Hi, welcome to today's webinar. It's over to you by a professional learning program. I'm very happy to welcome all our alumni here at this webinar. My name is Friedrich Knuth and I'm Director of the Turwig University Fund and I'm also Director of Alumni Relations. And today, as Director of the Fund, I wanted to ask your attention for something that really concerns me and it's about our international students. They are in financial distress due to COVID-19. We have all kinds of arrangements in an allowance for students but international students are not covered by this. At the Turwig University Fund we set aside funds to help these students who are in financial distress and can't study. I would like to ask you today if you would please consider donating to these students. That would really help them a lot because you know if you're in financial distress it's really hard to study and focus on success. So if you could help us today we would really appreciate that. We'll send you a ticket and SMS with a request for a donation and obviously if you don't want to donate it's fine just ignore it but if you can and if you are willing to that would be very much helpful to our students. Thank you, enjoy today's seminar and see you soon. Welcome the alumni of Turwig University to the very last and the sixth edition of the Turwig University Knowledge sessions in close cooperation with Alumni Relations and the University Fund. My name is Kiercham Peters and I'm your host for tonight. Before I introduce tonight's speaker to you let us first agree on a couple of rules or guidelines in order to get the best out of tonight's session. So here we got a couple of guidelines. So please think of a Zoom meeting as a face-to-face meeting conduct yourself as if you would be all present in the same room online. Find a quiet space without interruptions ensure you have a plain background address as if you are attending an in-class lecturer. You may sound a little bit odd. We have had some experiences over the last couple of weeks that this can be very annoying. So there's a couple of rules here. So if you would have any questions during tonight's lecture please don't hesitate to post them. You can go to the chat function post your question and send it to everyone. Then either Kuno or us we will pick up the question and answer them during the session or after Kuno session. So those are a little bit the rules of the game. So let's quickly go to tonight's speaker. Let me introduce to you Kuno Heusman Ambassador to Ambassador of Professional Learning programs and he's holding the endowed chair sponsored by ASML and the known company ASML. Kuno is working for Tyson for the Department of Econometrics and Operational Research and like I said he is organizing quite some he's been organizing quite some masterclass already this year which were already all time very well evaluated. So with that I would like to give the world to Kuno. Let me wish you a great and inspiring night. Thank you Gert-Jan for the nice and kind introduction. Let me first share my screen. I hope you can all see my screen. So let's get started. What I would like to do is basically three topics and I would like to of course spend most time on the last topic. That's about decision making and on what are the topic of decision quality. But first let me introduce myself a little bit more and then I also would like to introduce ASML a little bit more and I'll tell you why I do that because I think there's a great link between the topic and ASML at least for me and my personal life and I'm getting working on this topic. So here just a few pictures of myself. So I've been born and raised in the southern part of the Netherlands went to Tilburg University to do my studies also finished a PhD there. That's 20 years ago now I would say and then the left hand side and the top you see and the title of my thesis, Technology, Investment, and the Game Theory Options model approach. Very nice time at Tilburg University doing very theoretical models but then I said okay I really want to do something in practice and that's why I joined the CQM for which the logo you see here. This is a company in Eindhoven also a spin-off of Philips Electronics working on using quantitative methods as consultants. And then 10 years ago, so I spent 10 years at CQM and now 10 years, I'm almost 10 years at ASML. So I joined ASML 10 years ago and being at ASML so to say I also went to Stanford and the topic that I will address later on on how to make decisions in uncertain times is coming and has been developed at Stanford somewhere in the last century. And the right hand side you see a picture of my inaugural speech that I held three years ago, almost this date but I think three days ago or something. When I accepted the end-out share like I did also the pointing at.  I like skiing with my family. I like to do some running, cycling. So this is on the Mont Ventoux in France and then of course I really love my three children and we also have two dogs. It's a full family so to say. So this is about myself. Then something about ASML. ASML is a company which has its headquarters in the Netherlands. It has been founded by Philips Electronics and ASM International in 1984. And we are in the world of chips and they're not chips that you can eat but the chips that you see in all electronic devices. So what I tried to point out here is that's really hard to really think about a world without chips. So think about iPads, iPhones, Samsung Galaxy, your watches, the game consoles, autonomous driving of course, big data centers and also we also have chips sometimes in human body to really make sure that we can have a better life. That's really hard to think about a world without chips. So one from a process point of view, Kecan, I cannot see the chat at the moment. So if there are questions you really need to help me. I thought I could open it at the same time. No problem. I thought I could open it at the same time but it doesn't work. No, no, don't worry. Since I will let you know. So this we are in the world of chips and the chip industry is a very fast growing market I would say, especially also in these corona times because everybody is working at home, working with Zoom, Microsoft Teams, Skype, all these kind of things. People really need one plan with, need data samples are needed for that. So a lot of chips are also being produced and used for devices at home but also in the data centers. Now what you see is that so in 2018 we passed the one trillion chips, so to say Mark, so more than 130 chips for every human on the planet. Every year. So every year every human has a consumption of 130 chips. And then from a revenue point of view, so turn off point of view, it's a big market that's a form of safety almost billion US dollars in 2018. So there's a big money going on and it's growing very fast. So what do we do as ASML? We don't make the chips, but we make machines that can make the chips. Now we make one specific machine even, and it's a lithography machine. So there's a picture of the lithography machine and you can think about it as just as a big copy machine. So what we do is we have an image of a chip that has needs to be created. You put the image, the customer of ours put the image in the machine and then it will be printed on the wafer which is here below and under the light and then it will be printed and then the next line will be there and then this is how we build up chips. It's the most critical tool in the production process because this makes it possible to really shrink and to make as the same more transitions on a chip. I will come back to that. Today, ASML has more than 25,000 employees worldwide. Almost half of them are in the Eindhoven. The other ones are working close to our customers and most customers you can imagine are in Asia and in the US. When I joined ASML to give an idea, 10 years ago we were with 10,000 people. So we more than doubled the number of people in our company which has all interesting things that happens to a company when you double in this size. Also sales has more than doubled. So in our last year it was 11.8 when I joined it was almost four. So we're really in a big growing market and also the company is growing fast. And then if you think about, what makes ASML so special? So you see a picture of the campus of ASML. Here is the highway to Belgium. So on the bottom part of the picture you have Eindhoven and this is the highway to Turnhout. And if you pass by by car there you will notice that there is a very long parking lot there because 12,000 people have to work there and everybody needs a parking place. So having said that in the market that we are operating we all the major chip markers are using our technology. So if you have a device at home where you are watching this webinar it's I would say 99% sure that the chip that's in the device has been in an ASML photography machine to create it. We are the second biggest spender on tech company and market cap so 100 billion plus. There's also something that of course increased over the years. So when I joined ASML, the stock at the Amsterdam Stock Exchange was around 25 euros. I just looked at it and today we