high resolution temporal high temporal resolution as well so yeah see our temporal resolution we see where is it here so um yeah it is kind of slow but yeah so the next question is can you use these techniques for gluesniffer imaging do we have new gluesnifers that might match I mean we're using a I assume so I think the the difficult thing is to if you want to have with gluesnifers you would like to have it more sparsely and such that you don't have I mean or zoom in bigger I am like but I mean if they are response properties that you can image if I think if there are lots of different tones at different positions you might it might be blurry so that is perhaps disadvantage but if so if you are sparse enough then yes for sure a bit too green no need a regular sniffer I think it will work in green with UV2 yes I don't think that this is the you will see responses just because you will saturate the green pathway here I don't think we're saturating it but we cannot get response because our contrast in the green channel is just too weak it's like four percent calculated and if we put it bigger we have a very strong bleeds through so then we cannot use it anymore but I mean for for Ganius and reporting it really well before we continue because it's been an hour already I'd just like to tell our audience that is still live on YouTube that we will soon close the stream so if you want to continue this discussion and join us you can still do so on the link which is currently on the chat so after this short question I will stop the the YouTube stream what a success because there's a lot of questions that we can still discuss but Tom but then suggested a discussion regarding special temporal asymmetries do you want to bring that so Gautam has has since deliberated on that but I can just steer away Lee's question which was about well you can you can manipulate the mouse to be less asymmetric with the with the option gradient at least right either with genetically or by giving it some well taking away thyroid hormone I guess messing with hormones so imagine you now take your mouse which is super asymmetric and you force it to be symmetric developmentally would you expect that to give you symmetric receptive field so do you think that's a different property um I mean there is this albino mouse which basically have no gradient and ideally you could basically test that by by crossing them or back crossing all of the lines to and yes I mean that would be my prediction that it becomes less symmetric and that would be nice way of testing that hypothesis what the benefits are for a mouse I think it's very difficult to test that behaviorally if this is the next question you might ask I mean if it's a it's a mouse without a gradient more coping better in a natural environment not I mean these these questions are very difficult to address but I would assume at least from the physiological level that the that this asymmetry might not be there anymore but I might be wrong if there could be other mechanisms that that are important perhaps I mean there's a gradient of synaptic strengths that and just that we think of the the obscene reading as being them them a direct obvious link I mean it must be quite hard why it right because these are the poor mice sitting in some page somewhere not like they've ever seen an horizon exact yeah rising okay thank you for that so we will close this discussion now see you all next week for another talk I am now closing the live stream and I'm considering my mother at the right to you all please continue thank you and we are


Hi, Gerald Friedman, Department of Economics, University of Massachusetts. We're going to talk about demand, individual demand and the demand for products by the entire community. Now you may remember the standard model, neoclassical model of demand is based on diminishing marginal utility because of satiation. I have one chocolate, that's wonderful, I love it. Second chocolate, good, third chocolate, okay, fourth chocolate, I'm getting tired of it. Fifth chocolate, don't want it at all. The first chocolate I'd pay a lot for, second chocolate, I'd buy if you reduce the price. Third chocolate, yeah, cut the price a lot, I'll buy it. Fourth chocolate, make it cheap, I'll buy it. Fifth chocolate, if it's free, I'll take five, yeah. That's the standard demand by an individual. It's the way Robinson Crusoe would do things. He would make one canoe even if it's hard work. If the work's easier, he'd make two canoes. If it gets easy enough, he might make three, downward sloping demand by an individual living all by themselves. Following this approach, what do you do when prices go up? You get less. If you start out with low prices for chocolate, I'll buy four. Raise the price, I'll get only three. Raise prices some more, only two. That's the orthodox theory of demand. The real world, though, it often doesn't work that way.   Are you going to buy it? Who would pay $10 for a laptop? What sort of laptop could you get for $10? You go and you see somebody selling dresses on the street for $5. You're going to buy a dress for $5?  