Hello everyone. Welcome to another international capsule for the Shankar IAS Academy. Today our topic is the visit to India by Anthony Blinken, the Secretary of State of the United States. He was here just for over a day and on the whole the visit was disappointing in the sense that many of us had expected that he had come to the region at a time when the situation in Afghanistan was critical. Because we all know that the United States forces had withdrawn and they are committed to completely withdrawing by the end of August and that they had left it to the parties in Afghanistan to find a solution for all the problems that are plaguing that country. But after our foreign ministers visit to some other countries including Russia, Iran, Tashkent, Uzbekistan and some of these regional countries, it appeared as though the announcement of the Secretary of State's visit came as though he had a formula to offer. So most of us who expected Blinken to announce a new formula to bring peace to Afghanistan without it being overtaken by the Taliban were disappointed by his visit. It was obvious that the United States did not want to go back to Afghanistan in any particular way. But more than that, it did not appear that they had a contingency plan to help out the Ghani government in Afghanistan to find the Taliban and continue in power for some more time. We know that American forces not on the ground but have been giving air support to the Afghan army. But beyond that, they were not doing anything to prevent the movement of the Taliban towards Kabul. So instead of putting forward a particular plan, what Mr. Blinken was simply to repeat the biased hopes and also expectations as to how things should turn out in Afghanistan. He agreed with us that Afghanistan should have a democratic, inclusive, non-aligned independent government. And India and the United States were agreed on this. But what would be the steps that the United States and India could do to help the Afghan government? There was no proposal. Ironically, when Mr. Blinken was in India, we were also seeing a Taliban, a high-powered Taliban delegation being received in China. And declarations were being made by the Taliban that they would welcome China to participate in the development of Afghanistan. And that Taliban would make sure that they are safe and that properties will be safe, etc. Preparing the ground for what I think, massive induction of the BRI into Afghanistan and then through Pakistan. So, but Mr. Blinken did not seem to question that or even suggest that this was not going to be very healthy for Afghanistan. And moreover, after a couple of days, he said that Chinese involvement in Afghanistan may not be altogether undesirable. Perhaps there may be something good in China's approach to Afghanistan. This was even more disappointing. Because while talking to China, he had a different approach talking to China with India. He talked about the principles that the United States and India share, the kind of democratic background, human rights, so many principles that the United States and India share. And made it clear that the Quad, about which he was very positive, would provide the framework for the United States and India to cooperate in bringing stability in the India-Pacific future. So, when he was independently talking about China, his position was very clear that he was looking at India as a democracy to support the American efforts to contain China. But unlike Mr. Pompeo, his predecessor, who was Mr. Trump's secretary of state, he did not make any commitment to standing by India in the context of the Chinese Moves in Ladakh. There is no particular concern was expressed that disengagement was not taking place. It is true that some discussions had started when Mr. Blinken was here. And there are some indications that there might be some disengagement in the next few days. But the kind of support that this predecessor had declared for India in the context of China, I did not see. It is possible that they might have been ascertained in the official private conversations. But on China, he was very specific and very clear that India and China should work together. So, if you look at Mr. Blinken's visit in a traditional bilateral visit, there are quite enough positive points about his approach to the United States. But I may be wrong. The impression I gained from your overall visit and the outcome of it, I felt that the Biden administration was still very tentative about China and India. It seemed that they were still in the process of analyzing the situation and coming to a conclusion as well as China is concerned. So, they do envisage some amount of cooperation with China. But at the same time, they were quite clear that they would not allow China to dominate the world. And that is a matter of some comfort to us. And to make us even more comfortable, Mr. Blinken talked about squad not being a military alias. This is our requirement because we do not want to call it a military alias. So, at one stage, he said that the squad was not a military alias. But at the same time, he made it very clear that India, US partnership, the cooperation in dealing with China is a major factor in that. But to say that China's activities, if any, in Afghanistan may not be undesirable, leaves a bit of suspicion as to why he has taken that position. India, US relations, he characterized this as work in progress. And this he was saying in the context of some of the concerns that he expressed before he came to India about the human rights situation in India. So, he had said quite formally before he left that one of the things that he was going to raise in India was the reported deterioration of democracy in India and the growth of authoritarianism, etc. So, when he said that even our two democracies are work in progress, what he was saying that maybe the Indian democracy is not mature enough and seem to be the suggestion. So, on the so-called vital India-US relationship and the Indo-Pacific strategy, the Quad and COVID-19, these were the issues which were discussed, everything seemed to be tentative. As far as COVID is concerned, he declared that some $25 million will be available for vaccination. He reiterated that the United States was most grateful to India for the support it gave to the United States at the initial stages of the pandemic and that the future also the cooperation will continue. He did not say so, but it seemed as though the intellectual property rights related to some of the vaccines and the components of vaccines which have to be sent to India, United States will have an open mind. So, as a bilateral visit, yes, the ingredients were there. In fact, the Chinese criticized Blinken's visit as proclaiming that United States and India were such close friends and their shared ideals, etc. So, they criticized both saying that democracy in the western sense is not the answer for cooperation and they criticized the whole concept that democratic principles are which guide India and the United States and their relationship is based on these principles. So, they said one man, one vote democracy is not necessarily the most comfortable and most suitable form of government. So, his assertion about the relationship between India and the United States being very principled was called into question by the Chinese. But two very interesting things that Secretary Clinton did in India surprised everybody. One is he called a meeting or he met the representatives of the Dalai Lama as the Tibet government in exile. This is a very, very unprecedented meeting. Of course, United States had its own plans for Tibet and President Trump himself had enacted a Tibet assistance plan to provide some funding for the Tibetan refugees or scholarships. And in addition to that, they said they would want to open a consulate in Lhasa and if the Chinese did not allow United States to open a consulate in Lhasa, no Chinese consulate will be allowed in any of the states of the United States. They had also said that no outside forces should intervene in the matter of the succession of the Dalai Lama and if anybody intervened, action will be taken by them by the United States. So, we know that the American position had become tough on Tibet and particularly in the context of Xi Jinping's visit to Tibet just a few days earlier than that, which we think had implications for India meant for the Chinese position on the border This toughening of the position was significant. But the fact that he met the Tibetan leaders on Indian soil was something new because in the past, we have not allowed even the official representatives of the US government to have meetings with Tibetans in India. In fact, we have an understanding with the Dalai Lama that he will not engage in any political activities here. And if he wanted to do that, he had to go to Europe, he had to go to Austria, he had to go to the United States, he had somewhere with countries where he used to go and make speeches and so on, but not in India. And what was the purpose of that? The purpose was that we do not provoke China unnecessarily. The Dalai Lama's presence here itself is a provocation and therefore we didn't want to press it to a point. And after Mr Modi's visit to Wuhan, we had even softened our position in the sense that we did not participate in the Dalai Lama's but their celebrations officially and did not participate in any protest activities inside India. So towards China on Tibet, we are becoming a bit soft. But his meeting here was certainly a provocation, although I have not seen any reaction by the Chinese to this nor from our side. And so this was a point to be noted. But he did do something more, which is a meeting he held for some activists, human rights activists, journalists and others who are critical of the Modi government as though it was a kind of fact-finding mission as to where democracy is in order in India. This American presidents do all the time because they have a mandate from the US Congress to look at democracies and minority situations even with the most friendly countries. So that is why before Secretary of State President Obama, when he came here, it was a fantastic visit. But after the bilateral visit, he went to a halt and spoke about value of democracy and how India should not divert from its democratic principles. And we ignored it. And similarly, this time also, it seems that India has not questioned this meeting. So because he said, for example, one of the elements that Americans admire most about India is the steadfast commitment to its people to democracy, pluralism, human rights and fundamental freedoms. And he went on to examining the way India functions in a quest for self-correcting mechanisms, to prepare the challenges to democracy. So all these statements appear to confirm the impression that the Biden administration was still very