hit 349 euros. So that's more than a 10, what is it, even a 12 times increase. And then we have an annual R&D budget of 2 billion to really keep up with to make sure that we can deliver to our customers that we have the latest technology and the latest and greatest. So this is a big campus of ASML. This is the factory. So now I'm working in the factory at ASML. So I'm the business controller or the financial director to say, oh this factory where we built not the latest technology that's built in the factory below here and then at the end you still see construction going on because there we're building a new logistics center which is also part of the factory that I'm responsible for. And this is something like five soccer fields. So it's a really big facility to really handle all the materials that we need for our machines. So what is the driving the semiconductor industry? That's the chip industry is a semiconductor industry. It's Moore's Law. And so Moore's Law, Gordon Moore is one of the founders of Intel company in the US and he said somewhere in the end of the sixties beginning of the seventies last century that every year the number of transistors per chip will double and that was adjusted a little bit later that it's every two years and this trend has held for more than four decades now. And so we're on the line cannot really read it without different processors so to say from Intel where you see that because it's a logarithmic scale you should notice. This axis is not a normal scale but it's logarithmic.  So you see here that the number of chips this is the number of transistors and transistor in the chip there's nothing else and an on-off switch that doubles every two years and it has been there for more than four decades now and it's the ASMLs. How do you say it? Mission to be here on the Europe to really make sure that this happens that we can still do this all for having the benefit for the humans of course. So it's a law of economics also think about it most law of printing this book The Hitchhiker's Guide to the Galaxy think about that if you would print it at a font size 14 and you know it from Emma's word or PowerPoint then you would need 227 pages. Now if you swing it to font size 7 then you only need 140 pages so this is the idea of shrinking so that's what ASML also does we make chips the same chip we make smaller so now you can print the cost the book at half the cost you're also half the time to print it because it's less pages that you have to print and you have of course double the information density of the book and this is essentially what we also do with chips really shrink it and then two-dimensional not only one but two-dimensional and now it's also sometimes three-dimensional so we make chips cheaper so here you see an example of that you see the time on the horizontal axis and you see again a logarithmic scale of the price per gigabyte and this is flash memory I have to explain what flash memory is flash memory is the memory that you see when you buy a Samsung Galaxy or an Apple iPhone and it says 64 gigabytes in memory or 128 gigabytes memory this is the kind of chip that's in so in 2004 this price was $110 per gigabyte and so at that time if you would like to build an iPhone so to say with 64 gigabytes that would be too expensive because it would be only for the chip already it would be more than six and a half thousand dollars 2018 this price went down to 30 cents and nowadays it's around 25 cents so now you can see that it comes interesting to really make these products because you can on a cheap way produce these chips and also make them give them to your consumers to store data on the other thing of course what most law has done is has made electronic devices more powerful some of you may recognize the left machine that's one of the machines that I saw when I also entered Tilburg University some time ago I would say and IBM I also worked a lot in secondary school two floppy drives, one for your operating system but all one for your files introduced in 84 then think about the latest iPhone the iPhone 11 Pro which has been introduced last year price range it's of course cheaper the iPhone there is a bit better processor in it much faster it has a working memory of 4 gigabytes whereas the IBM had to do with 16 to 265 kilobytes so that's really some dimensions in between the floppy disk where you could store 160 kilobytes 512 gigabytes of the flash memory then the monitor the number of pixels on the phone instructions per second that's magnitude different and also the weight is different so you make by screenking you make more powerful devices and that also opens markets and drives market growth which is in this picture so you have the same green line that shows the price and what happened over time in 2004 time frame digital cameras came up because we had a means to store digital images because flash memory was there still very expensive but for professional photographers it was possible then MP3 players came then after that smartphones entered the area and say four or five years ago we saw the transition from conventional hard drives to solid state drives and laptops meaning markets for our customers transitioning from conventional hard drives to solid state drives meaning of course more chips per human meaning also more lithography machines which is of course good for asml so this is about asml so I joined asml 10 years ago in the decision support department and I have to explain it that's the department that goes to the board and the executive committee of asml and that team supports them in decision making and to do that they use this concept of decision quality which I'm going to explain to you to say okay how can you really structure and make sure that you make good decisions and I mentioned it before this is a concept that has been developed at Stanford University I really liked it so I came across it in 2010 when I joined asml and I found it a pity that I didn't knew it earlier because it really helped me solve also difficult projects but also discussions with stakeholders and projects you can really use it to make sure that you're working on the right thing and have the right agreements on it so I think it's a very nice machinery and tools and the coming time I would like to take you along okay how does it work and what does it bring you are there any questions so far on asml no not so far okay good go ahead so decision quality and so what is the goal for me what I would like to do and I understand it's just a flavor in the webinar so we can always what Geertjaan says I give a yearly masterclass about this I also do that in company but I think it's good to understand that you can of course learn more about it you can also read a book of it, you can send me an email and guide you to some websites or reading material because I think it's very powerful and very practical usable also for decision making so it's a toolbox I should warn you decision making will not become easier but I think it will give you a ways to tackle complexity in a structured way so thinking about uncertain times for example you have a way to really make sure that you use uncertainty and also embrace uncertainty and make it visible what it really means what I always tell to people because I give workshops on this in the company but also to my students at Tilburg University and now to you I try these concepts at home and so I'm not saying to you prepare some nice power points to compose to your wife or husband to be but think about the concepts because I think asking somebody to marry buying a house, whatever, these are big life changing decisions think about them and I'm not saying you only should do these things by ratio that's maybe the feeling that you can get after I told you the theory let me just be clear think there should be a balance between ratio and emotional decision making and the feelings because if they are not sensing you have to think about it what's going on here so is it my feelings that are maybe not driving me in the wrong direction or is it really the structure that I set up that's not correct so make sure that it's really in balance but this gives you a way to really on the rational part to really rationalize things and make sure that you have to write things on the table for the discussion so try these concepts at home so there are a few I think important things that I would like to get across and one of the most important one is the difference between the decision and outcome so you see a lot of people they judge the quality of a decision on the outcome so they look back and they see a specific outcome of a decision and they say then this was a good decision that we made half a year ago, a year ago or whatever or they say this was a very bad decision because look what happened and I say that this is fundamentally wrong so you shouldn't judge the quality of a decision by looking at the outcome you should judge the quality of a decision at the moment that you make the decision then you should have a feeling or at least I would say that you should have a measure to really make sure that you know that you made a good decision and so and yes of course you really