Why are you going to buy a dress? Guys don't buy dresses, even at $5. If it's a woman, if you're a woman, will you buy a dress for $5? Or will you assume that the seams are going to rip as soon as you put it on? Or it must be an antiquated style. There are all sorts of situations where you don't buy more at lower prices, and in fact you may buy more if prices go up. That's social life. The orthodox, neoclassical approach to demand that leads to this downward sloping demand curve is based on individuals, not just any sort of individuals, sociopathic individuals, individuals who don't care about anybody else, individuals who don't think about what other people are doing, individuals who are so sure of themselves that they don't need to think about, to take information from the outside world, from the social life of other people. No, that's not the normal person. Most of us are not turtles walking around in our shells. Most of us are looking around all the time to see what are other people buying? What are other people doing? What information can I get from that? How can I fit in? And in real life, we often end up acting contrary to the orthodox theory of downward sloping demand. The social sources of consumption that lead to upward sloping demand curves. I could break them up into four types. First, status, which we call the Veblen effect. Second, bonding, which leads to stampede. Third, information, which leads to price signals. And finally, network effects, which leads to Microsoft. Okay, Veblen effects are named after Thoresteen Veblen, who was late 19th, early 20th century American economist, one of the more interesting people around. Notable for two things. First of all, he had a voracious sexual appetite, which led him to lose several jobs because he not only had an appetite, but apparently he was very attractive. And at least twice, he was caught sleeping with the wife of university presidents. He lost those jobs. Not really fair. I mean, they could get divorced, but it shouldn't lose its job for that, I don't know. Second, he's famous for some of his writings, including most notably his 1899 book, The Theory of the Leisure Class, in which he coined the phrase conspicuous consumption. Why do you like having a fancy car? Yeah, you like having it because it goes fast, it jumps off when the light turns green, it has a good stereo system, yeah.  Well, I'm going to drive up in Alexis, I'll teach him. Why do you buy? Why do women wear high heels? I mean, they're not comfortable. You know, American women, adult American women have an epidemic of feet troubles coming from high heels, coming from pointy shoes, which goes back to middle ages when men wore pointy shoes and the French government banned pointy shoes in the 14th century because they were ruining the feet of Parisian bourgeois men. We buy things to signal to others that we are successful. We are good. Do you remember Kira Knightley in Pirates of the Caribbean and her corset? She almost died wearing that corset. Why did 18th century women wear corsets to impress others? It showed that they were rich and successful because they didn't need to breathe just like driving Alexis shows you rich and successful. I say rather than drive Lexuses, rather than wear corsets and high heels and fancy gowns that aren't comfortable and women trip over them and all that stuff, burn money. You want to impress people?  That'll impress them. You don't need to get status by spending money. You can get status by burning money. Okay, second, sometimes we follow the crowd because we think they know better. You want to buy an airplane? What type of airplane? I don't know. Look and see what other people are doing. You see people spending ten bucks on an airplane? Are you going to buy that airplane? No, you're going to buy an airplane that people spend a hundred million dollars on. That's a plane you trust. You want to buy something cheap? What are you, cheap? If you're really good at computers, maybe you'll buy the ten dollar laptop because you know you can fix it. But if not, you're going to buy a seven hundred dollar laptop from Dell or a thousand dollar laptop from Apple because at that price, you trust the quality. We even believe that more expensive stuff is better.  They told them the wine was cheap. The brain images showed people thought wine wasn't very good. Tell them the wine's expensive? Same wine, brain images. People are enjoying the wine more because they're told it's more expensive. The expectation is if something's expensive, it's better. Third, you buy stuff to be part of a network of people who like the same things because you want to fit in. Why don't guys wear dresses? What happens in high school if a guy gets on a bus wearing a dress? Is he going to get off the bus without a black eye? No, somebody's going to beat him up. Guys aren't supposed to be like that. Women aren't supposed to have short hair. Guys aren't supposed to have long hair. You fit in. Raise the price of haircuts. Are women going to stop? Are going to the hair salon? Raise the price of haircuts. Are men going to grow their hair long? They'll keep doing what they do because they want to fit in. If more men want short hair and hair cut prices go up, are other guys going to stop cutting their hair? No, they're going to cut their hair more because they want to fit in. Higher prices, more demand, higher prices, still more demand because people want to fit in. Networks work the same way. You want what other people want. Are you going to buy a metric ruler or metric tools just because they're cheap? No, you buy imperial measures and imperial tools because they fit American stuff. Are you going in Europe? Are you going to buy imperial system measurements? A Fahrenheit thermometer just because it's cheap? No, you buy what fits in. How else do you explain the triumph of Microsoft? Why else would anybody use Microsoft stuff? Except it's what everybody else uses and it's easier to communicate. Even people with apples use Microsoft Office increasingly so that they can fit in with other people with people using Microsoft. You put it all together and yes, you have diminishing marginal utility for yourself. The first time's great, the second time's good, the third time you have a headache. But as a community, we have an upward sloping demand curve for many of the products that we consume. And on that counter-intuitive note, thank you and have a good day.


