All right. Good morning, everybody. Happy Wednesday, everyone has everybody doing on this beautiful day of 8 8 18. Oh, look at that. All eights feel lucky. Love the eights I was born on the 18th eight is a good luck. A good luck number. All right. Good to see you all I feel good about today Good good. Good. See Lawrence Nancy Currie. Google's great names Craig Benito, Andrea. What's up, Bill? Fred, Gary, George. There you go. Sid, everyone. All right. All cyber traders. Let's get ready to roll We got some good news going on got some good stocks going on We had that one stock that's making already pretty well big movement in the a BIL a lot of you guys have already been trading in pre-market. Look how gorgeous that stock has been since 8 30 We've been trading already. It's been about 45 40 minutes already If you got in here early, which you supposed to you should already be up about a thousand dollars on a thousand chairs a lot of you remember this stock very clearly we trade this stock not too long ago Back here and being In June this went from 28 to 90 up to 14 bucks So and so you got good support resistance levels. You got great iceberg orders over here on the matrix So we're gonna go back. We're gonna check it out But before we do that and talk about all these stocks that were on our watch list Let's talk about what happened yesterday Hold on one second. Let me share my screen got the wrong screen up there and running. There we go Number six there we go. All right We're I'm recording it but oh, but on the webinar wasn't really broadcasting on the right one So let me just bring this back up so we could see that again Come on. Come on. Come on. Come on. All right So as as as the a BIL is coming up and you're gonna be seeing Obviously some of you could see it. That's up there We're gonna go look and talk about what happened yesterday because we had some good moves Yes, and a couple of stocks first of all the Hertz. All right, so Hertz just want to bring that up Hertz had a great great rally You know, I'll be honest with you all these cards stocks You know, they want to say but they really got hurt a lot regarding uber and like I told you just came back from Chicago and Do you listen? I thought New York City was expensive the park car Do you want to cost this park a car in in Chicago?  You can you leave you were going back to the airport wherever it is But anyway, a lot of car stocks get getting hurt But you know, that's just my two cents, but you could see that the stock did really well So don't hold it against them money's money. You could see had major resistance levels. It was a great move for us yesterday Let me just fix a little ticker right here Stock came right out of the gate beautiful from 16 all the way to 20. You're talking about most of $4 move. I Mean, it was no brainer. You saw resistance levels back in June So everybody did really well on that one TIS another nice little move yesterday Stock took a big big hit from 380 down to 220 Stock had a nice little pop right there right at a pregate right a pre-market Stock went right back where it started took a big hit. It was this $15 stock great little dead cap bounce right there ETSY Another nice little move right there on the short a little bit more on the expensive side But that stock gap that pretty pretty nicely, but sure enough boom Just tanked and it was a great short going into the close I mean we had some of you guys did really well on this at 46. Listen once it was testing the lower lows And and the low of the day. I mean that was really a no brainer right at right around right around 2 o'clock You see it went from 46 dollars of 43 great great winner only bad thing. It was a lot on the expensive side not all of us Really good afford to trade something so expensive, you know, and then obviously the eks so that one was also not as nice It's the other ones, but that one did pretty well. Anyway, a lot of people did pretty well on that Doug did really well in the ETSI. He made me about 60 cents on that one. Let me get my Always like to always like to tell everyone how good they did it from what I can remember a lot of people did pretty well Give him a round of applause Gary also he did pretty well on the eks. So he made about 20 cents 25 cents give him a round of applause And by the way the the Hertz and the ETSI both are good on on all good on earnings. So Now whoops wrong screen. All right, there we go. So let's talk about what's going on What's going on this morning?  We'll talk about that Ben was the Tesla Let me bring it up Tesla rumor has it that Tesla There we go that The owner wants to buy back his stock He wants to get out of the public business. He doesn't like it and honestly, I personally Dell did the same thing, you know, don't underestimate you as big corporates I would think speaking a public company is you know Is rainbows and puppy and puppy dogs? It's hot. You know, they mean sometimes it's it's kind of cruel and So that was the rumor, but what a great little rally right there stock went up about a hundred points When that announcement kind of came out you could see it went from like 290 all the way to about three three 80 great great rally But you know listen a lot of us would know we can't afford it But you know what that in that situation you could trade you could do an option trade so that one you might if you guys know this but you can day trade options and That was actually something that you could have did really pretty damn. Well on it a couple of the stocks So let's talk about what's moving and we're to get back to the ABIL, but the other rumor is it that snap? Got some big funding I didn't know one of the prince Guy from Saudi Arabia put over a quarter of a billion dollars in snapchat. They lost three million users I was reading it this morning. I'm like well, it sounds like a lot of users, but they had what 150 130 million users. I'm like what else three million users if you had 130 million users imagine us losing That's like us losing. We got a thousand traders and we lost like three people. It's like, okay Not really that at the end of the world, but but anyway, they got a lot of funding But we trade snapchat, you know, we stock out a little nasty sometimes got a little ugly Pretty volatile. It's got some decent volume great pre-market. I don't know if I'm really gonna trade it I'm gonna keep it on the watch list a couple of you guys also bringing up the pizza trade PZZ a That one took a big big hit. You could see it went from 55 down to 41 Your earnings came out wasn't too. Well one thing we know about this one. It's not a pharmaceutical stock.  This is Papa John's and You know listen that whole thing about the CEO of resigning now. He's trying to assume. I don't know what's going on with that story But anyway, you know, it's amazing where Something so You know, how can I say it? So personal people take it like just sky puts the opinion and could just destroy the stock Would you ever think of that? So anyway for us as traders? With his catastrophes is opportunity. So anyway, Peter Papa John's gonna be on our watch list All right couple of the ones SBBL I want to go through this group pretty quick because you guys need to get out there and start trading MTC H. That's the one match Matt's got a nice little push going on right here Was this match calm? So anyway, that stock is moving pretty well. It's got on the match group Stocks got a nice I had really good earnings yesterday.  Anyway, this company always does pretty well on Valentine's Day TP Where am I? ENDP Here's another stock that gapped up pretty nicely. Not only has been a good day trade It's been a great swing trade, but as a day trader now it gapped up so much I don't know if I really want to day trade it, you know, it's testing some support levels from pre-market You got if you could see you have some 4000 share buyers out here nice little big buyer out here at a whole number We all know what that means. So we'll put that one on the watch list a couple of now Listen, there's a couple of nice little shorts ENDP Waiting for the data to come up. Okay ENDP. I'm sorry EXTR There we go and bring up that one. So EXTR. I don't know if it's on your short list This stock is taking a big big hit. The stock was pretty good early in pre-market around 780 You could have got around 740 now you got 690 Listen, you can see the sellers out there. Look for those iceberg orders. That one looks pretty good TWNK another one stock look for the bounce You can see stock from 14 down to 11. It's testing some major support levels going back in the beginning of the year That one looks pretty good. Also, look at the short on BECN Tanola when I found It's kind of holding pretty good here on that support levels right around the 35 But if she does break it, you know, then the stock can really take a big hit But the only thing that it looks like it's good It's breaking your yearly lows. So that kind of really gets me a little nervous But yesterday's previous support levels is kind of holding pretty strong there So that is a very very good indication. Also look at the buyers out there on your matrix You know looks if you see them out there not really a lot of buyers sitting out there yet So with a lot of hundreds not really a thousand share of stock that we want to look at. All right guys, so That's about it. We got a very good go and in the AB IO Put that one there. It looks like a backed right off right there. All right. Listen, you know what profit-taking But remember that stock did go up to 15 and we did pretty well on that back in You know back in June so this talk we do know it moves anything. I'm missing guys I did not call out good call out there Nancy and Ben on the pizza trade. All right, you know, they missed earnings It's always about what they project not just on the earnings But what they're projecting the following in the future no problem you more than welcome Ken By the way, Ken that kung fu fighting song we just heard just remind me of you I don't know what you want to tell you that We're all fighting in the trading right always always out there in the cyber group room trying to make money So we got a fight for We got a fight with these market makers to kind of profit from the you know what they're trying to hide from us So we're just following that co-tail. Did you mention the SBB P? The you know what the this one the you know what I I Was looking at it and you know, I'll put on the watch list. I don't know I kind of got a little turned off. I had a big rally and it started breaking lower lows It's only reason why I wasn't too crazy about it. All right Joe that was the only thing. All right everyone. So let's get ready to start trading. Remember today is Wednesday Tump Day What's gonna happen going into this weekend? It's gonna be gorgeous. It's a nice day here Let's try to capitalize as much as we can go in into the week and Enjoy our weekend guys. All right, but remember let's hit them hard early And you know that volume dies up pretty quickly right after about an hour when the market opens up And then we get that last push going to close So try not to try to focus on the most valid you time valid the times whatever you missed yesterday Listen, that's old news. Let's focus on today. All right always gives you the always makes you feel Excited that there's opportunities every day. So go out there and do it and hopefully all you new trial members out of here enjoying yourself and watching listening talk to education advisor and And don't forget tomorrow. We have another open house. So so hopefully see you then. Good luck guys. Happy trading