want to have a good outcome I don't get me wrong but you want to make very high quality good decisions and then yes it can happen that is a bad outcome but you really want to have at the moment that you make a decision make the best decision because hindsight things of course very easy hindsight you can say why didn't the Dutch government start to do COVID measures 3-4 weeks earlier that's something that people say now maybe we maybe not so many lives would have been more lives would have been saved yes that's true but you should look at at the moment that the decision was made by the government and I'm not saying that it was a good decision because I don't know I wasn't involved but you should look at the information that's available at that time and if they at that time should look at all the circumstances all the things they have Mark Götter mentioned himself I have to make 100% of decisions with 50% of the information this is of course real life this is how things happen but look at it and did you look at it and did you I will tell you how you can judge the quality of the decision if you look at all the six elements do you still think this was a good decision yes or no I didn't evaluate it maybe somebody can do but that's how I look at it and then hindsight you can say you can look at it okay is it a good outcome or not so what's the decision just the definition it's an irrevocable allocation of resources so it means something irreversible something that you cannot turn around you spending your time now in this webinar means that you cannot think when it's 8.30 I let me do something else last hour that doesn't work your time is spent time is gone since sometimes happens with money when you buy a new car you all know if you go back to the dealer two weeks later you don't get complete your money back there is some part irreversible in it so think about that so how do you really allocate the resource that you have can be time, can be money, can be all of things then a good decision is logical one based on uncertainties, values, preference of decision maker, come back to that and a good outcome is something that's profitable or otherwise highly valued so let me give an example on the bottom part of the slide you see the boxes so a good decision can lead to a good outcome but also a good decision can lead to a bad outcome same hills for a bad decision can be very lucky and you can have a very good outcome but you can also of course make a bad decision and have a bad outcome so let me give a hypothetical example of myself when I was young so to say I went to a pub say on Saturday evening and I drunk a lot of beers because there were a lot of friends there and I had a really nice time and then certainly it was time to go home and I walk out the pub and I see my car and I step in my car and I drive home so I was totally drunk but I still step in my car and I drive home and I come home safely and so I didn't hit anyone on the road I didn't park the car in a tree I parked it just on my driveway in front of my house so everything's fine I still say this is a very good outcome but a very bad decision I shouldn't drive when I'm drunk so at the moment I should make the decision not to drive I'm very lucky because I made a bad decision and I have a good outcome the other way around you could also think you're drunk too much you shouldn't drive yourself don't be a fool, don't step in the car just call a taxi so I call a taxi, so I think that's a good decision and this state of mind being under alcoholic influence but this taxi driver, we drive off and he starts driving to my house but he doesn't pay attention to the road he's just texting on his phone so while texting on his phone he drives the car into a tree so I'm in an accident I still think it's a good decision to say but it's a good decision not to drive myself but I'm very unlucky and I have a bad outcome so make sure that you understand the difference between the decision and the outcome it's very important to look at that and why is that there is not just one arrow from a good decision to a good outcome but also from a good decision to a bad outcome and that's because we have a world of course with uncertainty that's why there is uncertainty in the world some people sometimes say to me I don't like uncertainty then I say I disagree because I think there are good things that you shouldn't know in life I shouldn't know for example some things in life depending on when are you going to die what's going to happen to you or to your wife whatever things there is a lot of uncertainty in the world but you have to think about it and make it into consideration when making decisions so you cannot change it there will be uncertainty make sure that you don't ignore it but incorporate it in your decision making the last thing to say about this slide is that there has been scientific research by looking at some German companies where they had very good notes of the Board of Management meetings where you can show that if they really focused on the six elements of decision quality that I will present after this they if they were on a very high level so they made good decisions that also the percentage of the outcomes was also more in the good area so there was a positive relation between making good decisions and coming out in the good outcome so that's something that you can really show of course no guarantee because there is still uncertainty so important look at the decision moment and not at the outcome so okay if you say to me okay now I cannot look at the outcome so where should I look at that and where should I focus at that's the concept of decision quality that has been developed at Stanford University and it came up with this six elements of decision quality and here it's depicted on the right hand side as a chain meaning there are six links and the weakest link determines the quality of the decision so let me introduce the six elements to you so the first one it's not a process because I will also present the process how you can approach this but it's more like there are one to six and I think one is important to start with but also sometimes you have to go back so it's not that you have to first step one then two and so on no it's something that you have to keep in mind that it's also possible to go back so the first one is about a frame and so it's the context of the decision so it has to do with okay what are we trying to decide upon is it for example at Tilburg University yeah so assume we are in a lecture room is it that we're going to refurbish that lecture room or is it about that we're going to refurbish all the lecture rooms in the building that we're in or maybe refurbishing all the lecture rooms at Tilburg University or maybe all lecture rooms of Tilburg universities at all locations so what's the scope of the decision and make sure that you have agreement with the decision makers on so what's the scope so that you really know okay this is really where I should focus at very important and very useful I noticed in practice also to come back to it if you have a decision process that takes more than a few days that you really can go back and say last time we agreed upon that this is the the frame and this is the this is the thing that we are going to focus on write it down that's another advice that I have and so really write it down and make sure that you can get back to it second point is about alternatives so make sure that decision makers have something to choose from I also see at ASML sometimes people go to the board of management and they say you should do this and that doesn't work actually you should give them a range of opportunities or options or alternatives to choose from how decision making works there should be something to choose from otherwise there is no decision and in support and that is of course doable so that they are doable and that they are synced and that also human brain isn't really capable of thinking about say 15 or 20 different decisions alternatives so make sure that's a maximum handful and the most companies I encounter is that if you go from 1 to 2 or 3 the quality of the decision will massively improve because you will open up and see different routes to come to the right decision the third one is about information and so it's also about uncertainty make sure you show what the in fact is of uncertainty and make sure you have relevant and reliable information so make sure you team up with the best information sources can be also be of course if I look at ASML I worked a lot with the data experts for to say people that know and experience in developing new machines, new technologies sometimes you have to extract data from these people and also make sure that they also show you what the range of uncertainty is that they work in so don't ask them for one number ask them for a range so not okay this it will take us 10 days to develop this part now it will be somewhere between if you have a very lucky it will be 8 days but it can also be 20 days so it will be very interesting to show what the effect of uncertainty is fourth one it's about the value metric it has to do with okay so what is what you really find