Jose Castillo. Thanks, Aaron. Thanks, everyone. I'd like to propose and present the case of Mexico City and the particular case of Ciudad Nesa as a kind of mirror image, even if so an oblique mirror of what's happening with Mumbai and more specifically with Derabi. As you may know, Mexico City was not founded on six islands as Mumbai was, but it was founded on one island on a dried up lake and over the last 700 years has achieved a population of 19.1 million over an extension of 1400 square kilometers. The key part to the spatial conditions of Mexico City is that 60% of the city originated through some kind of informal process. When we talk about informal process, and it was something mentioned yesterday at the Tata Institute workshop, we should qualify what we mean by slum. When I say informal, I refer to any process that happens outside the legal, professional, procedural or regulatory frameworks. The slums that I'm going to show have nothing to do with the slums that you have here in Mumbai. I show this slide because in a sense the growth of Mexico City is a very quick and fast growth happening mostly after 1950. So a city that stayed more or less stable for 500 years grew immensely only in 50 years. This graphic, which is kind of a mirror image, the top part, the green part of the graphic, it's economic information, the bottom part is demographic information. The green is the gross domestic product, and you see a number of lines which are the parity of the peso to the dollar, the currency, inflation and annual economic growth. In a sense I show this graphic because it shows the boom and bust cycles that characterize not only developing countries but cities as well. One could almost define those peaks and valleys in economic development along the presidential election. So every time a president came out there was a crisis. On the bottom part of the graph you see how in a sense the growth of Mexico City has been taking place outside of Mexico City. The dark gray shows the federal district, the lighter gray shows the growth taking place in the state of Mexico. So when we talk about Mexico City we actually talk about the metropolitan area. 1949, I show this image because it was a type of how, when one spoke about housing policy, this was what housing meant in 1949. The most important project by Mario Pani, the foremost architect and urbanist in Mexico City. Fast forward 15 years and this is Tlatelolco, a community slum clearance project only two kilometers north of the city, north of downtown Mexico City. And it was actually a former slum turned into a community, modern, hygienic, progressive city for 80,000 people. This used to be housing. This was the idea and not only in terms of architecture but also in terms of politicians, policymakers. But actually what was happening in 1963 was this phenomenon. So it's kind of an irony that while most policymakers were thinking that housing was slum clearance, actually the largest slum in Latin America was taking place and this is a picture of Nesa. To understand a little bit more about the spatial geography of Mexico City, the idea of the slums, it would be very difficult to find which are the real slums in this size and how they relate to density. In some cases like the western sections, the slums appear right next to high-end development in the same fashion as in Loma as the Chapultepec. Or in Santa Fe as it has been shown before by both Ricky and Andy where we have these huge gated communities right next to these other encroaching informal settlements. But I'm more interested in presenting what's happening on the eastern side of the city. A city where actually urbanism has become a kind of geography. We're talking about communities. This is the Chapultepec, the previous photo was Nesa. Both communities of more than a million and a half inhabitants all developed in less than 50 years and all developed informally. This is Valle de Chalgo. It's crucial to understand that the informality is not about actually a collapse of very simple organization but actually about a very sophisticated set of networks, dynamics and players in which the key to the success takes place the moment that those social protocols arise and become evident. Nesa, which is a big square, you see the sort of the large grid you see in the center of the picture. It houses 1.25 million. Started in 1943, 38 square kilometers. If you add up the other neighborhood, which is Chimalhuacan, on this part here, you have the population of central Barcelona. And some pictures again. 