Today we're going to explore one of my favorite regions, Friuli Venizia Giulia Italy. Stay tuned in this video. Hello, welcome back to Exotic Wine Travel, the show that helps you drink adventurously so you can expand your palate and expand your mind. I am your host, Matthew Horky, coming to you with another tasting video. This time we're going to dig into four wines of a region that I love very much, Friuli Venizia Giulia Italy. For those of you that don't know, Friuli, as they call it for sure, is on the northeastern corner of Italy, right on the border with Slovenia. So the culture is very, very unique. It's kind of where western Europe means central Europe and then also some eastern European influences. Fantastic wines. Also, there are swaths of watery, cheap, Pinot Grigio's, but we're going to talk about more higher quality wines and those wines exist and are grown on the eastern part of Friuli Venizia Giulia, especially Colio and Colio Orientali, which we have some of those wines here today. You know, I've been going to the region for the last couple years. Unfortunately, this year we haven't been able to get out and explore and I'm just so blown away at such a versatile region.  So there's a lot to offer. You know, it's one of my favorite regions in the world. I think it's right up there with Santa Barbara County in America, Chianti, Clasco, Piamonte, that kind of stuff. Without further ado, let's get into some of the wines, shall we? The first one I'm really excited about. This is Massacan Sauvignon Blanc, or it's just Sauvignon, it's labeled for short, 2018. Massacan is actually a project in Napa Valley.  We met him at the 2018 premiere in Napa Valley, but he makes a few whites at Ramco de Liemes. It's in Frioli Venetia Giulia. That's where they bottle them in the cellar. Now, a lot of you will groan when I'm starting to talk about Sauvignon, but don't groan about it. I'll tell you why. Frioli Venetia Giulia is one of the premier regions for Sauvignon Blanc, in my opinion, and especially ones that are a little bit bigger, ripe or barrel fermented, which this is, this was fermented in oak and aged for 12 months. You know, when I think of Sauvignon Blanc, some of my favorite regions are obviously the Loire, Styria, and South Styria, and Austria, Frioli Venetia Giulia, Otto Auge, Italy, and some parts of the Central Coast in California. Let's get into the wine, shall we? You know, these Sauvignon Blancs in Frioli tend to be a little bit bigger, a little bit more riper, but they're not so huge and fat. Like the example, sometimes you get on a Napa. Let's get into this. I'm really excited to try this. So from the first sniff, it's kind of, it's not, don't expect a New Zealand Sauvignon Blanc, where it's so explosive, so much gooseberry, so much grass. This is a little bit more under control, although there are some grassy notes, some herbaceous notes to it. Kind of a subtle lemon zest, if I squeeze the lemon in there. White peach, just maybe a little bit of kiwi fruit. It's not overly big and explosive.  Let's get this to go. Boom. For a barrel fermented white, oak integration here is perfect. Snappy acidity, really, really snappy. They're kind of the citrus type sensations on the back end. I'm really liking this. It's actually acts more like a wine that doesn't see oak, which I'm quite surprised. For me, right now this wine is showing kind of like in the 89 to 90 point type range, off to retaste and retaste through. Maybe I'll go back to it after I taste the other whites. But a good showing by Massacam. For the first start, I really like it. I'm cheating a little bit with the next wine. The next wine is not from Friuli Venetia Giulia. It's from Vipava in Slovenia. But here's why I'm not cheating. The Vipava Valley is right on the border of Friuli Venetia Giulia. You know, the town of Gorica, basically in Italy and Slovenia, the country split the town. There's this valley that kind of leads in the central eastern Europe, Vipava Valley. It's incredibly beautiful on there a couple of times a year. This is a producer I have no idea about. I've never tasted the wines before. Hedele. This is the Chardonnay Goce 2016. Also barrel fermented Chardonnay. This part of Slovenia is actually known for orange wines, macerated wines. And this, this winemaker actually consults in Friuli. But this wine is not macerated. It's just a classic barrel fermented Chardonnay. Let's check this out here. So don't think a big butter bomb, a big oaky Chardonnay. Oak is integrated very, very well here. A little bit of grassy notes. Get kind of green guava melon. This is kind of shy and mineral. This is more on the mineral side than fruit. The mineral stuff sticks out a lot here. There's not tons of fruit here. It's more mineraly. The nose is quite shy. But it's the mouth feel that really gets me on this wine. This is really lovely. Let me give it another shot here. Finest length. I think I'm 90 points on this. I want to go back because we got two whites. I want to go back to the Sauvignon and compare a little bit here. Okay. The Sauvignon blocks a little bit snappier. I think fans that want a little bit more of a city, that's definitely somewhere to go for. I think I'm about 89 on the Massacan. I'm going to go 90 on the Hedale. I think it's a really nice Chardonnay. I just opened this. So I'd like to see how it develops with time. And I'll taste through and take my tasting notes as we move on. Let's move on to one of the reds, which I'm excited about. Now, Frioli Venizia Giulia is a region that really built its name on white wines. Clean, crisp white wines, barrel fermented white wines as well. But actually 40% of the production region is red wine. They have a lot of unique grapes like Skiopatino, Pinnolo, Tazalangi, Merlot also does very well as does Cabernet Franc. This is a new project. This is the Nero Magis 2014. This is the first vintage of this wine. I think the owner owns a design company. Hence the cool little label here. This is Merlot in Pinnolo. A grape that I'm super fond of. It's a grape that's pretty tannic. It needs a lot of bottle aging to show its best. So I'm assuming they blended it here with the Merlot to soften it up again. It is aged in an oak. Let's give this a sniff. Very exciting wine. I'm first very exciting wine. The Merlot, the Merlot in this part of the world. It varies. You see producers that can make it big, fruity, rich structure. And then wines that can be kind of earthy, showing more mushroom notes, more fall leaves type flavors, stuff that you usually associate with Pinnoir. So I'm liking this. Think dried cherry. Only like almost like fruit rolls. Like dried fruit rolls, stuff like dried cherry. Pepper, fall leaves. Oh, this is super intriguing and really complex nose. This wine for me is really awesome. Not a big fruity red wine. So wine for fans of the old were a lot more subtle flavors, kind of more earthy notes. Tannins are pretty big, but they're not overly powerful. Wow. Wow. In a time like today, where a lot of people can make pretty good red wines, put them in the barrel, make them fruity, structured, big. This is kind of a throwback to an old school style of red wine making. Kind of more medium-bodied. I was expecting it to be massive with the Pinnolo. Get some leather notes, some savory notes. Wow. And for 2014 was a terrible year in this part of the world. I'm pretty huge. I'm really high on this, as you can see. I'm as of right now, 92 points on this baby. I think it is a fantastic red. And I'm really excited to taste more of that tonight. Oh, tangy. Really good wine. My kind of wine. Oh, let's get started. I'm sad, but let's move on here. We have the Philippon. This is the Ramandolo 2017. Now, for those of you who don't know, Ramandolo, it's one of the great sweet wines classed along with piquelet of this region, made of the grape verduzzo. This producer was so fond of the wines of Saterna, especially Chateau de Iquium. De Iquium. And makes this verduzzo with botrytis, with some noble rot, then ages it in a barrel for two years. I'm excited to taste this. You know, I love sweet wines. I feel like I could write a book. I feel like I could write a book on the world's great sweet wines. They're kind of out of fashion now, and I'm going to tell you why. Think about in the past, hundreds of years ago, when there wasn't a lot of processed sugar around. There wasn't a lot of sweet stuff besides fruit. If you drink a beverage that's super sweet, plus gets you a little bit tipsy, there's no wonder that so many of the world's great sweet wines were called the Wine of Kings, the King of Wines. These wines have kind of fallen out of fashion, take a lot of work to make, and I'm a big fan of them. I think that it's really the most magical transformation of wine grape into wine, are the world's great sweet wines. Nice little golden amber color. Orange, kind of like orange peely. Let's give this a go. So definitely, right away, sweet apricots, dried peaches, fall leaves. And you know what, a little bit of, it's a white wine, but it's got a little bit of cherry notes, which doesn't surprise me. You know, Verduzzo, when it's made of orange wine can have some more red fruit type flavors. Now, here's the key. I like acidity when it comes to my sweet wines. This guy, this has got a really rich, mouth-coding, complex feel. There is acidity, but it's not super sharp, like you might get in Togekai or some of the great sweet rieslings in Germany. But it is lovely. There's complexity and spice here, which I like very much. Very complex for such a young sweet wine. 2017, you can put this away for a long time. I think that it will continue to develop. This is something that I want on a cool fall day with some hot apple pie. I think that's a really, really good pairing. That's something that I would like to see a lot. Really nice sweet wine. The cool thing about this is, since Romandolo is not such a well-known, super sought-after appalachian wine, you probably can get this. I don't know what the price is, but you can probably get this at a great price. If you want to try to experience some great sweet wines to get some of those noble route flavors, goodbye. I'm going to sell 91 points on this. I think it's a delicious, delicious wine. So, guys and gals, check out the wines of this part of the world, Frioli, Venizia, Giulia, and then to a greater extent across the border of Slovenia. There's some really exciting wines made there. I know a lot of people, sometimes when they think about Frioli, they think of the watery, cheap Pinot Grigio, there's a part of Prosecco that's in Frioli, Venizia, Giulia. It's also home to some of Italy's most exciting wines and food, by the way, and that's the reason I love the region so much. So, check out the wines of Frioli, Venizia, Giulia. I think I'll put a link to these wines in the description box.  You have to wait and check that out. So, I hope you remember to keep drinking adventurously, expand that palate, expand that mind, and I will see you at the next episode. Hello, thanks for watching. Hey, you made it to the end. Make sure to subscribe to our YouTube channel, click the bell so you know when new videos are out. If you like content like this, check out our Patreon page where you get some behind-the-scenes exclusive content. Thanks for watching. Cheers.