important to make this decision so what do you really want to get out of it so what's your metric for this now ASML is a stock listed company I just told you stop price of course making profit is very important for ASML we are a stock listed company but on the other hand we also want to be technology leader and then we also want to do something good for our customers then we also want to do something good for our release we also want to do something good for the so we are one of the sponsors together with VDL and Jumbo of Paceway Eindhoven we don't do that because that costs money of course maybe it would be better for ASML not to do it because it's not automatically giving profit we're doing that because we also find the region where we're acting in very important we want to make sure that we can attract a lot of talent from outside of the Regio Brabant to Eindhoven to come work there and then also a soccer club at some level and you can doubt whether it's your team or not but it's at least one of the soccer teams in the past that has been champion in the Netherlands a few times that's also another region the same thing for my chair at Tilburg University we do that at ASML also because we think it's very important to have close relations with universities and so I have nine other colleagues and also part-time professor at some university somewhere in the Netherlands and so that's something that was important and making a decision to say yes we're going to to endow this chair at Tilburg University and the value metric was not just spending money it was about what is the influence of these things so think about it so what's the value metric why you make this decision and if you have a group of decision makers are they aligned on that or do they have different value metrics so one thing I didn't tell but at ASML for example you have two presidents so we have one president which is a former CFO we have one president who is still the chief technology officer and together they make a lot of decisions of course for the company and sometimes of course one may think the CTO may think about I want to be technology leader but it can conflict with making profit for the shareholders so what's now the most important thing to make this decision so make sure that you understand that and also that you align that fifth one is about logical reasoning and that's where I see that people with a quantitative background are normally very good at really make sure that they explain why is this alternative better than that alternative on this value metric that can be modeling but it can also be a qualitative reasoning and then you think you know what you have to do but then the last element comes in and that is about alignment and commitment that says to do that all decision making is nice but if there's no commitment to action nothing happens and here I can give a personal example so two years ago I found myself a little bit too heavy so I thought okay I need to lose some weight so I thought what I'm going to do I want to get up say two times a week at 6 am in the morning and then I will not go for the shower but I will put on my running shoes and clothes and then I will go for a run so I think to improve my health I think most of you can agree that's a very good decision to say okay maybe you can lose some weight by doing that and then your health will improve so what do you think what happens at the first morning my long clock goes at 6 I snooze it because I don't want to get out bad of course because it was still so I think still very good decision of myself I really want to improve my health so let's go out for a run but it didn't work out so what did I do that evening I said to myself now tomorrow morning I really want myself to get out of bed and go for a run so what I did is I said to my wife I said I'm going out for a run tomorrow morning and that was for me enough to get commitment to action in myself so when the alarm clock goes at 6 I wouldn't want to have a discussion with my wife saying okay now maybe I stay in bed or I don't want that was enough to have the commitment to action for myself and the 6th element it's not an easy one some people say yeah but it's clear if you know what the best thing to do is it will happen I think most people working in companies or organizations know that's not true there is a difference between getting the right and getting what's right done and this is all in the 6th element that's about commitment to action so for me personally I had to control myself making an agreement that my wife was enough sometimes in the company of course more of us needed to get that agreement done so this means, number 6 means taking them along, taking them actively in your decision process making sure that everybody knows what's going on listen to them arguments with you all these things to make sure that at the end when there is a decision made it's also executed, it's committed otherwise all decision making it's nice because I also like it from a scientific point of view so you can also get some benefits from that so to say but there's nothing that's changing so I didn't say that before but I think this is one thing that I came across in years I think if you really want to steer your life you have to make decisions and you have to take control of your life by making decisions I think this is a very nice framework these 6 elements that you can really make sure that you make the decisions in the direction that you really want to have it so on the top it says something like decision maker says what I say to the people in the executive committee and in the board of management as well you have 6 decision rights like in court you also have rights but you have 6 decision rights at the moment somebody asks you to make a decision you should think about these things in your mind do I understand what is it about and do I know what the frame is do I see relevant and doable alternatives did I see that they take information into account do I know what my preferences are and what the metric is for that do I see a logical story explaining why I should do what and then am I also committed to go to action these are the 6 elements and I think this is I really like it and I have seen very powerful examples both in personal life in ASML but also at university to really make things crisp and sure that you tackle the complexity in decisions of course there is a whole world behind this because on the number 5 I also teach students in the econometrics and operations research area there are a lot of difficult algorithms to make this model and solve it where it all starts with what is the decision that we should solve so keep in mind that you work on the decisions that are giving you highest value then I come to this chat can I ask you a question sure hello sounds a little bit like McKinsey Ellen is saying sounds a little bit like McKinsey Ellen may do you want to come in please do you have any question to Kunal I hope I'm your boss yes you are there is also a way of working like how they do big reorganizations that everybody is frightened when they come in I think the difference is at least what I've seen I also like with McKinsey what I see is that what we try to do I've been an internal consultant at ASML what we try to do is with this method and that's why also on the 6th element I really stress that you have to take people along and that's what I see as a fundamental difference between McKinsey and maybe they use the same concepts I don't know but at least what I've seen with McKinsey is that they really work with the top management and then come with a solution and they post it on the organization what we do here is we really take the organization along and come with the best solutions so it's I would say in that sense you really want to make sure that you have alignment and commitment also from different layers in the company and I agree this can also be the way McKinsey works but I didn't see it in my cases that I've been working with them so I hope that answers your question well Ella is thanking you for your answer I got a question myself you had some nice examples in which the methodology worked out very well can you give us one example in which you said well you were very much surprised by the result of it or maybe disappointed by it where I really saw the power is in some really tough strategic decisions at ASML where we had really different views in the board of management where this process or this way of working helps you to guide it so maybe I should go to this slide because it also relates to it because there was a topic on the table where we should take some certain strategic direction yes or no and there were two opposite views what this method does you is to say okay we're really going to work them out and put facts on the table to make sure that we have a discussion where the facts are on the table too often it happens that people start shooting from the hip and saying and then the one that has the loudest voice to say wins the argument here you really want to go a step below and say okay and make everybody see what is really behind the two viewpoints and making sure that you also think them