tentative about its judgment and it is also quite clear that in settling the Afghanistan situation, they would rather depend on Pakistan rather than India. So on bilateral relationship, which is being called vital, earlier President Obama used to say it is the most defining relationship in the 21st century. It appeared that there is a slight decline or shall be saying certain caution about India and its democracy. As far as the Quad is concerned, he announced President Biden's intention to convene a face-to-face meeting of the Quad at the summit level later this year. And there was also some discussion about Prime Minister visiting Washington later this year. So it could perhaps be an occasion where Prime Minister Modi will be in Washington to attend the Quad summit. And that shows the further strengthening of the Quad partnership. But as I said, he repeated that Quad was not a military alliance or India's sick. Our external affairs minister Jai Shankar responded to all this in his usual philosophical manner without showing any kind of emotions on this, whether it is the disappointment or happiness. But he added in good measure that China should not see any action by other countries as being aimed at China. So this was his sole motto statement he said. While Mr. Blinken was talking about the Quad, he slipped in this sentence that when other countries do things, China should not think that this is aimed at them. In other sense, in other words, India's position that Quad is not an anti-China grouping, not an Asian network, etc. But again, there was no indication, however, that the line of actual control situation was serious. Because when Mr. Blinken was in India, there was concern that disengagement in remaining sectors in Iraq has not taken place. And in a sense, Mr. Blinken's visit to the Tibetan leaders may have aggravated the Chinese attitude towards the line of actual control. Because our intention not to play the Tibetan card or the Dalai Lama card was intended to solve our mutual problems, particularly the border peacefully. But this may have been a provocation for Tibet, together with all that the Trump administration had done, providing financial provision for support to the Tibetan community, a keep of camps in India. So meeting with the Tibetans was probably in connection with this proactive position with regard to Tibetan exile in India. And India did not seem to question that initiative. Of course, COVID-19 occupied a good part of the discussion and there was a specific comment of US dollars, 25 million to provide for vaccination in India. As I said, it was explained that this was basically reciprocity towards India's own assistance in the early part of the pandemic. But here again, no multilateral effort was indicated. Because the failure of the United Nations to deal with the pandemic in the United manner because of the Chinese position remained so. Even today, there are no efforts to have a multilateral effort to take this forward to the pandemic cooperation. But it was clear that US would not be lacking in the case of extending support to India. We should expect that the US would be liberal about intellectual property rights and vaccines. The competition with China is also an element in the COVID issue. The US is still pressing for investigating the origins of the so-called Wuhan virus. So to summarize all that had happened that we know about, of course, he met the Prime Minister or may have been very important discussions. But we didn't find any, any readout on that. And my own feeling is that the general approach is very positive, particularly in the context of China. But the contrary to the position of President Trump, there was no clear commitment on the part of President Biden to stand by China in the event of a deterioration of the border situation. This may have to do with the fact that the US has not yet reached a definitive conclusion about the deteriorations with China itself and India's role in it. So it's quite possible that these matters will be further discussed in Washington when the Quad meets and hopefully at that time the Prime Minister would be personally there. And the developments after that are not at all encouraging. The situation in Afghanistan has further deteriorated. Taliban is moving forcefully to occupy the towns. Earlier they were only in the suburbs and the villages etc. And now they are closing in on Afghanistan. There are reports that Taliban suffered some casualties inflicted by the Afghan forces. But the trend definitely, according to all the news sources, is that there is no letdown in Taliban movement towards Kabul. We do not know what discussions have taken place in Doha after the Americans had left. And the American assurance in that context is only that if they don't follow, if they don't keep the promises they have given in Doha, United States would not recognize the whatever regime comes up in Kabul. And as I said last time, that is not a big issue for Taliban because last time also they had no recognition from most of the states of the world. Their focus is on developing a fundamentalist government, support Pakistan, get support from China, and in the process create more problems for us in Jammu and Kashmir. So that movement has not been stopped by Mr. Blinken's visit or anything else they are managing for time. So as a bilateral visit, it was a good beginning. This was the first time that Mr. Blinken was in India. And all the right noises were made. And there was clear transparency in its dependence on India or cooperation within India in meeting the Chinese threat. But since they have not yet decided on their real strategy with China, and they still have some interest in Pakistan, China, etc., sort of operating as a stabilizing force in the whole of this region, we have some concerns. But I'm sure during the prime minister's visit, by then of course the situation in Afghanistan would have stabilized one way or the other. And that would be the time we would have a chance to compare notes to the United States and see where we go. We have no news from Iran or from Russia as to what they would be doing. But nothing helpful has been said. Some observers still believe that Iran may play a role in consolidating the Shia elements in the northern part of Afghanistan. And that may create some problems for the Taliban. But we have not heard anything to indicate that Iran had an intervention, intention to intervene. So this in some is what has happened. And we simply have to watch and wait for what's happening as far as India is concerned. We have no role. We have no intention to put our boots into Afghanistan. But we'll probably continue to support the Ghani government in whichever way we can without direct intervention. Thank you very much. I suppose we think in terms of all kinds of contingencies, because on Biden's son, we know he declared he changed policy towards Ukraine. So that is the idea is already burnt by getting involved in his business. But China's policy is much bigger than any involvement of Mr. Biden's son in China. Because China's US cooperation is very extensive. Even if Biden had no son, I think he would have been softer towards China. Because like we discovered, it is very difficult to decouple China from our economic and political relationships. And then they also see, Trump was a little bit adventurous in these matters and he took very forward positions. But Clinton did not do that. Obama did not do that. So Democrats generally have a soft corner to a stand. But in the actions of the Chinese during the pandemic, must have made the Americans realize that these soft kind of approachmen would help. But that's why I said they are still tentative on their China policy. And tentative on India policy. They are still making soundings. And that is how we have to see this visit. As far as the government of India's calculations are concerned, I'm sure they have all the information that they are analyzing and dealing with it. At the moment, they seem to be focusing on disengagement on the border. Yes, the options are open. But look at our situation today with the Chinese pressurizing us from one side and not having what shall we say, as opposed to we have our own plans to meet the Chinese threat. But we need powerful friends in this particular situation. And the only powerful friend who is willing to be with you is only the United States. Russia is not doing that. Europeans are not doing that. So our options are also limited. And we have to operate within that. So if we are able to disengage the Chinese troops someday, we are able to reinstate our patrolling, because that's an important aspect of this engagement. Because in actual situation, we say nothing has changed because the border has not been changed. But we have been stopped from areas where we used to patrol earlier. Because we have stopped the Chinese from patrolling the areas where they used to do that. It's true. In terms of non-patrolling, we seem to have lost land. And that is the issue which is to be, even after these people withdraw, you might have noticed reports as to the day before that India is pressing for the right to patrol the same areas that we used to do as part of the disengagement. Thank you. So these are all options. We are playing a chess game. You have to see what assets you have and what the enemy has. And that skill depends on how you move your pawns. That's how it is. Well, I don't know really the interconnection between these tribes etc. But we know that Pakistan is not entirely united with everything that the Taliban does. There is a difference between the Pakistani extremists and the Taliban extremists. And so each will also try to see that the other one dominates, does not dominate them. So there must be a kind of underlying currents of difference. And that may have something to do with the composition of the tribes also. So that's how I understand it. And therefore, I cannot say clearly as to how this chemistry will develop. But it's quite obvious that there will be differences. And those differences may surface when the Taliban is ready to form a government, whether they will be willing to share power with others. So it will depend on that. These are all several imponderables. But somehow I feel right from the beginning and even now that the Taliban government is likely to come about at least for a short period at the time which when India and Pakistan and the Taliban would work out their relationship and see what they will do for each other, whether they will work together. When Chinese, if they are involved, they will try to keep them together so that the opposition to India does not get that into it. And that's all I can see in the nearest future. When I say Mr. Blinken, so we said, did not give me any recent to feel comfortable about this. Thank you very much. I'll see you next week. Bye-bye.