και πιστεύω ότι είμαστε αρκετές. Γεια σας, καλύτερα και καλύτερα για έναν άλλο σημεριό της Σασάξ-Εξ-Vision Σεμίναρς, όπως όλοι ειδικά στην παιδίωση της Worldwide Neuroinitiative. Η Σασάξ-Εξ-Vision Σεμίναρς την νερά σε σύνε이다. Η Σασάξ-Εξ-Vision Σεμίναρς το μηρήθηκε σε τη διάρκεια της ΠολύΜπρος στο ποιοφέρ προς την παιδίωση της Σασάξ-Εξ-Εξ-Vision Σεμίναρς. Παραστάλαμε στο νερόaky, όπως μίατε απάντηκες μένα το Γιυνέδι και να βισθύ Jungkook. Οπότε, είναι ένας λόγος να βρεις για frogman για να βρεις μόνο από τον επίπεδο της στραβήθησης shall be our findings in your talk entitled connecting structure and function. In early visual circuits. So without any further ado from my site, please welcomeur baina. Ruthie, the stage is officially all yours. Thank you George, I'll start sharing, okay So thank you so much George for this nice introduction and I also want to thank time for the invitation it's really lovely to be here διακοτήσουμε κάποιοι. Αυτό το MyLab είναι εντυπίστητο σε νομική προστασία, με την νομική προστασία και εξαιρετικά, η δημιουργία μας είναι να εξηγήσουμε πρόσσυνοι σύνταρες που εξηγήσουν μοναστά τρόπινα, τελικά σε βασικές στενές, such as motion, color and so on. Για τη δημιουργία, θα μπορούμε να δημιουργήσουμε όλα οι διεξεδοχές, τα καταστηματικές, τα οποία δημιουργούν αυτή η δημιουργία, but also the neural circuits that implement these. And we have recently been more and more interested in understanding how different states and different stimuli can alter this representation and even more recently on how these representations can be used for higher cognitive functions. We do this in the fruit fly, not only because of the very well known advantages of this model system, such as genetic tractability, but also because of the increasing availability of connectomics data in the salmon, which has really, in my view, transformed our field. So we are experimentalists, but we collaborate very closely with theorists to build biologically constrained models that use connectomic driven synaptic information to understand how transformations are actually supported by real neurons and circuits. And so today I'm going to tell you about two recent stories from my lab where I hope you will see this thread of connectomics driven circuit investigation at play. But first I'm going to have to give you a short introduction on the fly visual system to get everybody on the same page. So the fly eye is made of 800 independent units called omatidia, each corresponding to one pixel in the field of view of the atom. Each omatidium contains eight photoreceptors, our one to our eight, our one to six form this trapezoid around our seven and our eight, which sit on top of each other and are in the same light path. Our one to six are involved in achromatic vision, whereas our seven and our eight are thought to be important for color vision. There are two types of omatidia in the main part of the eye that are distributed stochastically in the fly eye. So these are defined by the expression of wavelength specific options in our sevens and our eight. So in paleomatidia, our sevens express a UV-opsin and our eights a blue-opsin. And in yellow omatidia, our sevens express a different UV-opsin and our eights express a green-opsin. Our one to six always express a broadband receptor called RH1. Here are the relative spectral sensitivities of the opsin that we measure in the eye. You don't have to remember any of this. I'll have little diagram as we go through the talk. So now for the circuitry. All photoreceptors send their axons to the optic lobe where visual information is processed. This is a cross section showing the eye and the four neuropells that make up the optic lobe. Photoreceptors are in green. Our one to six target the first optic ganglion called the lamina. And our seven and our eight go through the lamina and target the medulla. Lobbular and lobbular complex present higher level of processing, which then lead to the central brain where sensory information is integrated and used to direct behavior. And this image I really like because it beautifully highlights the retinotopic nature of the visual system, which we use to our advantage in our experiments. So here's a schematic that I'll use throughout. But a lot of my lab, a lot of what my lab is doing is trying to figure out what kinds of computations are taking place in the optic lobe with a focus on the medulla and the lobbular. And for the vertebrate people here, I would like just to equate the level of processing that we're dealing with in the medulla at least to what probably happens in the vertebrate retina. The lobbular is quite, is still quite a mystery, but we're very curious at what's going on there. And just like in vertebrate retina, which is the role of which is partly to deconstruct the image into multiple representations within parallel circuits that each extract specific features of a visual scene, there are parallel pathways in the medulla that extract different features. And today I'm going to tell you about two stories, one about achromatic vision, specifically a circuit that extracts the direction of local motion, and one about color vision, a circuit that extracts information about spectral information. And so first I'll talk about the motion vision project. So this is the work of two extremely talented students in my lab, Jesse and Jacob. Jesse did all the experiments and Jacob did all the modeling and together they formed really a great team to tackle this problem. So local direction selective signals are very important and versatile signals. They're a building blocks that can be used by the brain for making much more complex motion signals. For instance, building optic flow signals, which can be used for self-motion estimation, which I think is nicely illustrated by this roller coaster video. These signals can also be used for object recognition as illustrated here. So in the still image, you don't quite know what's going on, but once you see the motion, you can recognize the dogs playing around. Okay, so local, in the fly, the first local direction, the first local motion detectors are called T4 and T5. T4 gets its inputs in the medulla. So here are many, many different T4s. Here I show you a clone of one T4 cell. And T5 gets its inputs in the lobular, same here, and they both send their axons to the lobular plate, which is a very important part of the brain in terms of motion detection. So both of them are direction selective, meaning that they respond to their preferred direction of motion and not or less to the opposite direction. T4 detects on motion while T5 detects off motion. And the question I'm trying to answer here, and in the next few slides, is how do these neurons achieve this property? So motion itself is not accessible by single photoreceptors since they each look at one point in space. What the brain needs to do is to compare luminous changes in space and time somewhere downstream to extract the direction of motion. Phenomenological models of motion computation have been extremely useful to think about motion. So I cite two very famous ones here, and they have in common that they use inputs that are displaced in space and also a so-called delay line that transmits information to a downstream direction selective neuron at a kind of a slower time scale than the other. So I'm going to give you an intuition of a very famous model in insect vision, the Hashansai and Raikar correlator, which works by enhancing the preferred direction of motion. So as an object moves in front of the correlator and activates the delayed line first and then the non-delayed lines, the two signals arrive at the same time at the next step, which detects coincidence and then you get a signal, a motion signal. As the object moves in the opposite direction, the delay actually separates these two signals in time further, so they arrive one after the other at the coincidence detection step leading to no signal. So this is a very intuitive model that actually accounts for many aspects of insect motion vision, but a problem with this and other models like this one is that they don't consider two key properties. One is the complexity of the processing properties of actual neurons that make up this circuit and the fact that neurons can adapt to various conditions. And the other is that they do not consider the connectivity of actual neurons in the brain. And so to stress the first point, I want to show you a simple toy model that shows, I hope, clearly, that incorporating more complex filtering properties can have a strong effect on the output of a motion detector. So here we have two inputs that are separate in space and for the sake of simplicity, I'll just add them up at the second stage of motion detection. In the first scenario, we use two low-pass monophasic filters to describe both inputs, one slower than the other. In the PD direction, the responses look like this and the ND direction, they're separated in space more and you can just sum them up and compare the output. And as you can see here, this model shows very modest difference between PD and D, if any. In the second scenario, what I'll do is instead of using one monophasic, two monophasic inputs, I use one monophasic and the second will be a biphasic bandpass filter. In this case, so you see this is the PD and the ND, if you compare the sum, you see that there's a large difference between the PD response and the ND response and this is because in this case, the trough of the bandpass neuron coincides with the peak of the low-pass one leading to some cancellation to happen. And so I hope that this convinces you that differences in shape and filtering of inputs to motion detectors can have some very important effects to the extent of direction selectivity in such a scenario. To illustrate a second point about the importance of incorporating our knowledge of the connectivity, I'll focus on the off pathway, which will be the focus of my talk. The pathway leading to T5 looks like this. T5 gets feed forward inputs from two columns equivalent to two points in space. The first one comes from TM9 neuron and the other from TM1, 2, and 4 or three of which look at the same point in space. So I'll schematize this like this from now on. It's important to note that all four neurons are cholinergic, so putatively excitatory. And many experiments have been done recording from T5 itself or from its inputs, but most models that have been proposed so far and I've added a couple of examples here, whether they're filter-based or conduct in space invoke some sort of direct inhibition. So although they can account for specific experimental measurements, they're still hard to interpret in terms of neuron implementation. So it's not quite clear in this case what the source of inhibition would come from. So we took a very intentional approach in my lab to incorporate these two aspects, the constraints from the structure, as well as kind of our maybe our hunch that the actual temporal properties of the circuit might be underscribed and matter more than what people have been actually using. And so this led us to think about adaptation because related to this is the obvious fact that motion circuits have to work in many different behavioral states and in response to a variety of similar statistics. And we know that sensory neurons adapt their processing properties in different ways, such as for instance, frequency tuning, gain tuning, or biphasic tuning, I would call here. And actually inputs to T5 have been shown to display at least frequency tuning and contrast gain adaptation. And these are the papers here that have shown this. And so our hypothesis from this was that accounting from T5 inputs, state and stimulus dependent adaptation could clarify the core motion computation in T5 and its implementation. So we decided to test this very explicitly. So what Jesse did was perform wholesale patch clamp recording of all four inputs to T5, to different stimuli and different states. In terms of stimuli, I'll show you some responses to white noise as well as some simple flashes. And in terms of state, we did this indirectly by adding the neurotransmitter dopamine to the bath while recording. And this is because motion circuits have been shown to be modulated by local motion and flies, which correlates with the release of octopamine. And octopamine has been shown to broaden and increase the temporal frequency of T5 neurons are sensitive to, presumably to match it to the statistics of motion of flying and walking flies. Okay, so now I'm going to get into the data itself.  