through and see what could happen in the future and then putting them together and show what is the best thing the best way forward and so a very concrete example we bought a company a couple of years ago in the US that makes the light source of our UV machine the latest machine that we sell nobody in the world is making it so we are the only ones giving this technology and we had lack of progress in one of the key components which is a laser light to the machine there is no light you can also not make chips to say there is no prints so what we did is we had some kind of way of working already we investigate different alternatives that we could do and we brought them on a very nice one slider to the different people in the board of management that did have different views on it but showing the the real and how do you say it relevant facts on the table made them all see ok there is only one way out here and this is the best way forward and so it really helps you to kind of get that on the table and I will explain this graph so the top right you see strategic decisions and there is a process to do that and I will explain how it works in a few minutes but by following this process we really saw ok we getting people together and that they really talk with each other and that they understand each other and then make stuff forward so it also gives people a way that I see in how they present results I show to people that have to make a decision on any level in the company what are the things that they really looked at what are the options that they really evaluated and sometimes people don't show them they only show the preferred one by themselves but maybe the value metric of people is different that they have to make the real decision so show them to it and that's where I really hint at if they say ok I've seen really powerful things so one on getting people together the other one on getting the scope right making sure that you work on the right thing so by writing it down explicitly so I don't have an example in this webinar but in the masterclass that I do I also let people work on their own problem so this is the scope this is already the decision being made and this is the decision that we can make later give an answer Gertje yes you did so on the slide so we see two dimensions of complexity on decision making one is on analytical complexity so here what I said so you can do a lot of studies on data science, decision analysis, mathematical analysis mathematics, economic analysis whatever you want variables and scenarios and criteria and uncertainties then you need a lot of machinery maybe to solve it that can be sometimes very difficult and we all know maybe examples where you can have this and on the other hand what I said I teach students also in this area so I let them solve differential equations and then they know the answer and then they start working and they also use mathematical methods in their work and they say and they come back and they say to me but now I know the right answer but nobody is listening and that's on the other axis that's organizational complexity and that's to do with if you have a different if you have an organizational structure there are different powers in there so people with different personalities but also different frames are really looking different at the world they have different values, desires, motivations and that all can be in conflict and that gives a lot of organizational complexity if you have a lot of this then you need I always say you need a facilitated leader that can bring people together and make sure that you make the right steps if you have both then that's what I just mentioned the example of the acquisition that we did the dialogue can really help if they're in the middle there is a checklist I will also send it you will get the PowerPoint later on there's a checklist for every of the six elements and you can think about it okay did I really think through this decision and then if you're in the low low then please use your common sense and rule of thumb don't overdo the the methods so this also gives you guidance of where do I use what method and what I do and what I really like to do with management teams or any team making decisions and every team makes decisions if you ask me is to think about what are the strategic decisions of this group and what are the significant decisions and does everybody agree that this is a strategic decision because a lot of times I notice that somebody says this is a strategic decision where other people say no this is a significant decision and have the discussion on that and then the next question is okay and what kind of methods do you use then to come to this conclusion so how do you structure it how do you approach it so that's what I like about this you can really have a good discussion with a group that has to make a decision or confront it with decisions and have to work on decisions so in the meantime two questions came in one from Maurice Weinberg Maurice are you still on can you come in please and ask a question yourself yes can you hear me yes we can yeah so the focus is very much on the decision making itself so there's a certain outcome but you look at the decision so what happens in front of it but is it more like a kind of cycle so you have an outcome you can learn from the outcome and then make a better decision next time yeah so I agree with your point so I really focus now on making say one good decision but of course there's a whole trajectory on it that you see you look at okay I made this decision okay what can we learn from it and how can we improve the decision making next time that's of course true and that's not something that's on the slides but you're totally correct I think that you should of course also include and I have the learning loop there as long as we value it okay thank you and also Ellen came back again with a question Ellen are you still on yes I mean banking now we have a lot of pressure all the time to market how does that impact the decision making process in your opinion so I heard something about markets but I didn't hear the word in front of it it I will repeat the question from Ellen time to market gives pressure does that impact decision making yes it does because I also see that as well as what I said we make very complex machines and if our customer one of our customers has this machine earlier then they can make a lot of money about it there with it because I think about the chips that they could use for Apple for example there's a lot of money in it so the time to market is very key there and it's also one of the successes of ASML the ASML grow very fast it was the one the first one to come to the market with what's called a twin scan so having two wafers in the system at the same time the other one that we did is we had the first with the immersion and now we are the only one with UV source of the UV system time to market is key of course but still it's important I think to realize that this decision making process so that's on my next slide and I only have some final slides to explain a little bit of the steps but I can also do it with this one this process you can run in a week so you can come in a week to a very very good high quality decision and this is a decision you can think about it of course if you if you're in the banking sector and that's then of course you only have seconds then it becomes different and then you have different and you have to think about how can I incorporate this decision making concept for example in my trading algorithms there are also ways to do that to make sure that you look at the right alternatives all these things but if you think about strategic decisions and you have a week you can do this in a proper way you need of course to free up all the people that's most of the time the problem you need to free up the people who have the information so yes it's also possible to do it in a very say short time but still on a high quality so I showed you the six elements and then if you want to incorporate it that's what I want to show here how do you incorporate that in a process for example in an organization then this is a way to do it so you kind of distinguish the people that make a decision so you have to think about okay who is now at the end making the decision is the board of management or maybe a layer below it or is it I don't know is it the manager of the team but who is doing it and at ASML it's most of the time it's a group of people and these people are down the decision board and then there are of course people working on the decision which I call here the project team and they do most of the population work so what they do is there is a situation where the project team is installed they will assess it and on the first discussion that they have it's just about the frame making sure they have the right scope context and also value metrics knowing that okay this is what we want to solve and this is what we want to get out of it so this is the metric that we look at it that's also linked to the question just earlier yes good outcomes are still of course very important so here you're going to make of course a