1943 is the photo on the left, a floating part of the city. 1973, I'm sorry, 1963, the photo on the upper right. In 1973. It was actually a community that was very much planned illegally, if you could say, but I would like to play the idea that space matters in the sense that the way that one imagines the particularities of urban design guarantee or entail a kind of success or failure for informal settlements. In a sense, and these are people receiving their deeds for the lands, it is not enough to think about the legal and procedural aspects, but we actually have to think about space and how in 50 years a community that kept floating every summer, not unlike the monsoon, has achieved this kind of vitality that we see in some of these pictures. Even to the extent that some gated communities are beginning to appear in NESA, opticians, private facilities, even cases like swimming pools appearing in private schools which talks about gentrification and even lipo sculpture. For $1,000 you could get lipo section now in NESA. And so in a sense, this is imagining like new context for sort of social behavior and if we look at what's happening in terms of housing policy nowadays, in Mexico, this would talk about the dual system. On the one hand, 100,000 units being built every year, but at the same time a similar number of units happening by the informal sector. This is what housing policy, focusing mostly on finance, is imagining. The irony is that within this formal settlement, a series of informality processes are taking place. Therefore, and I would like to ratify, space and urban design doesn't matter. The way that policy makers, architect, designers, sociologists look at this is a guarantee to avoid this kind of ghettoization, this kind of almost photoshopping of what seems to be the informal city against what seems to be the formal city. Thank you very much.


Hi, it's Mark Punsock, head of Product at GitLab, and I'm going to show off an idea we have for Auto DevOps. At GitLab, we believe in learning the best practices from around the industry and making them available to everyone in an easy and default way in our integrated platform. Auto DevOps is our vision for doing just that for the intersection of development and operations. What I'm going to show is our vision that hasn't shipped yet, so some features may change or some may not ship at all. I'm starting off with a plain vanilla Rails app, literally Rails New. Now we'll just check it in Diversion Control, and now we add the GitLab remote. Now we push it up, GitLab detects the new URL and Auto creates a project for me. And then without any further action, it starts a CICD pipeline, starting with AutoBuild. If you have nothing specified, it'll use Heroku Buildpacks to detect the language and build an appropriate Docker image. If you have a Docker file, it'll use that instead. Then it kicks off AutoCI by running Heroku TestPacks for your language. And then when everything looks good, it auto-deploys to production. If you have nothing configured, it'll use a default Helm chart to deploy to Kubernetes. If you have a Docker compose file, it'll use that instead. Or if you have a Helm chart in the repo, or if you specify a Helm chart in a project variable, it'll use your custom chart. Now let's go and make a code change. Here I'll just make a trivial change to the read me, put it in a new branch, and create a merge request. So now in addition to AutoBuild and AutoCI, it's running auto-code quality to make sure you're not introducing bad code practices in the merge request. And it's creating AutoReview app, so you can see those changes live. Back to the merge request, we see a link to the new review app, right on the merge request. So that's it. AutoDevOps complete with AutoCreate, AutoBuild, AutoCI, AutoDeploy, AutoCodeQuality, and AutoReview app. Thanks for watching.