So they're mildly bandpass. You can see they've got a prominent first negative lobe and a very shallow second positive lobe. And you can see this better when you look at it in frequency space. These are the parametrized versions of these filters. They all peak slightly below one hertz, with TM9 being the slowest of them all, which was known already, of course. When adding octopamine, we see this very interesting change in the waveform of the responses that all become narrower and speed up and TM1, TM2, and TM4 acquired this very obvious second positive lobe, making the filter just much more biphase. And then the frequency domain, this corresponds to a shift towards the faster frequencies of motion, as well as a narrowing of the bell-shaped curve. And I think one of the interesting properties that comes out of this experiments is that octopamine spreads the sensitivities of these four inputs, you know, across the frequency space, which is something that's actually seen in T5 and could be a reason for this apparent redundancy. I'm not gonna show you a spatial filter or non-linearities because we've measured them, but they don't change much. So, but we'll use them in the models eventually. So then the question now becomes, can we use these linear temporal filters extracted with the white noise to predict the responses of these neurons to different stimuli with different statistics? For example, if we convolve the temporal filter or temporal filter with the flash stimulus of a specific duration, can we predict the responses of these neurons? What we do is that we also record, of course, from these neurons to this flash and compare the two. Okay, so this is what we did here. Here are the white noise predicted responses of TM1 to flashes of different durations and dash lines. And these are the actual responses that Jesse recorded from TM1. So it's very clear that white noise filters don't do a good job at describing these responses. TM1 responses are much more biphasic to these flashes and the short duration flashes have responses with much higher gain than expected from these white noise responses. This is the data for the rest of the cells. TM4 and to a lesser extent, TM2 have similar properties. TM9 doesn't acquire a biphasic response, but both the shape and the gain are not well described by these white noise filters. So, but then we thought, these maybe the flash responses with more similar contrast changes to white noise, where contrast steps are very small from one presentation to the next, might be better predicted by these white noise filters. And so that's actually exactly what we found. Jesse used low contrast flashes as opposed to these high contrast ones. And you can see throughout that the responses are much better predicted by these white noise responses, by these white noise filters. Okay, so to summarize the data here, we indeed find signatures of frequency, gain and biphasic tuning in all inputs, most inputs and importantly, yeah, so we find frequency and gain tuning in all inputs and this biphasic changes with CNTM1, TM2, and TM4 specific. And I think it's important to point out that we're still making very discrete measurements of the temporal processing properties, but we think that these neurons are actually spanning a space of parameters which we've tried to illustrate here. I won't go into the detail of this because there's a lot of data here, but I just wanted to illustrate the fact that if we look at the processing properties, the temporal processing properties of each of these cells in a space where we look at biphasic tuning and speech tuning, these cells span this space. It's very clear also that TM9 changes much less than the other cells with this kind of depiction. In terms of stimulus dependent, dependence, we think that it's information content that's the main driver of these moves that push the cells towards specific points of this space. And it's something that was pointed out also by these two papers, which look at inputs to TM themselves. And so the idea is that in low signal to noise regiments, you want to integrate in time corresponding of a monologued filter, whereas in high SNR regiments, you might want to transmit information about change to reduce correlations. But so these are things that will need more work to be established at the level of these cells. Okay, so we have our date, but now if can we explain the output of T5 across stimulus and state using what we know now about the responses of the inputs. And before building our motion model, we wanted to get an intuition as to whether we could capitulate T5 responses as a combination of our recorded inputs, starting with a simple linear sum. So Eyal Grotman in the Reiser Lab performs some beautiful experiments recording from T5 while presenting flashes at different locations of its receptive field. So the responses are very large in the center and get smaller as you get further away from it as expected, but something you found that was not expected is very interesting is that at the trailing edge of each of these cells, the responses are biphasic. They depolarize and then they hyperpolarize. And we wanted to see if we could explain these responses specifically this asymmetric property using our recordings. And so Jacob performed a linear regression using our input measurements as variables with the only constraint that it should be a positive relationship. And so to see if we can match, because we wanted to match this to the excitatory nature of the inputs. Okay, so this is the data from Eyal and dashed lines at six different positions. Jacob only used two inputs, TM1 and TM9, because TM1, 2 and 4 are so similar. And so this is the result of the linear regression using the white noise prediction. So as you can see, we do okay, but we really don't manage to reproduce this asymmetric hyperpolarization here. We then try the flash responses, which are closer in their statistics to the flash stimuli that Eyal used in his experiments. And in this case, we do better but we still understood the hyperpolarization to the average responses here of T5. And then we thought we could use the Octopamine extrider flash responses. And in this case, we do very, very well at reproducing these measurements. And so we were very excited about this because our flash responses are more similar to the flash, the bars that Eyal was using. So it kind of fit with our hypothesis.  The idea is to predict the responses of T5 to various stimuli. And we had a number of very important constraints that come from everything that I've told you earlier in this talk. So these are the ones for input to start with. TM9 is spatially separated by TM1, TM2 and TM4. All inputs are excitatory. We use connectome weights to constrain our model. And we also use our measure process and properties of inputs that vary with stimulus and state. So we try to match inputs filtering properties to the stimulus that's used to probe T5. And finally, we use just a simple linear summation informed by the success of our linear regression to the static flashes. So the first thing we did was to simulate the responses of T5 to moving sine waves. So first we use our white noise filters to describe the inputs because we can actually show that those account very well for our measure responses of these four inputs to actual sine waves. This is the response of T5 to increase sine wave to sine wave of increasing temporal frequencies moving in either the PD or the ND direction. The curves peak just below one hertz as we expected and PD is larger than ND showing a direction selectivity. Then we did the same thing using the filters that we extracted in octopamine. And as expected again, these peaks shift to the right towards faster speeds and you still see the directions selectivity. And so from this we plotted the directions selectivity index of our model using temporal frequency across temporal frequency and compared it to actual data from two voltage studies from T5, from the Klandinen lab and from the Reiser lab. And our model recapitulates the data pretty well both without octopamine and even better with octopamine in terms of the extent or the magnitude of the DSI responses. Then we wanted to check whether the connectome constraint was important. So what Jacob built was to build models with random weights and we see the average is here and you can see the performance decreases quite a lot showing the relevance of these weights in our model just to specify our model has very few parameters. So we next use our model to predict the responses to T5 to high contrast moving bars. We first use our white noise filters with the intuition that this would not work well and that is the case. If you look at the responses of our model to 80 millisecond moving bars or 160 millisecond moving bars you see that the PD and the ND responses are very similar. If we use the flash responses we start seeing some direction selectivity and this gets better when we use the OA extractive flash responses. Here is a summary of this data compared to the data from EL's paper. And as you can see with the white noise filters we really don't do well but then when we start matching the stimulus the properties that we've measured to the stimulus that was used to measure for T5 then we do much better. Okay, so now I can summarize this part of my talk. I think what I've shown is that direct inhibition first of all is not necessary to reproduce T5 responses across stimuli. We've also shown that a simple linear core computation is sufficient for direction selectivity at the level of T5 but it's very important to account for adaptive non-linear properties of inputs. And therefore something to mention is that like a thorough description of the filtering properties of inputs to motion detectors rather than this simplistic delay is really important. And I think finally the knowledge of the synaptic connectivity in this circuit is also very insightful. Okay, so now I'm gonna move on completely switched gears and tell you about the second story which focuses on color vision. So specifically circuits downstream of wavelength specific are seven and are eight for receptors instead of these broadband neurons that I've been telling you about. Before I get into this I want to acknowledge the two wonderful students who together led this project. Again, this is a theme in my lab, Sarah and Gouki. So Sarah did all the experiments and Gouki is behind the analysis and the modeling and stimulus design. Okay, so spectral information is very useful to understand the world around us. One of the most obvious uses of spectral information to add information when a chromatic contrast information is just not enough. For instance, here in this monochrome image it's very hard to see what's going on. You can see leaves and trunks of these trees but if I add the color information now the berries pop out and really it's thanks to a spectral information that we can get a lot more information about our natural surroundings. So one of the most important concepts in color vision is the principle of univerity. So if you consider the spectral sensitivity curve of particular photoreceptor which represents the probability of absorption of photon as a function of wavelength and you shine this purple light close to its peak or its brighter green light closer to its tail you'll get the same response. So there's a confusion between wavelength and intensity when you're dealing with a single photoreceptor. Because of this an observer with only one type of photoreceptor is colorblind it will not experience color and we'll see the world actually in grayscale. The way visual systems can start dealing with this problem is to add another photoreceptor with different but overlapping sensitivities such as this green photoreceptor here. And these two photoreceptor will respond differently to these different combinations of inputs and the signals and using these signals the system can tease apart wavelength and intensity but this can only happen if somewhere downstream these signals are actually compared in the visual circuits. And this comparison is apparent in neurons called color opponents which get antagonistic inputs from different photoreceptor types and are therefore activated by a range of wavelengths and inhibited by a different range of wavelengths. Color opponent neurons with this type of tuning have been described across the animal kingdom but much of what we know about color processing has come from working trichromatic primates. So there cone photoreceptors coming through flavors short, medium and long wavelength. These signals are compared in the retina and the LGN in two main color opponent axes. M and N versus L axis which is this red-green one and the M plus L versus L which compares blue and yellow. Deeper in the brain various chromatic signals exist which start to encode specific aspects of the quality of light. Perhaps most notably these hue-selective neurons are found in IT cortex and they're very interesting because they respond to very narrow range of wavelengths. And whereas the transformation between from cone responses to color opponentcy is starting to be pretty well understood involving horizontal cell circuits that connect neighboring cones the transformations that take color opponent signals and make higher order chromatic signals such as these hue-selective cells is not well understood. In addition to how any of these types of signals relate to actual color perception there's actually still an open question in the field. Of course these are not easy questions to answer and one of the main aims of my lab now in moving forward is to use Drosophila to understand kind of fundamental principles of chromatic encoding. So going back to my schematic the color vision of course starts with four receptors, R7 and R8. I've put the tuning here again and I want to point your attention to the axons of R7 and R8 here in the medulla. There are 7 and R8, target two different layers in the medulla but each pair of R7 and R8 from the same homotidium in the eye here occupy the same column in the medulla. And so EM reconstructions have actually identified axonal synapses between R7 and R8 coming from the same homotidium so from the same point in space in the visual field of the animal.  