prediction from the outcome because you still of course want to have good outcomes you don't judge the quality of the decision you still judge it on the six elements but here you have they come with a proposal there is a discussion on it and the nice thing which you can use here is a framing slide and I have been in discussions there where you take like an hour to really discuss okay what's in scope what's not in scope if you have that then you make a decision this is the frame that you are developing alternatives same thing you go back you have a dialogue with your decision makers and that's very important that it's a dialogue and that's one of the things that I learned in this process and this way of working is that you should if you're the facilitator of this process or you're kind of helping them to come to the right decision make sure that they have a discussion so it doesn't help if you're presenting in one of the decision making meetings or if somebody is looking at you they are the decision makers let them talk to each other and make sure that you have the right alternatives most of the time they will come up with something that's more brilliant than the project team does because they are not on the level because they cannot do anything they are very small people most of the time and they will come up with a blended or hybrid alternative you will go back and maybe the qualitative analysis then you go back and say this is the frame, these are my alternatives and this is the evaluation we think this is the best decision you should make if you make another decision, this is what you expect to get out and they have all the ingredients for them to make a high quality decision and then the implementation plan is made this proves that it will be implemented and so this is the way how we structure it sometimes people say getting all the decision makers in at least three meetings because these are the three main meetings and a framework alternative decision meeting this may be sometimes hard then I say okay then try to combine the first two elements and then I have two meetings so one where everybody knows you're not going to make a decision a final decision and just talking about what's the scope and what are the options that we have people will be different in such a meeting than that they know that it will be a final decision being made so in the first meeting we can really focus on having all the brilliant and great ideas on the table and all the total space is then spent to say you know what you can choose from then you start working on let's make an evaluation of that bring it back and then they have a decision they can make a decision based on the facts that are on the table you know a couple of minutes left so I think it's a good time to stop here I'm not saying that you should stop but out of respect for all of our participants that we promised that it's fine and the only slides that I have left people can also look at that from the one slide on what are the questions for framing what are the things that you think about alternatives and what about the evaluation story is clear I think for this I hope and it doesn't really bring anything too deep in this okay then I give room to two more questions the first Staj and Hans are you still on? Yes can show some kind of risk appetite be a part in this model in this decision process and I'm not only talking about persons but also the risk appetite for some as well wants to take wants to have Can you repeat the first part for me Gertjian? Can so the question of Hans is can the risk appetite be a part in the model Yes sure so whether the risk appetite comes in is in the value metric and how you of course so with information there you show what the uncertainties are and you bring them to life for the different alternatives and then for example if it's risk on money spending for example then the value matrix will be the cost or how do you say the value that it will generate but then in the evolutions so to say so in the logical reasoning that will be based on not only what's the highest value but also what's the risk your preferences will of course play a very important role so do you really want to take risks or do you say no we want to be on the safe side here so that will also come back yes Thank you Okay thank you very thank you Hans and there's a final question from Dirk Gardener Dirk go ahead Yes Kuno you said that there should be a balance between ratio and emotion is that balance equal in all the decision steps it depends on the decision I would say so what you try to do is to do sometimes people in organizations can get very emotional after you have a first discussion on something that they really don't like try to kind of get the balance a little bit more by also showing them the rational part of it more personal decisions of course and then if I look at the more important decisions that I made in my life thinking about getting children, buying a house getting married all these kind of things I think there should be a very good balance there and then I hope also that what you see is at least what I've seen in a company is that also tough decisions I think people are very quite emotional if you approach it in this way it will get more balanced because people will if you take the time they will understand the reasoning behind it and they will understand the facts and they will understand so they will also help them to give it a place that maybe it's not the thing that they wanted in the front to say and they were emotionally very against that they still understand why we're heading a certain direction and not balance so I give you one of the examples from Martin van der Brink who is our CTO he's one of the most brilliant guys I ever met and I first encountered with him 9 years ago and then he said what are you doing in a company and I said what my role was I was heading the decision support department and he said do you think that we're not making good decisions here then so this is nice standing on the 20th floor of the building looking at the campus of ASML I said now if you look at it then still the outcome looks very nice because I think we made a lot of progress as a company but I think we can always do better and this guy has a very good how do you say it he really knows one of course he's analytically also very very strong but he also has a very good feeling about a lot of things and I say what I want to do is maybe 99 out of 100 times my analysis will be the same as your thoughts about it but the one time that is not aligned I want to have the discussion with you because then I want to understand what's wrong in my reasoning and what is wrong in my rational approach or is there something wrong in the assumptions that he made and that's something that he accepted he said this is going to help me also because the other thing is that if you have an imbalance you also have a very good story to explain the people why the decision has been made because you can tell them you look at these different options you chose this one because it has the highest value and you can explain why you're going into a direction so there is a story it's not just we decided this and please go do it no there is a story so Kuno we're running out of time here sorry let me stop don't worry but because there is one I think a nice question that follows up on the previous one I will mention it myself from Suje he said as a follow up to the question how can you measure or quantify emotions can you finalize your inspirational session with that and answer on that I think we need a sixth lecture 7th quantify emotions yeah at least you can I mean you have to be prepared if you really want to make tough decisions you have to be prepared with people and understand how they are looking at it really quantifying it I think is hard but of course you have to take all the maps you can think about it you can look at how people normally react quantifying I think is I find it difficult to say how can you do that the thing that I always do is that I feel up to making a very important decision to try to talk to one on one with the key stakeholders not to arrange it not to make sure you go in the right action but to understand where they're coming from and where are the things that will that maybe make them emotional also to address them in the meeting that they also understand where people are looking at what is really important for them that's I think the most close I can I can do it okay thank you very much thank you very much thank you very much for your very nice and inspirational session tonight our last session the last one of six knowledge sessions let me thank you very much again we do get quite some thanks through the chat thank you very much everybody for joining the sessions please don't forget to donate to the university if you liked this session or any of the other sessions because that was one of the purposes next to sharing all of the you know the most recent knowledge that was developed at Tilbury University and with that I would like to wish everybody a very nice holidays nice summer time and hope to see you back again either at Tilbury University or anywhere online thank you very much and hope to see you soon bye bye now