Hello everybody, welcome to my channel, it's Iwa from Bohemian Crafting and I'm coming today with day 2 of 100 days of projects for Junk Journals It's inspired by Margaret from 7 Plaza and I really enjoyed the first day, so I hope I'm gonna continue with enjoying day number 2 In second day, Margaret created a paperclip dangle she did used large paperclip, that's what I do have here and then she used all kind of beautiful yarns I do not have yarns, so I decided to use this butcher twine or string as one of the colors which I will be using on my dangle and the second things which I would like to use is this one I had this on a beautiful spool a long time ago but then my friend came with children and they put their hands on it and it did end up like this so I'm gonna kind of like, you know, pull a few strings I do believe I can pull a few strings Margaret cut her yarn in 24 inches, I think like but then she folded it and cut it and I think she end up with 12 inches the leg of the strings I'm guessing so that's what I'm gonna be cutting 12 inches of the string and I'm not sure how many I will pull out, but 12 inches of the chain and of course I'm gonna be using again book pages to create beads and some kind of decorations on my dangle so I have managed to pull out three chains in the leg of 12 inches and I do have here five strings again in the leg of 12 inches from that butcher twine to string I'm gonna be using same nearly similar technique that Margaret used she grabbed all her yarns put them into one bunch and then slide them through and wrap them around her paper clip I'm gonna take always one string and one chain and start to put them in the kind of like row one next to another one string this way and on chain I will need to use jump rings and I just realized that I wanted, before I will continue to create that dangle I wanted to color the chains and paper clip as well for that I'm gonna take alcohol inks gonna put my chains here kind of like inking sheet I do have here my alcohol inking tool for dirty inking I do have here brownie alcohol ink and also this one I didn't try that yet it's alcohol pearls which I found on Amazon and I didn't try it yet and I want to try them this one so I think I'm gonna start with brown because that's kind of obvious light color first on my dirty tool but this one I'm using only when I'm making some kind of like very grungy inking so I don't mind if there will be you know few different colors I think I will need a little bit more on my tool and then to add this brownie ink onto my chain yeah I will need some darker color and I'm gonna add same color onto my paper clip ah it does look really good it's kind of catching same color but I really would like to try those alcohol pearls which I don't know what is it when it's black we will see I think it's only black color I'm guessing it's nothing happening bear with me there you go yeah it's kind of like grungy mix it's more like grey than black it's interesting color I have to say okay I'm gonna let this dry so I do have both of these kind of in the quite similar color I do have here my jump rings which I'm gonna be using for editing the chain to that paper clip and I'm gonna start so fold the string on half slide through this one and through to that loop slide the ends like on tax now I'm gonna take a chain catch it in the half I'm gonna pull out one jump ring just one please just one and with the jump ring I'm gonna fix my chain to that paper clip and I'm gonna take another string and again fold it on half and go next to that chain and fix the string and again I'm gonna go with the chain and so on so on till I will be with the last string and I do have my base so all these chains are kind of like between the strings I added kind of two and two strings on the sides then I do have chain chain here it got somehow mixed then are strings and there is one chain so that's what I do have here and now I'm gonna bring back my newspapers and I need to decorate this one somehow so first of course I'm gonna make a bead to create a bead from paper it's not too hard you just need some strip of paper yup, I'm gonna use this one it's better on beads than on this paper I'm gonna cut the strips not too big strips I'm gonna fold it on half fold it on half and cut the strips like this or I do have fold it on half so now I can actually cut it this way and here I'm gonna cut it on two pieces I do have here this bamboo stick or you can use maybe end of brush you can use a knitting needle you can use I'm not sure what maybe a large needle just try to use something what has beautiful circle-ish rounded shape and grab your piece of paper and create your own beads so you will start to roll the paper around the thing you choose to have as a base for creating the bead grab the glue add the glue on that paper and roll your bead slide it down and let it dry and grab another paper and do exactly the same I do have video where I created beads from all kinds of decorations for junk journals from book pages so I'm gonna link it down below you will see so many different beads how to make them so I'm gonna link that video down below for those who would like to see I'm gonna create few of my beads so here I am in the process of making my beads you saw me do something like this and I'm making also these little cuties and I just wanna show you how I make them if you wanna kinda copy the style so I ripped long strip of the paper so I cut a long strip of the paper and wrap it around this bamboo stick as a large kinda like tube and I'm leaving it on my stick this way go there so always moving that wooden stick and I grab my scissors and cut then I need because my scissors will squeeze it this way so then I need to kinda push it back on the stick like this to make it circulish again like a ring and once it's circulish like a ring it does have that hole in the middle I'm using a super glue and I'm brushing super glue over my bead the super glue gives to that bead the strong feeling so hold it still and soft and here on the ground so I'm adding this super glue on the sides and then once it's dry it does have this cute little bead which I can use on my strings later so I do have few of my beads done and I'm gonna flip through of the newspapers and try to find some images like here these large letters they can be perfect as a dangle as well I'm gonna be using this envelope with thicker cardstock to create background for it so first I'm gonna fast cut out that hole and glue it on piece of cardstock kinda layer so I'm gonna let this dry and then I will fast cut out the hole outside and inside as well I'm gonna have a look on some other images here as well and do kinda similar things and in the newspapers you can find so many things which can be used like decoration in your journals it can be just small small image on the pages like here this one with a 3000 holiday that can be perfect this can be perfect as a decoration this 20% off can be perfect as a decoration S here S letter S so I'm gonna fast cut out that letter S and go to have a look even more in my newspapers I do have cut out the letter O and a little bit took off piece of that newspaper but it looks good on the edges I'm using this nail file to sand the edges and make them nice and smooth it will help to make it look better inside it doesn't go exactly as I want but it goes a little bit so I'm smoothing edges on all of this and I found that some of these circles matching perfectly my circle punches so for the circles I'm using my circle punches and it does go much much better way actually you know if you do have the right tool for what you want to do so like here I found news so that I can fit into this I think it's 3 quarter of inch circle punch also I glue it on the piece of cardstock old envelope here I do have that letter S already glued on one piece of the envelope and I glue it again so I'm gonna fast cut out again just around the edges I did before like this now I'm gonna take this file and I'm gonna smooth the edges and sand the paper around so it will look more clean and it will make it will make my cuts look better and now I do have these on one piece of envelope and they are not too thick so I'm gonna glue them on a few more pieces that's why I'm glad that these circles are matching my circle punches because then it's much more easy to create embellishments using them so I'm gonna glue about 3, 4 these circles together and make my pieces look much much better thicker and heavier I think for now I'm satisfied with these pieces I'm gonna be using Distress Ink this is Distress Ink from Spectrum Noir Harmony line and this is Water Reactive dye ink and I'm gonna be using seal brown color if you don't have any Distress Inks you can always use some marker and very carefully, this can be very tricky very carefully to add the color to the edges of your pieces it will make them look finished so with marker carefully take your time just paint the edges the sides of your pieces it will give them really beautiful look but because I do have my Distress Inks I'm gonna be using that there is many amazing tools for Distressing, I'm using this one because I started to use it 7 years ago and I stuck with it, so not gonna change it but there is so many beautiful tools for Distressing the sponges, I used that sponge for editing alcohol ink so same sponge tool can be used for Distressing for using these Inks and editing color to the edges so with this I'm gonna make look make this letter look a little bit older and now I can start to make holes I can use some poking tool needle or this O which I'm using for making holes in my signatures or you can use any kind of hole punch I do have here this crocodile and I'm gonna be punching holes in the middle of the top of these pieces and then I will be able to add their jump ring and hang it on the chains here I'm gonna also used on few pieces like this, just small hole where you will decide and there are also supplies which can help you to create top coats beautiful effects on these pieces from newspaper one of these is this liquid glass something similar it's from Ranger it's called glossy accent so with this I'm gonna add a little bit of this liquid glass on my little circles hold it here with the needle and spread the liquid on my little circle and let this dry I'm gonna do same probably on all of these 3 circles and I'm gonna let this dry and then I will show you how it looks like and here is how your pieces from newspapers can look like with that glossy accent or liquid glass on the top so it can be really beautiful embellishment it's possible that 20% off but this one went really well and here do not touch it too easily here I touch that very easily and I get glued into that when there was just half dry that glossy accent so let it dry totally and it will make beautiful coat and I think I'm ready now so I'm gonna take my embellishments here on these some prints I can choose one of these chains and up there my little embellishment somewhere so I do have helped the paper beads or paper embellishments and now I'm gonna wrap the paper beads so I somehow messed up with this I do have one large and two small so I'm gonna take one of these side strings and go first with that small I'm gonna make a knot gonna slide there my bead and make a knot right under that bead like this I'm gonna take the second bead and do another knot and I'm gonna take that third one so I have tried to fit these beads here and I have to use hot glue gun to put a little bit of hot glue gun hot glue to hold the beads on the right spot because I made them on the bamboo stick so I'm gonna take the thread from opposite side and first I'm gonna make a knot somewhere here and I'm gonna take one of these smaller beads slide it there I'm gonna put right before the knot I'm gonna put small amount of that glue and slide there my bead and kinda like press the thread on the side of that bead it will be slightly off of the on the side but I don't mind that I'm gonna make another knot and move that knot close to that third bead here now I can take another bead and I think here I'm gonna put a little bit of glue inside the bead and just slide it over this way and I'm gonna make another knot take that last one slide it there, put that small amount of glue just a drop put that bead and make last knot this one, done so on this string I decided to add the bead to the end so first I'm gonna slide the bead on my thread then first I'm gonna do that bottom knot which will be under my bead which will be under my bead on the end of that thread right here now I'm gonna add a little bit of glue right here slide there the bead and now I can do the knot right next to that bead from opposite side much more easily because I do have that longer string just like that and I'm gonna continue and then I will show you how my dangle looks like I do have here a few more and this is my little dangle from newspaper and string so that's all what I'm gonna show you today I actually like it, it was quite some job but I love these, these looks amazing with that glossy accent so they are perfect, I do love this one it's great as well, I little bit screw up with this because I slide the top paper I color it the backside and I love these beads, these smaller ones looks awesome and because of the because of the super glue they do have nice sound these, because the top it's much harder than it will be if I will don't use that super glue to wipe it on the top so that's my sharing for today I hope I gave you some idea what to do if you will be missing some beads and you will need to create some embellishments, the newspapers can be really great background to find some interesting images thank you so much for jumping in today thank you so much for spending your time with me I hope you will join me next day on next challenge inspired by 7plaza 100 days project have a beautiful day, take care of yourself and I will see you soon, bye