So these antagonistic interactions between R7 and R8 are of course well poised to support the emergence of color proponency. And indeed the lab of Derek Reif found that these synapses are functional and showed very nicely that these lead to two types of color opponent signals. One that compares UV and green and another one that compares UV and blue. However, the direct interactions are not the only ones that can support oponency in the medulla. Indeed the same EM study also identified another type of a new type of neuron DM9 which both gets inputs from R7 and R8 and sends outputs back to R7 and R8. DM9 is called energetic so puritively excitatory which I've depicted with these arrows here. Has a really interesting shape it wraps the terminals of on average six to six R7 and R8 very closely in the upper layers of the medulla and I'm schematizing this DM9 like this in the rest of my talk. So we hypothesized that in addition to this direct columnar pathway there's also an indirect pathway akin to a horizontal cell like circuits in the vertebrate retina that will allow comparison between ometidia so inter-columner comparison and add to the richness of the comparisons that a fly acts on the full photoreceptor axons can actually achieve. So here's a bit of a recap of everything I've told you at the level of the raptomers we know the spectral sensitivities we know that there's a circuit that does account for oponency and there's a neuron DM9 that we want to know what actually its role is there. So I'll use this little schematic to frame the rest of my questions. So the first thing that we wanted to do is to measure the axonal tuning curves of these axons. So this hadn't been done. The DM9 prediction was that we would find a color of point and axis in addition to the UV visible ones that were already described. Then we wanted to relate the circuit connectivity to the function of these axons and then we wanted to ask if we can mathematically define the transformation that happens between the raptomeric and axonal responses and build a model that can actually explain this transformation and take circuit into account, circuit architecture into account. And finally, we wanted to ask what's the function of all of this? So I'm going to step through these questions one by one. So first, let's build the tuning curves. To do this, Gouki set up the following experimental system. So here are the spectral sensitivities. Again, we wanted to be able to excite these as independently as possible. So we chose six LED spanning from DPV to orange and we built a color mixer, which allows us to present full field flashes of light either alone or from these LEDs either alone or in combination to a fly that's mounted on the object of a two-photon microscope. So our goal is to measure the spectral tuning curves of our seven and eight outputs over the fly's visible spectrum. So ideally we would like to show many different light sources with narrow tuning but we only have these kind of broader elements. So how do we do this? The idea is to make use of the principle of univariance that states that once a photon is absorbed its identity is lost to mimic the effect of a target light source onto the fly's array of spectral sensitivities. And so what we do first is to consider one of these wave lengths and calculate the photon capture that this light source will elicit at the level of DI which basically gives us a five dimensional vector in photon capture space. And then to figure out our combination of LEDs will achieve the same vector. We just use a simple linear regression to get the weights to say the intensities of each of the LEDs we need to use to kind of mimic to get as close as we can to this point here in the space. And this is really basically the same ideas as the way that your screens that you look at work which can use three LEDs to give you the perception of million hues but with our method we can do this for any animal for which the sensitivities of options are known. And Gouki has actually since then greatly developed this method. So I'm plugging this in here. We have a preprint where he explains these algorithms extends them to allow the users to deal with uncertainties and spectral sensitivities to reconstruct natural images and so on. So he's also worked to make a web app to make this very easy to use. So I hope that those of you are interested in color vision we'll check it out and maybe even give Gouki some feedback. I'm sure he'll be thrilled to hear from you. So now that we have a method to measure it here are the measurements that we did at the level of the axons of our seven and our eight photoreceptors. As you can see they're all opponents As expected, of course, our sevens compare UV and visible with slightly different tuning curves. Yellow rates also compare UV and visible with flipped tuning curve. And pale rate was very interesting to us because it was the only one that showed this trilogue shape. It's activated by blue, of course, but it's inhibited by UV and also by green. So we wanted to also measure the raptomeric responses to eventually be able to relate the two. So we can't directly report from raptomune with our methods but we use a genetic trick that I won't go into for the sake of time that allows us to measure cell autonomous responses which should correspond to putative raptomeric responses. So here they are, as expected, they're only excitatory. And we wanted to be sure that these curves would directly relate to the spectrosensitivities of the absence that each of these photoreceptor expresses. And this relationship has been shown by others to correspond to simple log transformation of the photon capture. So if you plot that, they really match perfectly well which is really a nice control of our methods as well. Okay, so now we have the raptomeric responses that are color opponents. And I can point out now that there's at least one opponent axis which cannot rely only on these direct intra-material connections. This blue-green comparison and pale R8s and that needs to rely on maybe intra-material interactions within this circuit. So we wanted to test this hypothesis and also look at the role of TM9. So we did many genetic manipulations. So I'll show you just a few, very targeted ones focused on R8 which is the one that is very nice because of this trial shape that helps us interpret our results. So this is the control which I'll show in black from now on. So we made flies where only pale R7s and pale R8s are active, as well as TM9, of course. And in this case, we keep the inhibition in the UV but lose it in the green which fits with our model. The opposite is true when only our R8s are active which also fits since here we see that we keep green opponent C but completely lose the UV opponent C. I should also mention that we've looked at the contribution of our one to six and we don't see any contribution to the way from pale R8s here. So in pale R8 we see a signature of both intra and interometidial interactions. And then we did a series of experiments to confirm the role of TM9 here because so far there's nothing that tells us that TM9 is the one who's doing this. So here's a control. The first thing we did was just to very simply silence TM9 and as expected, we lose completely the opponent C in the green and maintain a little bit of opponent C in the UV. This is because of, sorry, that there's a mistake here at the R7 and R8 in trauma-tidual interactions are still here in this case. So then we did another control here but what we can do is to basically remove all the histamineergic transmission which basically abolishes both intra and intra-metidual interaction but then we can restore newer transmitter receptors only in TM9, restoring only in intra-metidual interactions while leaving the reciprocal inhibition impaired. In this case, we still have some opponent C in the green but also in the UV. You just have to compare the dashed lines and the black line to the actual response. So this shows that TM9 is sufficient to drive this intra-metidual opponent C. Okay, so far I've shown you that we're dealing with a dual circuit composed of intra- and intra-metidual interactions and now we wanna build a model. So before getting into the circuit structure, again, the same way that we did with the motion model, we wanted to ask if we could express axonal responses with a simple linear sum of raptomeric responses. And so with an unconstrained linear regression, Gouki shows that using our raptomeric log of Q responses as independent variables, you can see a linear transformation that can account for this neural transformation. So then the important question we wanted to ask is whether we can constrain our model with the architecture of the circuit, which I've schematized here again. So Gouki built a linear recurrent network that's constrained by circuit connectivity and the signs of the circuit and he fitted to the steady state of our responses. And so we call this a fully parameterized model. You can see it works well. But then we also wanted to constrain the model further by using synaptic counts as a proxy for synaptic weights the same way as we did this in the motion model. So Gouki just plugged in the synaptic counts instead of the fitted weights. And as you can see, this works very, very well. So this was a very nice result, but it could be that the model is very robust and that any weights could work and to control for this, he replaced the weights in our model with randomly drawn weights as we had done also in the motion model. And we can see that our model with the synaptic weights actually performs much better than the random weights. So we figured out how this circuit works and now we just wanna understand why it works. Why it works the way that it does. So I think it's really important to ask this kind of question. And so the first thing that we did to answer this was to compute the correlation coefficient between the different photoreceptors at the level of the wrapped news. And you can see this is very red because there's a lot of correlation between the photoreceptors, which is simply due to the high degree of overlap between the sensitivities of the options that they express. But now we can do the same for the external response and show that there's much less correlation between the external responses, which is a very useful thing in terms of signal processing. So clearly these opponent circuits have the benefit of the correlating photoreceptor responses but now the question is, why do they do it the way that they do? So why do they use opponent responses that vary along two main axes, one comparing UV and visible and the other one comparing UV and green? To answer this question, we were inspired by a very influential paper by Bushbaum and Gottschalk, who showed that the axes of opponents that are measured in the human retina, the blue versus yellow and green versus red correspond to the principal components that are obtained by PC on the spectrosensitivities of human cones. This very simply means that by recording this chromatic signal from three channels to two opponent channels, it's optimal in the sense that it reduces redundancies and the dimensionality of encoding was still capturing kind of a maximal amount of information. So we did the same thing basically and performed PC on the spectral sensitivities of the