What is going on guys, welcome to another video. Today's video is a little bit different because today's video is a giveaway. So I have not done a giveaway in quite some time. I actually paired up with Z-Bates. It's been a while. I've actually had this package for quite some time. I honestly totally forgot to give this thing away. I've had it and I communicated with them over at Z-Bates and had this stuff to give away. So this is what we're doing today. We got a tackle giveaway because I haven't done one in a while. I haven't had the stuff to do it. And there's honestly a lot of good stuff in here guys that I'm going to be going through that one of you guys are going to get lucky and win. So guys, let me show you everything that I got for this giveaway today from Z-Bates. Special thanks to them for sending all this stuff out. So I'm going to start off with the worms to show you guys what you guys will be getting if you win this. There's actually a lot of good stuff in this. I'm actually kind of jealous because I could use a lot of this right now but let's go ahead and hop into it. So first, I want to talk about the Z-Sticks right here. Got three different colors. As you can tell, Junebug, Green Pumpkin, Watermelon Red Flake, all great colors. You know, Junebug is going to be good in dirty water. You got the Green Pumpkin, which is going to be good and you know that moderate clear water. Honestly, it's just a good natural color in general. Then Watermelon Red, same with that. These are going to be great ponds, lakes, wherever you guys are going to want to use them. If you guys take a same coat to a pond, you're going to catch a fish. I mean, that's just a guarantee. I've actually been using the Sticks a lot. All on the river, I've been using the Junebug and also the black and blue color. I've been blacking them. So you're going to get three packs of those right there. Next bait I'm going to be showing you guys is Old Beaver right here. It's an Angry Beaver. This is in black and blue. You can put this on a Texas rig. I'll be perfect for that. This is actually the smaller version. Then you got some Swimmers.  Got some three and a half inch. Well, natural color. You got a little bit of green and blue on top, white belly. And then these are my favorite. You actually got the 4.75. We're actually five inch regular Swimmers. I've actually been using these a lot.  You can throw it on a fish head spin. You can also throw it on the top of the lily pads. Like that's what I love to do. Throw in a weightless on the top of the lily pads. That's actually what I've been doing on the river. I'm with this bad boy right here. What else we got? So we got some old basser. If you want to put that on there, make it bait stink. Make the fish hold onto it a little bit longer. And lastly, guys, really great bait right here. This is a Rip-Tel worm. This is actually in Junebug. Let me take one of these out for you. There you go. Look at that. It's looking good. Catch a lot of fish on that, especially in that Junebug color. Throw that bad boy on a shaky head. It'll be good to go. Always guaranteed to catch fish on shaky head, guys, or a worm in general. That is all the baits that I have. This is just a small giveaway, guys, that I wanted to get to you guys since I haven't done one in a while. And I meant to do this giveaway a while ago, so that's why we're just not getting this. So you got Rip-Tel worms. You got the swimmers. Three packs of those. Three packs of Sancos. One pack of the Beavers. Oh, lastly. And you got two wobble heads right there. The Swing Head, which I absolutely love.   Absolutely kill it. If you put this little Beaver on there, heck, you could even put this Rip-Tel worm on it. Heck, even a Sanco.  If you haven't thrown one, I really suggest trying it, especially to the lucky winner who's gonna win this giveaway. And then you got two little decals right here and then the bath syrup. But that is everything for this little giveaway, guys. You got all this tackle right here. And so now I'm gonna explain what you guys have to do. I'm gonna keep it very simple here. So the first step I would like you guys to do is like this video. Second step, subscribe. Third step, leave a comment below on what type of videos you guys wanna see next. Put some thought into it, guys, because I wanna actually know what you guys, like I ask you guys all the time and some people actually reply, but I want everyone to reply and see what you guys wanna see. So leave a comment on what you guys wanna see next. Subscribe, like the video and go follow Z-Bates on Instagram. I'll leave their Instagram handle below. I'll pop it up on the screen as well. And go follow me on Instagram. I kick in their Bass TV. And that is all the steps, guys. And we're gonna pick one lucky winner to win all this tackle. I'm sure I'm gonna end up announcing it on Instagram anyways, probably on the story. So make sure you have my post notifications on, on Instagram. You have my post notifications on YouTube and you're watching all the content that I put out because that's where I'm gonna release the winner. But thank you guys so much for watching. Thank you guys so much for the support. New videos coming out very soon, guys. This past couple of weeks, they've been very hectic and they're gonna be hectic because I got finals and I got a lot of stuff going on. I haven't been sleeping. I've honestly been sleeping during the day and staying up at night just because my schedule is so messed up with what I've been having to do. But thank you guys so much for the support. Be sure to follow those steps and I'll catch you guys on Sunday's video.  All you all up in my shoe when you got my problems in me. Yeah, I'm cool, but you don't wanna see that other side of me.   I know, I know that you been empty.  Ain't no...