Well, good morning, we're going to change scales now considerably. Ignacio was talking about the smallest details. I'm going to start actually the first half of my talk, talking about almost the whole brain and behavior. In the second half, I'm going to talk about neurons. Now, I was given the title of data analysis and neural coding, and I don't think in 45 minutes I can cover data analysis, and in another 45 minutes I can cover all of neural coding. So I've chosen to basically do what I'm going to call two case studies. The neural coding stuff is much more mature, but I'm going to show you some data from our lab on behavior because I can use it to talk about principles of data analysis that I think are important for young people to get early on. There are places where I know that difficulties arise, and hopefully you'll find the science that goes with the data analysis as interesting as my colleagues and I do. So the question we are answering, we would like to know, is how does the brain process information? Information about the world enters through the senses, is compared with stored information, whether genetically determined or learned, and organized behavior, we hope, emerges. And the goal of the whole laboratory where I work is to learn something about how macroscopic behavior emerges. And I've worked on topics that range from visual perception to reward seeking, and I'm going to talk in the first half, actually in both halves, it's mostly going to be about visual perception today. Some of it will be very high level, some of it will be not so high. To do this, we need an approach to study information processing in the brain, and the principle basically is you design an experiment, I should put that first, we collect data and we do some type of data analysis. Now, the problem is these days data analysis is approximately thought to be equal to statistical analysis, and I'm going to deal mostly with the issues of statistical analysis. And before we go any further, we need to make these two things clear. Richard Hamming was an engineer who did quite a bit of work on what we would call today exploratory data analysis. But he had two very nice quotes, the first of which is, the purpose of computing is insight, not numbers. And I think that it is all too easy to get caught up in the numbers and forget that the purpose is to find out how the brain works, not how you can compute a number from a bunch of data. The other is, most of the time each person is immersed in the details of one special part of the whole and does not think of how what they are doing relates to the larger picture. It's just another way of saying, you know, you're not doing the data analysis because you want the numbers, you're doing the data analysis because you want to understand something about how the brain is actually doing this fabulous job of looking around and in a semi-untrained way looking around the room. And if I asked you all, asked each of you, what is significant in this room with no other instruction than that, you would start to tell me about things in the room that seem significant. Now, we may not agree about their total significance, but this really badly underspecified problem is something we would solve effortlessly.  And yet, we actually can't do it with a computer. And in the second half of my work where I'm going to talk about neural coding, I once got locked into a conference room at the CIA in the United States. It's the only time I've been locked in to give a talk. Usually, they want to lock you out after a short period of time, but they wanted to know whether we had discovered some way to automatically analyze photos because they had great computer algorithms to look at these thousands and thousands of satellite photos. And this was in the 80s, so you can imagine how many more photos they have now. But what they wanted to know was whether you could make an algorithm that knew whether something changed from day to day that was significant. So actually, at my era, we all think back to the Cuban missile crisis, which grew out of satellite photo interpretation and some photo interpreter seeing a photo of an area in Cuba and seeing there was new construction going on that hadn't been there a few days before. And this led to the discovery that the Cubans were building missile bases with the help of the old Soviet Union. And to do that kind of task is something the brain does with no trouble at all. We have no idea how. So one of the points I want to make, and I'm going to show you, is how important visualization is. And to my British friends, I'm sorry about the Z, but I do come from the other side of the ocean. The value of adequate visualization cannot be overemphasized. The best pattern recognizer is the one between your ears. And if you can present your data in a way that simplifies it and you can make something of it visually, you just must try. Because if you do an analysis on your data, it reveals something that you didn't think you noticed and you go back and look at your data and you cannot see where the result came from. Don't trust it and certainly don't publish it. Okay, now I'm going to talk about a couple of roles for statistics. There are two parts really in modern statistics. One is to test a specific hypothesis. For example, the data from treatments, that is two sets of experimental parameters. Are they equal? This is something we all do. You do two conditions and you ask, one is a control condition, one is an experimental condition. You just ask whether they are the same or different. To do this, and then the second form, and I'm going to talk about both of them briefly, but mostly the second one, is to form a statistical model for assessing the efficacy of parameter fits. And I hope the next slide, okay, no it's not, but we'll go on from here. So hypothesis testing. To test a specific hypothesis, for example the data from two treatments, are they equal or are they drawn from a known distribution? So many times you don't actually have two treatments, you just have some assumption about what the unaffected treatment is. You do something and you expect your data to look different than say a Gaussian with a mean of zero, you might expect it to have a Gaussian distribution with a mean of ten. We actually believe that the two sets are different. What we can ask is whether they are the same, the so-called null hypothesis. The key is always to construct the null, the correct null. I'm going to show an example in part two of the neural coding where from my point of view a poor null was constructed and the whole field of neural coding chased a problem for a good 10 or 15 years because a poor null hypothesis had been constructed, which is troublesome, it happens all the time. Don't think you're immune, it's easy to do. Hypothesis testing is parametric. We assume a distribution from which the data are drawn, most often Gaussian. These are things like a t-test, linear regression, ANOVA, and we just look at the p-values and ask whether we got a significant result. That is not all you can do and we'll get to that in a minute. You can also do non-parametric, which is a misnomer. It's just the parameter you're measuring is different. You're always testing against some distribution and in a lot of non-parametric statistics instead of testing the data against a normal distribution, you're testing, for example, the ranks against a normal distribution because there are a couple of theorems that show for most real-world data, excuse me, for most real-world data, the ranks are Gaussian distributed and so you can do some statistics on the ranks. These are generally less powerful but much less likely to violate the assumptions, so if you get a significant result with it, you usually have something that is reasonable to believe. But again, be sure you know where it came from before you go talking about it too much. We're going to consider an experiment so I can show you what we've done in the way of data analysis and where you can get. We're interested in how primates, including humans, perceive visual objects. In these experiments we ask whether the weight of the mixture in morphed images is perceived, how well and what happens after damage to relevant brain areas. So I'm going to talk about one of the oldest approaches to doing behavior, which is looking at normal animals and then damaging a brain area that you have reason to believe is involved in a function and asking whether you've damaged the function or not and further in what way if you have. Our subjects are going to be rhesus monkeys.  The reason we use rhesus monkeys is it's a convenient species. In some countries they're pests.    Let me just make that clear. They may look like primates but they are not smart animals. The brain is about 250 grams. Your brain is about 1500 grams. That difference is huge. What we're going to talk about is the visual system. In the monkey, I think I have another diagram of this. There's the eye. The optic nerve goes into the middle of the brain to the lateral geniculate nucleus. The nerve cells there project to the primary visual cortex which on the monkey is on the surface at the back of the brain and then through a bunch of experiments over the past 50 years we've learned that information passes at least in a feed-forward way down what is called the temporal lobe through a series of areas and today we're going to be concerned with this area at the front of the temporal lobe, area TE shown both here and in cross-section and a region here in light gray that I'm going to call rhino cortex today. Now, anyway, so let's skip this. What we know about this, Hube and Wiesel showed in the early 60s that neurons in the primary visual cortex respond very nicely to oriented bars of light and they showed that these cells are organized in columns where every orientation for a particular location in space is located and then the next little location in space is mapped into the next what they called column and they won a Nobel Prize for this discovery. This information is fed down the temporal lobe in the early 70s a researcher named Charlie Gross found that there were cells in this area TE that I just mentioned that are sensitive to faces and hands and so Hube and Wiesel had already speculated that vision was hierarchical that you started with very simple constructs in the visual system and ended up with very complicated constructs and this TE area was sort of the final nail in that hypothesis that you went from simple objects to complex objects as you go through this hierarchy and there's this other area that I'm going to call rhino cortex for now. Okay, for the experiment I'm going to show you today I'm going to show it to you in two ways. The first is and this is the way we actually did