opsings expressed in R7 and R8. So the first principal component is achromatic, of course, and which accounts for about a half of the variance. The second opposes the R7s to the R8s corresponding to a comparison between UV and green part of the spectrum and the third compares RH5 to the UV and the green parts of the spectrum. And these first two chromatic PCs together with the achromatic PC explained 97% of the variance. The last one is it compares RH3 and RH5 to RH4 and RH6, but has very tiny explained variance. And so it's very interesting of course that the two first chromatic PCs broadly described the two types of responses with measure at the output of R7 and R8 and I've shown them here overlaid. And so again, here just as in the human retina, by aligning to these two axes, opponent mechanisms can efficiently decorrelate chromatic signals, reduce encoding space, but also allow for nearly full reconstruction of chromatic information. A small caveat to this analysis is that it assumes a flat spectrum which is not taken into account the reflectances of naturalistic scenery. So what Gucchi did was to perform the same analysis using a set of natural stimulus spectra, flower reflected systems, those are very easily available. And in this case, PCA on all four channels gives basically the same principle components. And then when you, when he projects the hyperspectral reflectances onto this PC1 and PC2, they're very nicely spread out as you can see. He did the same thing on the pale and yellow inputs separately. So the decomposition of inputs that segregates pale and yellow pathways and the two chromatic components here that we call CY and CP obtained that way are still somewhat trivially correlated. And you can see this when we look at the actual reflectances. And this shows that a full circuit combining both intra and entoro material interaction displays a more complete opponent mechanism. And so to summarize the second part, we have identified a dual circuit for color opponents at the level of Drosophila for receptor outputs, an intra or material kind of inside specific circuits mediated by direct synapses and entoro material evolutionary convergent circuit mediated by DM9. And we show that these two opponent axes allow for efficient and comprehensive representation of chromatic information. And just as is the case with the motion circuit, the constraint that are provided by the circuits of the brain really allows us to understand and describe these behaviorally relevant circuit computations. And before my last acknowledgement slide, I just have one little plugin here. We're recruiting, so if anybody is interested in doing a postdoc in my lab, we also have an opening for research technician, please email me. And if you want to know more about what we're up to these days, we have three posters at Cosine, which is coming up very soon. So please go talk to Gouki, Shani and Sharon, who will be talking about this color space geometry and the stimulus design, some normative models that try to explain the proportion of photoreceptors in the fly eye and spectral information for navigation. So with this, I would like to thank everybody in my lab as well as our collaborators, which are very, with a very special thank you to Larry Abbott, with whom we collaborate very well on these more theoretical aspects and our funders as well. And I'd like to thank you for your attention. Thank you very much, Rudi, for this wonderful presentation of two distinct but not really distinct stories of motion and color. Probably people think that I'm doing a bad job, usually asking questions. So they are keeping their questions to ask them live themselves. So I will be posting the link of the Zoom room we are currently sitting in so people can start joining us and then I will start with my questions because you mentioned the color story second, I will start with that. Do we know anything about the population or the dendritic spread of the DM9 neurons? Yeah, so from work that was done at Janelia by Alyosha Nern, we know we have a very nice idea of the span. So on average, each DM9 spans about six, seven columns which basically would correspond to kind of one central omitted dim and a ring around it. It's not quite clear that this is a structure that DM9s have, some of them are more kind of longer than others. So we're kind of looking into the diversity of these DM9s. And of course that will tell you something about integration and about maybe centers around receptive fields and things like that that we've started to try to model as well in terms of the opponents in space which I have not at all touched upon here which is part of ongoing experiments in my lab. Yeah, and it makes sense like to follow down that direction. The first question that appears from Tom is first he says very nice talk. You mentioned the huge cells that might be expected further into the brain but do you happen to see them? We are working on this and we have some really interesting data which hopefully we'll be able to share very soon. Sorry for the confusion. I think so we do see more specific signals later on. So we are very much interested of course on the whole pathway that's downstream of these cells as are other people in our field but I think some very interesting data will come out soon. Great, thank you very much for that clarification. So people start jumping in the room already and at this point before I continue with the questions I would like to let you know that there are a lot of people thanking you for your talk. Simon Laffrin is already here with us. So I would like to go to the direction story and because you tried to make models that are biophysically and biologically constrained I was wondering whether we know anything about the integrative properties of the T9 neurons like if they somehow post-synaptically can modulate the input they get. TM9 neurons, you mean? The TM9, sorry, not the TM9 the T5 neurons or the T4 neurons. A lot of work has been done downstream in the lobular plate with integration of these T5 and T4 neurons in kind of a higher order motion signal such as these like optic flow signals or even looming signals. So a lot of work has been done downstream. I think, you know, focusing on kind of the core computation is because historically there's been a lot of debate on what's going on. And I think this was really my interest there. And I do think that, you know, even though we haven't answered everything, of course, I think it's kind of the looking more closely at the adaptive properties of these neurons is really giving us a clue on what's going on at the level of T5 itself. Right, so but like when it comes to single cell T5 processing of the inputs, we don't know much because in the model, it's not included, right? Like I saw you mentioned... No, there's spatial information included. Right, the synaptic weights, right? No, but also the receptive fields of the inputs are also included. Right, OK, OK, thank you very much for that. I'm sorry, I did not understand your question, it seems. And then another question I have is because you mentioned that the octopamine is responsible for perceiving optic flow. Do we expect like also circadian changes in the level of octopamine that's expressed? So it's very interesting that you talk about circadian. So we haven't looked at circadian involvement in the motion pathway. It's true that the different times of day you might have different kind of overall intensity, but that I think is taking care of with contrast gain adaptation or different things that are happening at the level of photoreceptors themselves that, you know. But I think the circadian part is more interesting for the color pathway and definitely we're looking into different newer modulators. So more like serotonin dopamine that might be involved in dealing with circadian changes and affecting color circuits. So we've been more looking at the circadian effect on color processing. And I think we've seen also some interesting data. You would expect maybe some shifts in the tuning and we're seeing things like that. So before I continue with another question that Tom posted and he's taking us back to the color story, I would like to remind our audience that they should be joining us in the Zoom room in case they want to keep track of what we will be discussing shortly. So I will be stopping the live broadcast and the next part like the informal cheat set will not be recorded. So before I leave the stage for Simon or Tom or whoever wants to join and ask questions directly, one last question from Tom is, do you have an overall spectral tuning function for the brain? It's a green versus UV, which activates more stuff, which of the two? An overall tuning of the... Sorry, I don't quite understand. Yeah, so the question, and maybe Tom wants to clarify because he's here already, do you have an overall spectral tuning function for the brain? What do you mean, Tom? So imagine you've got decamp in all the neurons of the brain and you flush different color light, different wavelengths light. Some wavelengths are going to be much better at activating stuff than others, right? So like for example, if you do this in the fish, UV and red work really well and the in-betweens don't work so well. Like do you have that level data at all? We don't have that level data yet, but we do see, for example, as you... I think, okay, taking a step back. I think at the level of the brain itself, I don't know because what happens downstream of these oponent circuits is that the information gets basically split into different pathways and used very differently by different pathways. So one of the things that we're studying, for example, is UV versus green oponency in navigation. So it's not that... I think different parts of the spectrum or different types of comparison are used for different behaviors. So I don't know that I can talk about a brain sensitivity. Do you know what I mean? Some behaviors are going to care more about short spectrum, some more about long spectrum. And so we're really looking at this as kind of at the level of the outputs, then you spread all this information amongst different... You know, even circadian, right? You can enforce the circadian clock by knowing what type of light you have in the day. So that's another pathway. And in this case, you want to compare UV and green. So I think the UV and green comparison is like a very important one. Sorry, the UV visible is a very important one. Okay, thanks. So as there are no more questions appearing in the chat, I have one last by myself before I stop the broadcast. Again, going back to the direction story. And I was wondering whether we know... Like, you saw that when it comes to the subspace of parameters, T9 stays way more local compared to TM1, TM21, TM4, right? Do we know more molecularly why that might be the case? Actually, the story is a little bit more complicated. So you're talking about TM9 looking at one point in space. And TM1, TM2, and TM4 also look at one point in space. So I don't know if that's what you're getting at, but there's some really nice data from a Marion Silas that shows that some TM9s actually look at wider parts of the visual field. We see this also in our data. We've kind of separated those cells out. We don't really know what they could be doing in local motion detection. So I think that there's more complications. Like, I was trying to streamline the story as much as I could for kind of a broader audience, but there's a lot more complication there. Right, yeah. I was going back to the diagrams that you had, like there was a lot of information. And then like for the TM9, it looked like it is inhabiting a smaller space with respect to the stimulus parameters than the other... Oh, sorry, that, yes. Yeah, so we don't know why that is the case. Maybe you need one of these inputs to be much more stable, I guess, and to really allow the rest to shift so that the circuit can actually deal with this. We don't quite know... And we don't know what the cellular and molecular foundations of all these adaptive changes are. So there's some work from Tom Flanlinen's lab that shows that a circuit might be involved starting at the inputs of these TM1, 2, and 9 themselves. So L2 shows this biphasic shift. So there's something going on that we don't quite understand. It'll be really interesting to see what is the cellular and molecular basis for these adaptive changes. Right, right. Thank you very much, Rudy. At this point, I think I will be stopping the live broadcasting. So thank you very much for everyone that joined this talk. Again, thank you very much, Rudy, for presenting these really nice stories. Thank you. So I'm stopping the stream and I'm officially waiting.