Hey, this is Brett, and this 2020 Ford Transit 250 extended-length 148-inch wheelbase is stock number 13371Z. I am here at Summit Automotive in Fond du Lac, Wisconsin, your new and used cargo van headquarters. This 2020 Ford Transit 250 has the 3.5-liter V6 engine. It puts out 275 horsepower. It's paired up with the 10-speed automatic transmission. This van has been fully-safetyed and inspected by our service shop, has a fresh oil and filter change. All the fluids have been checked and topped off, and this van is 100% ready to go. I'm going to go all the way around in this video. Inside, start it up, take a look under the hood, take a look at the back, show you how all the doors work, and give you the most accurate representation of the vehicle as possible. Oxford White is the color, and I shoot all my videos in 4K. So if you have HD capabilities, turn them on now because it is your best way to check out the quality condition options, and of course, cleanliness of the vehicle before seeing it in person. And as far as cargo vans go, this thing is in fantastic condition, lots of signage area on the side of the van to put your business name and everything, and the sides are very clean. I didn't see very many dents or anything like that. And if you like the video, you can subscribe to my YouTube channel, which is youtube.com slash Summit Auto. Click the bell notifications, you'll get updates on the videos I do each and every day. The hood comes with these steel rims, and it has continental van contact AS tires, these are 235, 65 R16s, and they have right around half the tread left in the front, maybe a little bit more. Front fender is in excellent condition, headlight lenses are nice and clear, the front bumper is in great shape, as is the grille, I didn't see any major dents, dings, cracks, or scuffs on there. The hood is in nice condition as well. Passenger side front fender looks good, and the passenger side front rim, no scuffs or scrapes. As you go down the passenger side here, you can see it's just as nice as that driver's side, so it does have these side swing open doors. Take note of how clean that body is, how reflective and mirror-like the paint is. I take these HD videos so if you are far away or even if you're close by and you just cannot make the trip down, but you're still interested in purchasing the vehicle, you can see the vehicle, hear the vehicle, and have confidence in the vehicle you're looking at before you even get here, so that you can make a smart and informed buying decision from wherever you're at. If you're looking at this van, you're probably a business owner, want to make sure that it's going to be a good fit for your business. Back tires are brand new, and the frame and underbody is in really nice condition. I didn't see any corrosion, very clean all the way underneath. Looks really good. I'll show you these side doors here. So they open up actually quite a ways. So they open up that far, which is pretty sweet. Well, that one does. This one opens up about the same, so you can make it really easy to get in there. There is a metal partition with a pass-through door, then they've got carpeting for sound deadening on there, so that's really nice as well. Has a nice rubber mat back here, and the back area is very nice and clean. Looks really good. So these doors, you got to close this one first. That shuts nice and smoothly and solidly. Coming around to the back, the rear bumper is in nice shape, and the rear doors are in nice shape as well. You do get the glass on the back doors. These open up like so, and you can open them up all the way. You can unlock this, and they'll actually swing all the way open, which makes it really convenient if you're putting pallets or just generally putting stuff in the back. And the other side does that as well. But lots of storage area, and once again, very nice and clean. They do have these LED lights on the ceiling, and those are all working nicely as well. Alright, we'll check out the cab. Start it up, and take a look under the hood, and you can see how nicely that just shuts there. For full disclosure, this back rim is in nice shape as well, and that back tire is brand new too. Inside, you get the... I actually really like the black vinyl interior on these. I think it looks really good. No rips or tears on there. You get the heavy duty rubber floors. This one has auto headlamps, power mirrors, power windows, power locks. We'll hop inside, check out the miles, the radio. Everything that this van has to offer on the interior. You can see this one has 37,131 miles on it. You get the digital speedometer on there, outside temperature display, and instrument cluster is very nice and clean. There it is, 37,131. You get the multifunction steering wheel, cruise controls, audio controls on the left, Bluetooth, and information center controls on the right. You get AM FM and AM FM on this radio, as well as Bluetooth capabilities. There's your stability control. You have your different drive modes. So you have Eco, Slippery, Toe Hall, and Normal. Climate controls there. And then this is your 10-speed automatic transmission shifter. You get a USB hookup right there, passenger side, floor mat, and seat are in excellent condition. I don't think this van's ever been smoked in. And it's got that nice carpeting there for sound deadening. And headliners in really nice shape as well. All right, we'll start it up and take a look under the hood. Starts right up. No check engine lights or anything like that. I would personally like to thank you for checking out the video today. And hopefully from this HD video, you've been able to verify the quality condition options and cleanliness of this van all the way around. There's your tire and loading information. And there's your VIN sticker. Has the capitalist fuel fill as well, which is a nice thing. And once again, hopefully from this video, you've been able to tell just how nice this van is all the way around inside and out. Under the hood, we have the 3.5-liter V6 engine, 275 horsepower engine base. Very clean, runs very smooth. And once again, this van has been fully safety and inspected by our service shop, has a fresh oil and filter change. All the fluids have been checked and topped off. And this van is 100% ready to go. There's your emissions sticker. And I would highly recommend this van from a quality and condition standpoint. I've seen a lot of vans and usually in this miles, they're pretty rough shape already. This one's in fantastic shape. I would have no problems shipping this one to California, Texas, New York, Florida, wherever. Whoever's gonna get it is gonna be very happy with it. And to see more pictures of this van or one of our other 550 new and used cars, trucks, SCVs, minivans, Wranglers, cargo vans. You name it, we gotta go to that website right there. Summitauto.com. Full pictures and descriptions of every single vehicle from two locations. All at Summitauto.com. And if you'd like to check out more HD videos, you can go to YouTube.com slash Summit Auto. Remember to like, subscribe, and share on this video and all the videos that you see there. Click the bell notifications. You'll get updates on the videos I do each and every day here at Summit Automotive. In fact, in a second, you will see a link to subscribe to my YouTube channel on the upper left to link to more transit videos that I've done in the upper right. A link to this vehicle on our website and lower left and a link to one of our latest YouTube videos on the lower right. Click those, check us out. And we're super excited to help you with this ultra clean 2020 Ford Transit 250 extended wheelbase, 148 inch wheelbase in Oxford white. Thanks again for checking out the video. Remember to like, subscribe, and share on the YouTube channel. I really appreciate it. Thanks again.