it we were interested in how easily monkeys could learn to tell cats from dogs there's a long history of trying to train monkeys to do this and for reasons that still aren't clear to me always took a long time to train the animals. Now I'm not going to go into it today but we found some tricks for getting the animals to do this where they will learn it as quickly as we think a human learns it and I haven't got time to deal with that aspect if we were talking about animal behavior I'd be glad to if someone wants to ask me later we had three monkeys in each group we asked the monkeys to respond in one way for one class of stimuli say dogs and respond in another way behavioral response when I say response here to cats we have the monkeys learn the principle of the task with just a single pair of stimuli this avoids having to account for the monkey learning how to respond that is it establishes what is called a learning set because for this experiment we weren't interested in learning how the animal learns to respond A, respond B we wanted to know could the animal tell A from B so we wanted to get beyond that so we'll bypass the learning of the response and consider the categorization only and when we did this with three monkeys this is visualization we asked what's the percentage of items out of all these sets and it was 420 cats and 420 dogs that they saw once they had learned the principle from seeing 20 dogs and cats and practicing with them and then they were tested on 420 dogs and 420 cats they had never seen before and they did well they got it mostly right and if you all think about it there are a few dogs that you're not sure probably from a photo from the side whether it's a dog or a cat these photos were in all views we didn't just have it face on or head only some of it was head, some of it was side views some of it was actually from the back but the monkeys did this really well we also damaged this brain area called TE and on our initial testing it looked like the monkeys actually were having no difficulty remember this is that area that's supposed to have these face selective neurons we thought these animals would be devastated in this task and they're damaged there's no question they are not or this is the question we need to ask because we're worrying about data analysis this is this different than that and is that different than that and the answer is this is not different than that and this is different than that now how do I know that? no on what basis do I claim that? can you just compare the average data? what if each animal has an idiosyncratic bias in response a so-called random effect it's something the animal does and it happens to be part of our experiment because we picked that particular animal but it is not part of what the statisticians would call the treatment the treatment in our case is dogs and cats it is not whether one animal is different than another and the other part of the treatment is this damage to the area TE so we want to know is there a systematic difference across all of the animals the latter is a common problem in statistics whether biology or not we take several repeated measurements from each animal and we're interested in what is called the treatment or in modern terms a fixed effect so if you're reading in a modern statistics book you're going to get this concept these two terms of fixed effects and random effects and it's not always exactly clear about which are random effects but we won't get into that today it's just something to be aware of excuse me so we need to account for the variability arising from the difference in the animals and in modern terms this is called or semi-modern it's called a repeated measures problem because you're taking several measurements from each animal and what you would like to do is remove the bias that each animal brings in and one thing you could think of doing and it is reasonably valid is to go ahead and take the average performance of each individual animal and subtract that value from every measurement for that value and now you're measuring the animal's performance against his overall mean performance that isn't quite right but it's close because the variability can change things a little bit and as a first pass people do a repeated measures correction and this is an example of a printout that I don't expect you to understand I want to just make clear what you will see is something called error that's attributed to the monkey and just know that the statistic this is an ANOVA, an analysis of variance which is just a version of linear regression and hopefully most of you or all of you have heard of this but what happens is that computation is done to segment the variance that's due to the individual animals from the variance that's due to the treatments that you've imposed and so the error for the monkey is the part you're not interested in in this experiment or we weren't and the error called within is exactly what we're interested in and as you see there's a... sorry, it's a... in this case it was just two classes so this should read category is a significant effect and the group is a significant effect so we know what we could see the animals really do know the difference between dogs and cats and if the statistic had come out otherwise I would not have believed it but it certainly confirms what the graph showed but the other part which is is that no a little really higher than the no for the other animal and the answer is yes there's a group effect in other words the TE animals versus the normals the normal situation and there's a morph level or a category by group interaction and the interaction had I drawn it would be if I connected these by a straight line and asked whether those two lines were parallel if they are parallel it is said there is no interaction if they are not parallel and that deviates significantly there is an interaction and we can see the interaction because in the dog category the animals perform about equally well but in the cat category they don't now we understand this and it has to do with the way we did the behavior and not with their ability to tell cats apart because we can reverse this and reverse the result but that's not completely the point today so that's one level we know we have a significant effect we'd like to do what is called statistical modeling not just simple hypothesis testing so statistics have moved forward to modeling data and ANOVA is actually a model but I only emphasize the significance testing what is the difference between theoretical modeling and statistical modeling it's forward versus backward a theoretician and I think Uppibalo later today is going to actually talk about some real theory is someone who sits and thinks about all of the phenomena and makes up a model and says this is my model now we're going to go and see not only whether we can sort of interpolate from old data but can we predict conditions from the model that we didn't really put in in making the model statistical modeling is saying I'm going backwards I have a set of data I want to fit it with some kind of a model and I want to know whether the parameters I have chosen to encapsulate the variability in the model are appropriate for the data I have collected and in general a statistical model is not expected to extrapolate beyond the limits of the data that were or the conditions that were considered in the experiment a theoretical model to be a good theoretical model ought to take you into a new domain is that clear to everyone? because it's a really important point now to match data against a theoretical model you actually use some of the tools in statistical modeling because you use the theoretical model as your null hypothesis you ask whether you can reject it you hope not it's a good enough model the second role for statistics is that theoretical modeling is trying to estimate parametric descriptions of the data with judgments about which parameters are better descriptions of the data contrast with theoretical modeling is a forward process in which one hopes to extrapolate beyond the bounds of the current observations or explain the method of implementation so you could actually say in a theoretical model this is about what the mechanisms are that made something happen okay, so we're going to go back to our category experiment and we're going to model a little data the experiment sorry, I had a cold last week and I'm still coughing a little bit category ambiguous stimuli we originally did the experiment just like I described with cats and dogs but in this case what we did was take cats and morph them with dogs now this is an ill-defined concept we never did find a rigorous algorithm to do it but you can do something that you believe looks like a relatively continuous change from this cat to this dog and the question we wanted to answer with this is how well would our monkeys do with these morphed stimuli both the normals and the animals with the TE damage and this is a really nice experiment now we didn't use just one pair of morphs we actually used 20 pairs of morphs I must say some of them I find not so easy the reason I mentioned that is I'm going to show you how our monkeys did and I probably would not have done as well even though they did it with no practice and what we did was we just took these monkeys they were familiar with the cat and dog categories but they had never seen this stuff in the middle and they had never seen the boundaries either they had never seen the cats and dogs so what we did was just one day start presenting all of these images but what we wanted to know was whether they at the 50% boundary condition had a relatively random response and when it was a little more cat-like did they call it cat when it was a little more dog-like did they call it dog and you can all sit here and think about whether you would do this for them, yes well, there's software you pick some features and connect them and then the software doesn't what he's mentioning is whether the morphing software that exists the semi-automatic stuff which is what we used all of the real stuff that we found was terrible actually compared to doing the semi-automatic when you pick a few features like the tip of the nose and the tips of the ears and things like that you get a much more ambiguous kind of morph in the middle than you do when you play with the morphing software it gets really confused unless the faces look pretty similar to start with in which case it wasn't much good to us it's really good for transforming one human face into another it turned out not to work well for transforming a dog into a cat even though I can't tell you how you know a dog is a dog and a cat is a cat we would love to know that and if anyone has a good idea and wants to come to my lab and try to prove it so what happens with our monkeys? this is the performance of the normal monkeys what we're seeing is the proportion of dog against the probability of answering that it's a dog and this is what's usually called a psychophysical discrimination curve and we're going to model it in a minute as a logistic these monkeys did really well three different animals it's the average of the first three days that they ever saw the morphs I think I didn't put in but if anyone wants to see it later I'll show you how they did on the first day yes they're adults young adults mostly probably in the range of four to seven years I can tell you better what their weights are which is between four and seven kilos if that means anything okay okay anyway but there are a couple of things to note they really do at the edges very well and in the middle they have more trouble they keep working non-trivial these were all mixed up this is again the first three days they ever saw these they did almost this well on the first day and the statistical model I'm going to show you I've actually modeled the day but I'm not going to show you that model and it made no difference actually it was surprising to me because it looked like it should have but it didn't the other thing is there's just a little bias this curve for one animal the curve really does go through the 50-50 point or close enough but the other two have this little bit of bias and I want to point it out just to show you that when you visualize your data you have to get things out of it we understand why