So, that's for my contribution, we have some time for a few questions on technology ethics and economy in healthcare. Yes. Thank you very much. My name is Randy Cotier, I'm a graduate student at the Kennedy School and an incoming French public servant. My question is, during COVID we've seen how technology has become a key and strategic capability, but the question is, who should develop those technologies and who should own them? Should it be the private sector for maybe efficiency concerns and also for the fact that there will be more independence on that or should it be more on the state side for ethics concerns actually? Because people would maybe trust various states more in some places or in other places would be more. Apple, Facebook. Thank you very much. Thank you, Jacques. You want to take the question? Yeah. Well, thanks for the great question. Actually, I think I gave a pretty pessimistic view of the regulation of the system, but there are good news and the good news is that there is plenty, there's a big reservoir of productivity due to augmented intelligence, which would make it possible to get rid of many of the inefficiency in the system. Now, the question of who owns that is a big question, you know, and again we lack time and I think this would be an encouragement to Thierry to go ahead with more thoughts being given by E3 to healthcare issues. I currently have a project, for instance, which leads to roll out artificial intelligence into pathology and the question is who owns that? Is it the engineer, the physician, the people who provided the data which made it possible, the hospital, et cetera? So it's a big question. We don't have much time. I think in the end the profit has to be shared between the various players. Once again, the good news is that there is a reservoir of productivity and so we should all work together, first of all, to see how it is possible to really grasp this reservoir and how we share it in an intelligent way and in an equitable way. So I defer this question to another meeting, Thierry, if you are interested in going ahead with this theme. We have a question from Michel as chairman of the previous panel. I wonder what you have to say. Thank you very much. It's a question to Dr. Keeman, Carlos Moreira, maybe, I think beyond the technological progress and beyond the issue of consent, I think what you've been talking about is a major revolution to me, which is that these technologies will lead to self-responsible management of health by the people. Until now, the patient somehow, and the name patient, the word patient is speaking by itself, was a passive object versus the healthcare system. Now the patient will become the responsible actor, and that, of course, has a lot of implications in terms of what's his source of information and whether we should leave the decision to the patient. And I wonder whether you can comment on that. Can I go first? Yeah, is that a question for me? I will go first. Actually, in the diagnostic field, there is a self-testing system. The rapid immunosake kit is one of the examples. So the FDA has a process for self-testing for a clear test, clear wave of the test, that kind of things can be done by simple process. For example, if you have the one drop of blood, and then you can see the band, one band or two band. If you have a single band that is negative, if you see the double band, it's positive, very simple for the determination. That kind of simple system can be applicable at home. If I just completing the answer, I think that it needs regulation. The fact of moving from passive, which is what we should be, to be an automatic default login into somebody assessing our health, is something that can only be resolved by legislation. You know, there's HIPAA regulation in the United States, there's other regulation in Europe, but that's actually very vague in who owns the data. The data should be owned by HOS, by the patient, not by the system. If we own the data and it's under our consent, then we have our protection. As we have it everywhere else, we have it in the financial system, right? The bank is not authorized to do anything without our consent. That level of consent needs to be installed in the health industry. Now it's not the case. Now the data belongs to the health institution, and this is wrong. The data should belong to HOS, and we can give them their consent to use that data. If the data belongs to them by default, then by default, we will let artificial intelligence and other technologies to data mine that data. Okay, thank you. I think we come to the end of the session. Thank you very much to my colleague, Panelitz, on the insightful presentation for your question. And as we have the bus in the room, I would not dare to overrun, so... Thank you very much.


The President's budget proposal eliminates public media funding. Learn how you can help at lptv.org slash action. A crash in Ironton Thursday afternoon involving a school bus has sent two people to the hospital. Shortly after 3 p.m. yesterday, Crowing County Sheriff's Office received a call regarding the crash on Hematite Street in Ironton involving the bus, one other vehicle and a local business. The driver of the bus, 68-year-old Gerald Fisher was believed to have suffered a medical condition causing him to lose control of the bus crashing into a parked pickup and trailer, pushing the trailer then into a business and injuring the owner, Bud Hansen. Fisher and Hansen were transported by ambulance to a local hospital with unknown injuries. There were 27 children on the bus at the time of the accident. None of them were injured and were safely transported home by another school bus. If you've enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland Public Television.


Up until now, we've been using arrays. They're very useful, but they have one problem. They have a fixed size. You have to know in advance how many elements the array will have. What happens if we need to have an arbitrary number of items? For that, we use an array list. Let's write a program that asks the user for prices until they enter zero, and then list those prices as a percentage of the maximum value. To use array lists, you need to import java.util.arraylist. To create an array list, you must specify the type of object that it contains, and that type must be a reference type. In our example, we'll want an array list of double objects, with a capital D, because those are reference classes, as opposed to small d-doubles, which are primitives. You specify the array list type in angle brackets, and then you give the array list a name, and say that it's a new array list. Use the angle brackets again, and parentheses. If you want to, you can repeat the data type inside these angle brackets, on the right-hand side, but you don't need to. Here's a do-while loop to read in the numbers until we get a zero, the sentinel value. We'll set a small d-double, one price to 0.0, and then put up the prompt, and read the value. If the price is greater than zero, then we'll add it to the array list, and we'll do that as long as one price is not equal to zero. To add an item to an array list, use the add method, priceless.add one price. Hold on, you might be thinking. Didn't you just say that we need capital D double objects inside our array list?  What's going on here? In a similar way that Java promotes an int to a double when one is needed, Java automatically converts a primitive to the corresponding object when the context demands it. In this example, the right-hand side, 29.95, is a primitive, and the left-hand side is a double object. Behind the scenes, Java is actually doing this. It's calling the value of method in the double class to wrap the primitive 29.95 as a double object. This conversion is called boxing. The reverse from an object to a primitive is called unboxing. In the second statement, we're multiplying d-price, an object, by .90, a primitive. Behind the scenes, Java is unwrapping the object, extracting the primitive value, and using that in the calculation. And that's why it's okay to do this. Our primitive one price is being put into a capital D double box, and the price lists add method is happy. Now let's do the rest of the calculations, but only if we really have items in the price list. We'll set the number of prices to be price list dot size, and that's the method that tells how many elements are currently in the array list. If the number of prices is greater than zero, then we have work to do. Otherwise, the user didn't enter any data. We use the get method to get the first item in the price list as the current maximum value. We'll set double max price to be price list dot get zero. Just as with arrays, the first item in an array list has an index number of zero. Now go through the remaining prices to find the maximum. For int i equals one, i less than n prices, i plus plus, if price list dot get at that index value is greater than the maximum price, then the maximum price becomes that value. The next thing I'm going to do is go through the price list again and change it in place to percentages. I'll use the get and set methods to do this. Technically, I don't need to change the array in place. I could print out the percentages without storing them back in the array list, but I needed an excuse to show the set method. Let's go through the entire array list starting at zero and calculate the percentage of the maximum to be the price list item at the given index divided by my maximum price. And then I'm going to set the value at index i to that new value that I calculated. By the way, there's a lot of automatic boxing and unboxing going on in lines 36 and 37. See if you can figure out where it's happening. Now it's time to print out the results. And I'm going to use a different way of iterating through the price list. I'll say four double price in my price list, system dot out dot print f. Let's use three places to the right of the decimal point and a percent sign. Because percent sign is special, it introduces formatting. To get a true percent sign, I need two percent signs in a row and print the price. And then make sure that my output ends with a new line. Let's compile that and let's run it. Let's say $3.95, $20, $17.95, $19.95, and $7.25. And then that's our last price. And there are the percentages of the maximum price. When you're working with array lists, get and set are the two methods that you'll use the most. The ArrayList class has other useful methods, but that's the subject of another video.