this happens it's because there was an asymmetrical reward in this case the animal was more likely to be rewarded or only was rewarded for dog-ness and if the animal wasn't sure they were a little more likely to answer dog so they wouldn't miss a reward only in one way I'm not going to show the data the question is can we be sure it's a bias on the animal side yes because if we reverse it you get the same bias the other way we know isn't quite as good but if you move the Morph boundary over in 10 minutes the animal has moved his discrimination criteria and over also so I think they're not having any trouble with the images I think they're hoping not to miss a reward okay alright these are the TE animals you already get the sense they're not the same and we also did Rhino Cortex I haven't got too much time to go into the theory of that but the reason the Rhino Cortex data are interesting is that there's an argument in the field about whether Rhino Cortex plays a role in perception or not that's all I'm going to say about it and I'll show you in just one second that our data argue against it and this is just putting all the data on top of each other and you can immediately get the intuition that the TE animals are having some trouble that the normal animals are not and that the Rhino animals are not I mean the reason I chose to show you these data is they're actually very easy you don't we're going to want the statistics but in a sense we don't need the statistics because we already know the answer we think now one of the things you noticed is that we're dealing with something that is not a nice Gaussian distribution even if I differentiate this it will not be a Gaussian and the reason is it's a binary decision and this is something that is fit in modern statistics not using ANOVA you can do a transformation of the data if you want to use ANOVA but there are more modern tools now and what I want to do is just introduce the concept of these modern tools they are everywhere all of the functional imaging uses these tools in neurophysiology they're becoming important and this is the crudest level its behavior oh I didn't want that I wanted this and these models start with something called generalized linear models and then move on to linear mixed effects models generalized linear models all of these use an exponential family of data the data can be Gaussian they can be Poisson they can be binomial they can be pseudo Poisson they can be inverse Gaussian any family of functions that you think might describe your data that can be put into an exponential format can be the generalized linear model solution can at least be approximated for these families every statistical package these days will have the tools to do the modeling for you so you don't have to implement it it's a nightmare to implement the computation and instead of maximizing the mean squared error which is what we're all used to when we do ANOVA's linear regression we can maximize something called the likelihood function which for the Gaussian case is the same as the mean squared error so you're just you're doing the same thing you actually get a slightly different answer because you can solve if you minimize the mean squared error directly you can solve that even graphically but you can solve it algebraically exactly that's a hill climbing technique and you always end up with a slight difference the goal here is to get the most likely parameterization of the tested models so we're gonna one model against another with a likelihood ratio test which turns out if the model's constructed nicely to be an ANOVA between the two models so if we do that with these data now we have something that is a random effects and fixed effects now remember I told you the random effects here are the monkey because we want to we don't want to deal with that but we have to account for it and the fixed effects are the morph level and the group and what we see here is both by group interaction meaning that those lines wouldn't be parallel so what's happened here is that we see that the means are different that's what this intercept means it's a little I haven't got time to go into how well no I will tell you what happens what's happening is this is being modeled I took the formula out is being modeled using a binomial distribution and a binomial is not a Gaussian and we're doing the likelihood or the sorry the odds ratio so what we're modeling here is not the animals trial by trial performance we're modeling whether the likelihood that the animal is going to say yes versus no and it's like going to the racetrack so you're getting three to one odds or four to one odds and you're asking what the odds ratio looks like and that's how you interpret that logistic function sorry I should have covered that so what this is plotting is just the log the probability of yes over the probability of no so it's are the odds three to one at this level the animals going to answer that way or are they one to three and that's what you're getting out of this and the reason to do it is you get this very nice logistic function and this since you're taking the log means that you're in the exponential family and you can model it with the GLM machinery and you get these very nice answers and what you get is a correct assessment of the variance because remember this is in a binomial situation and the variance of a binomial is not the same as the variance in a Gaussian it's p times one minus p rather than Gaussian distribution so you need to account for that and that's what's being done here so the model against which this is being run is the binomial model and you get these very nice answers out of it I did that and now I can take my model which is here the other thing you need to think of the odds ratio is now being mapped into the odds into the logistic space so every data point is mapped through that logistic function and the advantage of that is now you end up with a straight line and the machinery you're using to solve this looks very much like linear regression and so when you get the answer back you actually get something back that's the intercept and it is in the space where the data are being modeled but it's not in the data space the one problem with these models is you can look at them in the transform space until you're blue in the face and you get no intuition at all about the underlying process what is critical is to map the data the model back into the data space and I've done that for you so this is the exponential this is the intercept plus this number times the morph level asking what the model predicts for the normal animals and these are the data and this is the red line is the model that came out of these data for the normal animals now in one fellow swoop I got the answer not only for the normal animals but I happened to have modeled the TE animals in the same model so I've done it all in one big operation and when we do that here is the TE data I'm sorry I accidentally made them both the same color but you ought to be able to see a continuous line that has no dots on it that's the model these are the data and we know from the likelihood test that these are significant coefficients so we can live with them okay so what we do is and I didn't put in what did I do here huh I took out a couple slides I meant to leave in okay I had the Rhino animals also and those two the Rhino model and the normal animal model are virtually identical so the conclusion that damaging TE interferes with the animals ability to distinguish these morphs in some mild but significant level and that the Rhino animals are undamaged which puts us on one side of this debate about whether Rhino cortex plays a role in visual perception of complex so this is an exercise in modeling data but coming to what I would like to the kind of conclusion I want which is which of these pieces of tissue actually has a functional role in this kind of perception and it led to this other very nice experiment because they had trouble with the morphs we asked what would happen if you just blocked off parts of the images and did it again and here is no mask and again you see the little bit of trouble that the TE animals the green are having and here are the Rhino's and the controls when you do those masked images the animals the TE animals just fall apart completely so they can do the categorization pretty well they can do the morphing pretty well but when parts of the image are occluded they have lots of trouble and we've actually done the occlusions in a couple of different ways and I'm not going to bother you with that today so our conclusion from this we've done all this fancy statistics but the conclusion is biological TE damage is followed by a little damage in classifying dogs and cats when noise is added to the images the deficit becomes quite severe so how does this map on to human to human patients the question is how does it map on to human patients with strokes we don't know yet I can tell you where this actually all started is that our hypothesis when we did this experiment was actually that when we damaged TE these animals were not going to be able to do the categories at all because the evidence while it was inferential from both human MR data what we do know is the corresponding areas in human MR light up when humans are doing face discriminations we've never seen it for categorization and I'm trying to get some of my colleagues to do that experiment maybe they will when these data finally are fully published these are only semi published data right now maybe I'm not supposed to be showing them but they're so nice okay this is what I can say is that at least the physiological correspondence between the monkeys and the humans is pretty good and that's what led to this and it leads me to this uneasy feeling that I have that I don't understand the connection very well between single neuron selectivity and responsiveness and actual brain function that's a big step there's a lot of assumptions people record single units and they get all kinds of selectivities and then they make very strong statements about whether that brain region or they do an MR study and they show you these bright spots and dark spots and then they make a they title the paper frequently this and this is is out by some piece of brain tissue and in fact they didn't do that experiment so they're not justified in that statement but they make it anyway because it sells really well okay so neural coding now the question I have I have in principle 35 minutes do we want a couple minute break or shall I just go on is everyone happy okay I get tired of hearing myself talk but I know I can shut that off okay so we're going to talk about neural coding this is going to change scales but again it's going to be mixing actually a little more something closer to modeling than we were talking about before but a lot of it was done using statistical approaches so what is the code that is used to carry information by single neurons we'll use the visual system again as a prototype we're going to look at one simple process identifying which stimulus appeared ultimately we want to know how the stimulus is interpreted that is what does it mean how are you going to choose which behavior to do and I even skip that in the previous piece and here we're going to make even less attention to it again we're looking at the visual system and mostly I'm going to be talking about neurons and primary visual cortex and I'm assuming everyone knows how a neuron generates action potentials is there anyone in here okay so this is we redid the Hubel and Weasel experiment so a monkey is looking at a spot except it's in an awake spot so it's redoing an experiment Bob Wertz did in 1969 where he showed that the Hubel and Weasel's data from anesthetized animals was about the same in awake animals and the trick is you have the animal look at a fixation point and the animal's job is just to look at that fixation point if he looks away he doesn't get rewarded if he keeps looking he gets rewarded if the animal learns this well enough you can wave things around and in primary visual cortex you can find the receptive field location and the orientation selectivity of neurons and it's a lot of fun I've never seen anyone go in the lab when we're recording in v1 who didn't enjoy it because you put that bar or slit on the