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Ni maizuwa wajimiwa ni maizuwa wajimiwa ni maizuwa wajimiwaM. Naji mida maimutaziami ni maizuwa wajimiwa ni maizuwa wajimiwa ni maizuwa. Blak Arewe ni maizuwaa? Nuri wajimiwa ni maizuwaa? Blak Kupihed, black kid. Hama, we are still in that mode of Wellington, so you black kids are also part of Wellington Colour. Black is a new nad. It is the new red. Black is the new red. Black nad red na pilekana is used. We got there. Me niko? Kusunlingi miyakany lini. Too many years. So Nikwaan bia hapiniu yam marambili. Please. Hapiniu yat twenty-three. Hapiniu yat twenty-three, four. I am Merry Christmas yat twenty-three, I am twenty-twenty-two. Baki ni tafadali, ususe me, Merry Christmas, kwa uni pi Story, Kamas Organ is in Aukuja. It's also the story you told me. кру复picious half four苦 higu or kan ma na dos na ma I am not having this discussion with you guys anymore I have moved on We are going to eat kek before the math ends When is the date? The whole of february Manu treki kwa sema date Laki na mezema ni mozimzi Let's talk about you Benzi Too many days You haven't been as busy as I thought I have been You have been? Actually I have not been I know I have been too idle What was happening? A lot But the biggest thing is that we are planning to do I love those kids I was trying to build a brand out of them It's not as easy It's not easy I have tried to build a brand out of them But they are still young So juggling that is a bit difficult We are going to shoot a video We are going to build a brand out of them But I am back And the last time you said you were going to bring those kids? I am going to bring them We are not going to build a brand out of them We are going to high school So it's a bit difficult I want to bring them when they have content I want to bring them when they have content I want to bring them when they have very nice content At least to quet when they have ideas Content or not I still love those kids I really love those kids Besides the kids What have you been doing? I have been building something I want to work on Bensi's music So I created There is an EP coming I want to work on a church girl So I said, hey, I want to make a music video Let's work on something else But finally we got it We are working on an EP Bensi For the church girl I want to join the Bazuka Tiawa The continuation has to come So we will drop it in Valentine's We will drop it in Valentine's We will drop it in Valentine's It was just two days ago We can still do it We will drop the lines The thing about Bensi Music is just She is the music She is the music I want to be a Bensi You are a motor You will have to take your time I know I will not make any sense I will not make any sense I will not make sense I will not not make sense We are doing the same We have done it I want to join I want to join People are coming here before me I will not go to anyone kwa mula punapatra, atavenya ame, ame release a new song ame introduce agitama, diyo nimi anza moa kata imaji punanza moa ka feba? diyo nimi anza sa the plan is the plan is, every few weeks takuana make sure nimi release something I want you guys to hold me accountable please kwa kuna kama he song nimi metua sa imagine we have, he song to live anja when he kuna like 10 months nikotuna he so kuna zengine piya tumefanya tumeshutu mehaka we were waiting for 2024 so an interval of a few weeks to natua sometimes twice a day kwa ni iko nimi? ati twice a day ati kwa kuna naubaya kwa kuna naubaya twice a day kwa zi songs kuna at least kolabo kolabo zi kon? very interesting kolabo iwa sitawa ame ya but from church girl yes of course very interesting kolabo tupehinti ame moja nikiwa piya hintu mta aribu umeka ame kudu kama utu jibizi masuari here is the thing I'm doing music with legends I'm doing music with new artists I'm doing music with living legends kuna wale waka kuna wale wali imba waka tulia waka retire kitkayo mimi ni nani naima paka kwa bedrum kuja we want you waja tukuski zetena so there's a lot more music coming out but naomba waso ni support nimi kwa quiet for a long time I know siteki kuenda siteki kunya maza mkini piya psych pia unagi inafanya unataka kuni ni so when we talk to Jana na we are here we are chilling with the we are here the very minapenda isho sana asan kizana I'm in love with this show you know that I'm looking at Bensia Kiyongen waja bwere Bensia let me say wami unaski angayini ni inanzanga J.C. Circle Montage Bensia Pali Tuika kaka drop kaka imba about J.C. Circle na penda isho ii na this is real spin di bilona ni angali it's personal for you it's personal for you I know it is kus I remember even the first episode nimi na penda ni ii nge kwa lasimani kama nge katara mii nge kama nge kwa nge kwa nge kwa nge kwa nge kwa nge kwa nge kwa nge kwa nge kwa I appreciate Kate She loves you so much I'm sure, Friends of pavement watching us but we love you as well I love you too Mwen emissions Bensia YouTube Niwang So, I thought to myself, what about this song? So, Muanga? Tenum meru dia tenu na hukwa mwanga. Muanga ni ga? Hisongi li kwenito Muanga Na kumuka tumika na wawu for a long time. So, mi nili jua isongi li to Muanga But, I did not even know that to kareyalize kora sinase na meremeta. So, we decided to change the last second to li eka meremeta. So, I'm still getting used to meremeta. We are o Warrior, but currently I still live in Karley InigSee We had never been the organization that expanded the company I'm not sure of the merch I have Nix Snuts is coming next and will ask her where you live Please dear Lord Meremeta nisongi kukumbusha wa say that the shine of Christ maze Wase wupit ya so many things na sa' O God na na watch Na sa zingene mungu wukupia time niya kugo, through is this dragon Yahile taimu tapata blessing ya ko,a uta ipu wuza uta ii hold dia So kitu wa kukumbusha tua say, there's the shine of Christ. Ile time to ita blow, it's going to be an amazing thing for you. So just hold on, hold on. Usi stressika na ya na wile umesota sa aii, ujali pa rent, umefungi o keja, una ka food, una kachapu kakukula. There's something God is planning for you and the shine is still there. So una merimetatu ya ni. Ile time ita blow apivi, blessings on blessings. Thank you so much for that. And how can people watch this video now? Please, it is everywhere. We make sure that the song is available everywhere. Kuna wa tuwa iPhone kama Mr. Nix Mazewa Kingiapu. Wow. I-tunes wa na ifat. Nixu kuna I-tunes. Usi onesha da tuwa. Ile phone. Ile bilimu kuchukichi I-phone on kuna. Aii, this is Aii. Ro machu tatu. Isi kwa api, this is Aii. Na maiku na nga nga ni kaba ni maiku na nga. Una na una na una. Matunda. Mama nga tia tuwa tutu ikutu. Una matunda. Historian, you sita ingili anyway. I-tunes. You can get it on iTunes, Apple Music everywhere. I mean, boom play, YouTube, Kilamaaani, available. Atamudundo.com, udundo.com, I get this music on. Yes, it's everywhere and I'm requesting guys support, support. Sayi manza mziki like a new artist. Ile benzi wa kitama. Ayuhuko. Uni mwingine. Uni mwingine manze. And you'll know it from the songs, the type of songs nita kwa na fanya. So please support, support. Naguwa siswa sa ni, the best we can do ni kwingia studio. Record a song and then release it.  Where's the amaiwezi, kaiwezi muna achananana. Nga ni ambi haro di studio. Gladi. Ume mention too, iso chana zingine. Na social media. Social media at Benzi again on Instagram, at Benzi again on Facebook, at Benzi again on TikTok, at Benzi again on YouTube. Kiyoko na hafanem, hafanem, hafanem followers, TikTok. Bro, me ni kona millions in faith. Wendy ni apple. Millions. Nini me kufolo. Nga naka challenge. Nama ni fanya challenge. TikTok is very important. Uki fanya challenge ineza, ineza asa idea sana. Numita fanya challenge amam na nichiwa. Uwe you to that. Stay tuned to my social media platforms. Nga ni kama wajapost, nita chkwa picha enu ilimzuri. Nita wana nika apuniseme lias. We are in trouble Nix, we are in trouble. Lazi mani wana ni. Shout out to everybody mania kwao tuned. Shout out to you guys. You guys are fantastic. I think you missed me so many times baka imeza. Imeza kuwa too much, always supporting my music of course. Thank you everyone who has been supporting my music Kwanzea ni Anze 2014. Almost normaliza 10 10 budi ya. 10 budi ya. So thank you so much. Asante kuwa everybody mania wana waka on my songs. What weingi sana. Critics. I'm looking for critics. Sinanga how? So thank you so much man. See you next time when you bring more music. Yes. For the first time appa on J.C. Wanda Lekwi support. Imi merimetha. Mwanga. Mwanga.


The remaining 29 students abducted from the Federal College of Forestry Mechanization of Faka in Cardona State have regained freedom. Chairman of the Parents' Committee, Abdullahi Usman, confirmed the release of the students by bandits. He explained that victims were released some minutes past 4 p.m. around the Kendantra area in Gewa local government area of the state and were on their way back to the state capital. Usman, however, refused to disclose the amount paid as ransom to the bandits for the release of the students. Authorities in the state and the Cardona State Police Command have yet to confirm the latest development. Hello, hope you enjoyed the news, please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.