receptive field and it goes on and the cell goes it's really nice and this is sort of what it looks like imagine these little pulses of sound and this is the optimal what's called the optimal orientation the orientation that leads to the maximal response you can get from an oriented bar and this is the orthogonal representation and you can see roughly what that looks like this is about 15 presentations of each stimulus it was mixed in with a lot of other stimuli if we consider the brain as a signal processing system can we learn enough about the statistics of spike trains to decode the messages this represent and then the more difficult question does this help us in understanding brain function a question that I'm less sure than the first one so this would be a complete experiment or a complete version of this experiment and here we've taken the data and I'm showing you all the impulses aligned on the stimulus that elicited them and this is a so called orientation tuning curve and one can see that this neuron was selective at something between 30 and 45 degrees and the reason we've reflected this we're using stationary bars a lot of people use moving stimuli I don't believe primates actually look at moving stimuli very much the reason you track things is so you can look at them carefully and if you can't look at them care while they're not moving on your retina your acuity is terrible okay so the first neural coding question if I just counted the spikes how good a job could I do and it becomes obvious that it's really difficult what we've done is count the spikes in a 300 millisecond period here which is the length of time that the stimulus was on and put a dot sorry put a dot on this graph for every time that spike count came up for this orientation and what you can see is that you could get 10 spikes for basically any one of those stimuli so you've got a problem the question is what are you going to do about it and it gets worse because if you look at the distribution of spike counts you now see that at high firing rates this distribution is much broader than at low firing rates and this is no surprise because this is a counting process again you could model this with a GLM model I'm not going to show you a GLM model of it I'm going to show you something else but people do do this now use GLM models because Poisson counting processes are exponential so you can make a very nice model of a counting process or you can consider a binary process where every bin has a spike or not but what this suggests is that this is what is called in the trade a point process and point processes I didn't put the slide in but what we should expect to see if the spike count goes up the variance goes up and if the spike count was equal to the variance it could be it came from a Poisson process because the one of the significant mathematical features of a Poisson process is the mean is equal to the variance and theoreticians love it the other feature of it it's too bad they don't represent neurons but I'm just going to assert that these distributions are not well fit by Poisson distributions so it's a point process but it's not Poisson ideally we'd like to decode as the response is unfold in time this is intrinsically a probabilistic or statistical problem how likely is each experimental condition that I've visited the spike train at any point in time and that's what I'm aiming for and obviously we think we can do it or I wouldn't be bothering setting this up we try to derive a model for the statistics of the spike trains so it's another way of using statistical representations the problem might be relatively simple if all we had to do was count the spikes to estimate the rate however there's this other little complication which is these two stimuli were chosen because they have there are two things that are of interest to me one is that the shape of this average of all the spikes if you just collect them down the page shows that the shape of this is different than the shape of that and if you look at the responses you can actually see that for this stimulus the neuron elicits a couple or three spikes and then has a period where it goes silent and then starts firing again same neuron different stimulus different at the beginning I would argue that if that were accounted for it would help in decoding the response if it's not carrying the same information as the spike count and one way I can show by example it's not carrying the same information as the spike count is it turns out that these two sets of responses have exactly the same mean firing rate and exactly the same variance that's how I happen to pick them but it turns out in our data it's not hard to go through the data and find these kinds of examples which means now that we have to worry about the temporal aspect of the response there's another kind of temporal aspect you can worry about these are responses where the contrast is varied and the orientation is varied here this is a primary visual cortex neuron the contrast is going down as you go down the column and what you can see by eye because this neuron is so silent is the latency the time of the onset of the response gets longer as the contrast goes down but the firing rate does not go down to the same degree it does go down a little but not nearly to the same degree and what's quite dramatic is to look at this response to this very faint this would be barely visible to you at 6% contrast this has a long latency and a higher firing rate at this orientation you see a shorter latency but a much lower firing rate and these cells are basically every cell in primary visual cortex that we've recorded in the upper layers does this I'm going to skip this how to decode them we want to know whether we're going to use the course firing rate or the individual spike arrival times I'm not going to spend a lot of time on that what code do you use spike counter, spike timing I'm already roughly rejecting the spike count code spike timing there are two ways to think of spike timing one is that the rate is varying but the spikes that are being elicited are sort of at random given the current rate the other is that there are spikes that are just unexpected from any kind of a random model if that makes sense so the possible relevant parameters are spike count spike count distribution as I showed you shape of the spike density which is that rate function from averaging patterns of spikes and correlations across neurons I probably won't finish all of this but we'll try so the first thing I just want to establish this is the graph that I thought was earlier I'm plotting the mean spike count against the variance for all of our data in a reasonably long counting window is that the variance to mean ratio is off this identity line so these neurons have a different variability structure this actually can be understood this is what you would expect to see with bursting where you have some periods where you get a bunch of spikes close together and other periods where they're further apart and in fact that's what most of our responses look like so this shouldn't have been a surprise I already showed you that I already talked about this issue sorry these are repetitive okay so you could decompose these responses we did that a long time ago using principle component analysis it's a way of taking data and compressing it and this is the average response across a bunch of conditions these are the principle components which add more and more complicated temporal dynamics that's the way to think about it for now that is not what the principle components how they're derived I just don't think I have time to talk about it here if you want to know I wouldn't tell you because I'd rather get on to something else this is just showing you the principle components or a good representation of the data this is reconstructing these two responses these are the same two responses where I showed you the rasters and the spike densities by the way this is just magnified a little bit in time these two have the same spike count this suggests that the first principle sorry these are the firing rates and they look identical this is the first principle component sorry this is the first principle component it tends to map the average firing rate and those are identical I asserted to you these have the same firing rate and this is essentially a quantitative test of the demonstration that that is true the second principle component had this funny peak in it with a little bit of depression and what we see if we try to reconstruct these two responses using the second principle component is all of a sudden we discover these two responses really are different but it's not in the spike count there is some slowly varying aspect of the shape of that of this thing it still doesn't answer the question about whether the spikes are stochastic or not and we're going to get to that and I'm going to skip the rest of this so the other observation you have to make about this is that it is a point process and you could think of any period you want broken into let's say one millisecond bends and the reason we're going to choose one millisecond is lots of neurons have a refractory period in the neighborhood of one or two milliseconds so the likelihood that you're going to miss spikes because they're closer together than one millisecond is low there are a few places where you would have to be careful about that but most of the places in the nervous system you don't and it's broad enough so you get every spike and you get to look at the statistics of the spikes and if you were just throwing the spikes down at random into the numbers of bends it would depend on the numbers of bends and the numbers of spikes this is what in statistics is n choose k it's the number of combination number of ways you can put k spikes into n bends and if it were a uniform rate you could just calculate this and know everything it wasn't a uniform rate and I'm going to skip this oh no I'm not going to skip it because this was an observation again this has to do with visualize your data always visualize your data we didn't do this for a number of years and we missed this point and we probably would have gotten to an understanding earlier had we made this simple graph remember I told you the variance of the spike count goes up with the mean linearly which means the variance of the first principal component goes up with the first principal component because the first principal component is something very close to representing the mean what happens with the second principal component which has this funny shape what is it related to in terms of variance and we fully expected that this graph the mean of the second principal component against the variance of the second principal component would look like the variance of the first principal component against the mean of the first principal component that's just not what happens it turns out that the variance of the second principal of the sorry the mean of the second principal component is still related to the variance of the first principal component and you can understand this I see puzzle looks why is this the case and it's because of N choose K the counts the number of combinations of firing patterns that you can have are constrained by the number of spikes you have so it means two things the principal components weren't actually a good way to look at this and we had a mileage out of it and we had not fully understood that you cannot talk about the patterns of spikes in a spike train till you know how many spikes you have available to make patterns N choose K you just cannot know until you know something about the spike counts and we did some stuff looking for repeating patterns of spikes I'm going to skip the information theory well we did information theory and I don't want to spend a lot of time explaining this but it is a way of measuring the relation between two distributions that are stochastic and basically it is a way of estimating if you have some random event and I send you some signal about that event how much better can I guess what the event is given that I know the signal okay so if I say I've got I'm thinking of one of my four fingers and I just tell you that I have I'm thinking of one of my four fingers you have a one chance and four of guessing which finger I'm thinking of if I tell you I'm thinking I've doubled your chance of guessing correctly and what information theory does is formalize that idea it makes it so that you take the entropy of the problem the uncertainty of the problem transform it by a logarithm so that you're in not multiplying probabilities but adding or subtracting you now ask again the probability of guessing once I've got the signal and the difference between those is the amount of information that's been transmitted either that means something to you or it doesn't if it doesn't don't worry about it too much at this point what it did allow us to do was look at these repeating triplets as funny codes and we could do the spike count and discover that 0.4 bits we could do the repeating triplets and discover that the repeating triplets carried information about which stimulus we had shown but it was less than the spike count and then the question comes if I knew both the repeating triplets and the spike count can I do better and the answer that we got was surprisingly no you can't do any better at all which should lead you to the intuition that somehow the repeating triplets again are related to the spike count and you need to go back and think more carefully about N choose K it suggests that it's a random process sampling something and the question is can we guess what it is now in the last 10 minutes I'm going to show you what we think it is okay not everyone agrees with us but I have yet to see a data set where we've been allowed to do this analysis where it didn't work pretty well actually spectacularly well what if we assume that spikes are stochastic samples not necessarily Poisson of the rate function so we're going to just ask what happens if you throw spikes down at random so that if you do it a lot of times you'll recover this thing back okay and the question is how do you do that and it turns out you could do it the hard way which is to simulate all the bins and throw down spikes turns out there's a much, much easier way to do this and you all have probably or many of you who've ever analyzed data will know what this trick is implicitly though you may not know explicitly which is you take this thing and integrate it so it becomes a cumulative distribution okay you've just integrated this and now you're going from probability zero to probability one and you now say I have a uniform random number generator and I need a spike train with five spikes in it but I need in the long run to recover this not to have it be uniform there's a theorem proved by Kolmogorov of the Kolmogorov-Smirnov test a well-known mathematician statistician and the theorem says that if you map a uniform probability distribution through the cumulative distribution of any probability distribution you'll get a point process back that matches the distribution you're after and the reason you all know about this is most statistics packages these days have a way to construct an arbitrary probability density function for you and they make use of this theorem to do it so what we did is said what if this is a model of a spike train so we know the counts are not Poisson but we don't know about the spikes themselves so what we did was just to map take the experimental numbers the numbers of spikes we got in the experimental data and just map them through this function and create artificial spike trains and we can create as many as we want and we can now match the distribution so we call this the spike count matched model oops if you do that and plot the number of these funny triplets in the data against the predicted number you get these these regressions that are better than I mean, you just can't expect to get this in biology it's a slope of one and on r squared of one it basically explains all the data and this works both in primary visual cortex and in the lateral geniculate we know it works in motor cortex also and I'm going to skip this so the spikes appear to be stochastically distributed according to the spike density the number of spikes must be accounted for to represent spike timing accurately how does this help us we can decode so you need to know one more thing which is how we built the dictionary so in spike count matched model we used the spike count distribution and the probability distribution of spikes over time and the integration of this so the spike count and spike density function carry the information about the stimulus that's our conclusion individual spike times are random with probabilities determined by the spike density function of the spike count well, it turns out there's a well established branch of statistics that knows all about this developed in the 30s but not paid too much attention to until about the 1980s called order statistics order statistics describes the result of independently drawing an ordered fixed number of samples from a continuous distribution doesn't that sound like what we did for the spike counts the spike trains yeah order statistics can be adapted to describe the spike trains and it allows us to calculate with the spike count matched model simulated so you can just calculate it all you need is this equation this is the order statistic of the k-th spike out of n spikes at time t actually it should be a point process I should say the k-th point I'll slip and call it spikes the this is n choose k multiplied by k this is a normalization factor to turn what I'm about to do into a proper probability density summing to one this is the integrated the cumulative distribution or the rate function and what this is is the rate function raised to the k-1 power which is what I can get rid of probabilities due to spikes that have already gone by so if I'm interested in the third spike I've got to deal with the probabilities that I need to account for the first and second spike that's what this piece is doing this is the um the probability density itself so you want the probability there and then 1-f of t to the n-minus k is to deal with the probability that's remaining for the rest of the spikes that have yet to come okay? and this gives you back the probability density and again we're using this this won't quite work the way I showed it you can only deal with it for the first spike because once a spike comes by what I gave you was the unconditional density we want the spike decoding to be conditioned on all the spikes that went before but we're going to do just a small amount of algebra that you either follow quickly or you don't did you send them the reference for the reprint? okay there's a reference that I put in that has this if you really care and so all we're really interested in is the time of the first spike then which is n times f of t to the 0 so that's just 1 f of t, 1-f of t to the n-minus 1 and then we're going to condition on previous spikes so we're going to consider every spike as the next first spike so every time a spike comes we're going to reconstruct this whole thing and do it again and we're going to this is some sample data this is what happens to the probability of spike 1 as the number of spikes goes up okay if you have only one spike in the whole period on average you recover the rate function you had in the first place if you have the first spike out of 5 you've got to get the first spike in and leave enough time for 4 more these are actually responses with more spikes in them and as you have more and more spikes in the train you see that it becomes peaked more and more that actually explains why latency looks shorter for higher firing rates because you're sampling of the driving function the rate function is actually denser deal with the dependence on n you can do that by just multiplying these first order statistics by the probability of having n spikes and you get something that looks like this the count-related first order statistic and then you start decoding using Bayes theorem the other thing that's cute about this is I'm going to show you in one second you get a probability of a stimulus when there is no spike also because 1 minus h of t the probability of a spike at a given time is the probability that you won't have a spike at a given time so you can keep decoding even when there is currently no spike this assumes you know the prior distribution but all decoding assumes that you have knowledge of a prior distribution no matter how you did it and I'll leave this and this is two spike trains being decoded a 0 degree bar a 67.5 degree bar these are the spikes the vertical lines this is a very low firing example the first couple of spikes don't do much for it but all of a sudden in this period where there are no spikes this guy starts to decode this one has basically finished decoding guessing that this anything that has these first three spikes looking about like this will be stimulus for whatever that was and it just keeps going and finally this guy finally finally figures out that it was probably stimulus one and you can do all kinds of calculations with it you can find out how well you can do the decoding at any point in time I used to have an example I guess I left it out where it started decoding before any spikes had arrived because the fact that no spike had arrived meant that it had to be one of the high contrast stimuli and the spike train came from a low contrast stimulus so the fact that there was no spike early meant that there had to be it had to be one of these low contrast stimuli and the decoding goes up really rapidly and you haven't seen a spike yet so it's I guess I'll skip this because we're done if anyone wants to talk about how to use this for pairs or more of neurons it works and the only thing I have to get to because the second half of this talk is all there just to set up this quotation there is in the United States once a year contest for someone to write the first sentence of the worst novel you can imagine ok this was the winning sentence in 1999 as published in the New York Times it wasn't the best of times it wasn't the worst of times it was the times you'd get if you arranged all possible times including fictional times in which the nights were usually dark and stormy in order from worst to best on the real number line from zero inclusive to one exclusive and then used a really good random number generator of value in that range thus choosing the corresponding times that's the times it was and that's the times they are when you're looking at spike trains so thank you 1230 on the nose if anyone has questions I'm here the next couple of days and longer and I'm happy to discuss any of it I hope I'm not as young as you guys are but I hope you see that even at my age you can still be enthusiastic about doing all this work it's been a real great ride for the past 35 years doing this kind of neuroscience so I hope all of you have as much fun as I've had any immediate follow up questions


It's real important for the Denver Police Department to reach out to the community. I got purple, red, blue. Off-times police officers only see the citizens in adverse situations. We really are pleased for the police that are around here. Hamburger? Two patties? That's right. They're kind, they're generous, and they try to help us as much as possible. Can I get you any fruit, ma'am? Thank you very much. They're everyday people. They're fathers and uncles and brothers, and children need to know that.  To interact with the youngsters that are coming up, that's the most important thing. Have the young kids look up to them as a partner, instead of just being a policeman. I would like a shake or cookie, please. We need to support them in the community, and they should be out here, so people aren't scared of them. Thank you guys so much, I really appreciate it. They're very supportive, and we're very thankful for them.


This paper proposes a new method for evaluating the presence of corrosion in beam-like structures. It takes into account shear deformation, which is often overlooked in traditional methods and uses it to accurately identify damage. The proposed method involves determining the analytical displacements of the structure based on the Timoshenko beam theory, then minimizing a predefined objective function to find the optimal solution. The results show that the proposed method is effective at detecting damage in beam-like structures. This article was authored by Xiang Wei Meng, Feng Shao, Yu Yan, and others.