Dendritic computation in memory and integration for a good while and she'll talk to us about Dendritic contributions to complex functions insights from computational models. Over to you. It's a great pleasure to be here. I'm looking forward to a lot of exchanges with people from the experimental and the machine learning field and what I want to talk to you about today is our work on looking at how Dendrites contribute to information processing and memory formation in different brain areas. So as I mentioned already, what is it? Why is it processing? Okay, we are mostly interested in understanding Dendrites, these thin processes that extend from the cell bodies of neurons and we want to understand using computational models how these structures may be contributing to different learning and memory functions like special learning, contextual learning, fear learning, working memory and so on and so forth. We've recently started looking into also the role of Dendrites in visual processing and the role of interneuron Dendrites in various kinds of computations. What we're trying to find out here is whether there is a single function or attribute of Dendrites that is common across different brain areas and different cell types. So how do we do that? We do that using purely computational approaches and I want to give you a few examples of the models that we have been developing in our lab over the past 10 or so years. So we have single neuron computational models which are very detailed biophysical models that incorporate realistic morphologies of neurons and what you see here is a layer 5 pyramidal neural of the prefrontal cortex. We also have similar models for the CA-1 area of the hippocampus. This is a model that I developed when I just started my own lab in CREED in collaboration with Barlett-Mell. We have models of the VA-1 area layer 2-3 V1 pyramidal neurons or we also have amygdala single neuron models and more recently we started looking into interneurons and what you see here are single neuron models of fuzz spiking basket cells. All of these models have a very large complexity in terms of the biophysical properties that they incorporate which are all these various ion channels. All of them are described by pairs of differential equations so there are a lot of parameters to fit and this is pretty much how this biophysical modeling work. So essentially you have a detailed morphology of a neuron which is then described by a series of electrical circuits. Each of these electrical circuits can be approximated with a typical cable theory let's say equations where you have the active conductances, the batteries, the capacitance representing the membrane and so on and so forth. If you have to stay closer to the mic because the people in the back can't hear you. Really? Okay, sorry. Closer to the mic than it is. Okay so we have all these complicated, let's say, mathematics in the background of these beautiful cell models and as a result there is a lot of parameters that one needs to tune in order to get such models. And as Iván Sholdel mentioned just a second ago there are new neuroinformatics tools coming out right now for doing multiple types of optimization to find these parameters. I have to admit that in my lab we don't really use those tools and I'll tell you why because I think that it is really important for a student especially to be able to understand when they play with this and the other parameter what does it mean that this parameter will cause to the network. So as a graduate student I also experience that doing this over and over and over again it lets you gain a very deep understanding of how each of the ionic mechanisms that you simulate contributes to the activity of the pyramidal cell. So for purely training purposes I ask my students to manually tune their models and once they've done that they can go into any online tool to you know end up with a final set of parameters. So these are examples of single cell models and we have a lot of microcircuit models in the lab as well. This is a micro circuit model of a few detailed biophysical neurons connected together in the prefrontal cortex and this is how it looks like when you simulate this in neuron. So you have the depolarization gosh sorry about this it's advancing on its own. Okay so when the color changes here it means that the cell goes from depolarized in purple to from hyperpolarized in purple to depolarized in yellow and it fires a spike when you see a yellow color and we also have micro circuit models where we eliminate this complexity of the morphology and make them slightly simpler and in these cases you even have more parameters to fit because now instead of looking at just one cell you have multiple. Still nothing close to what Ivan presented like a real size CO1 network. This is 10 or 20 neurons in this micro circuit and other examples of micro circuit included then the gyros network model that we recently developed in collaboration with Attila Lozonsky to see the contribution of MOSI cells in spatial learning and the CO1 network model very simplified one again to assess how spatial learning is influenced by VIP interneurons. So these are some of our simplified micro circuit models and they're simplified in the sense that they can also incorporate instead of detailed mathematical descriptions some simpler descriptions of integrate and fire kind of neurons where you can also extend them by adding dendrites and dendrites can also be described by integrate and fire type of equations. And finally we have this larger scale network models that are in the order of 500 cells or so where we also incorporate a large number of plasticity mechanisms and I will talk about this network model in a while and the idea here is to be able to come up with something that is capable of learning that different type of memories as opposed to just looking at information processing. So this is an overview of the type of modeling tools that we have been developing in the lab and there are many more in addition to those and I just want to highlight a set of recent studies where we use these kind of models to infer the functional properties of the network that we're interested in. So I'm going to focus on what we have learned over the years from these kind of models and I just want to start by the very old work that I did in collaboration with Bartlett which was pioneering at the time which we used a detailed C1 parameter cell model essentially to study how dendrites contribute to information. And in this early study what we found that was very interesting at the time was that dendrites of these neurons integrate information in a super linear manner so if input arrived within the same branch then they some made in a super linear manner which is the red line shown here whereas if the same inputs are distributed in two branches then they add up linearly which is shown by the green line here. So this prediction at the time was let's say revolutionary and it steered the interest of the community and it was later verified experimentally both in cortical neurons this is the neocortex layer 5 pyramidal neuron done by Jackie Chiller's lab where they found the same superlinearity in basal dendrites and this is the C1 pyramidal neuron oblique dendrites same as the model done by the lab of Jeff McGee. So the interesting thing that came out of this model is that we suggested something new the experimentalists got interested and looked into it and they came up with a verification thankfully of our prediction. Based on this early work we suggested that pyramidal neurons integrate as multi-stage non-linear integrators which means that they have one level of processing that takes place in the dendrite and in this case it's a sigmoidal let's say subunic that is present in pyramidal neurons and as a result of this finding we suggested that these cells act as two-stage artificial neuronal networks. So you have one level of processing in the dendrites and another level of processing at the same cell body both of them are highly non-linear okay and now what we believe about pyramidal neurons it's been a while since then is that they act as multi-stage integrators so they have non-linearities in their dendrites they can have non-linearities also in their tuft of basal trees but the take-home message is that these cells are doing much more than just summator inputs and processing them okay so we now are happy with that interpretation of what pyramidal neurons do. The question I wanted to tackle in this talk is what happens to interneurons okay there are recent data coming from several labs that suggests that the dendrites of interneurons also exhibit non-linearity similar to those of pyramidal cells for example in stratum radiadum interneuros in the ce1 we don't know the exact type people have reported this jump the supra linear jump in response to increasing strength increasing number of synapses activated within a dendrite which you can see here and here and this non-linear jump is dependent on the nmda receptor because if you block it it disappears so this is in ce1 stratum radiadum interneurons also in cerebellar stellate cells the lab of david degree goryo showed that you can have sub-linear dendritic summation of epsp so the voltage summates sub-linearly within the dendrites of the cells but at the same time if you look at the calcium concentration then the response is highly supra linear in the same dendrites so we have some interesting dynamics taking place in the dendrites of interneurons and also in fast basket in fast biking basket cells which is one of the most dominant classes of interneurons in the cortex and the hippocampus that have also been suggested to have supra linear dendrites which you can see here during sharpwave ripples in fact in the ce3 this is work from the lab of balash roxa so there are all these data not that many admittedly but a lot of data suggesting that the dendrites of interneurons may also be doing some interesting non-linear computation yet still the underlying dogma about how these neurons process information to most of us at least is the point neuron dogma which means that we think of interneurons as having dendrites but not actually utilizing those dendrites so they're there to collect the input and then we have a spiking non-linearity at the cell body which is what is representing a point neuron so alexandra zilivagi a brilliant undergraduate master students in my lab wanted to ask the question of how do interneuron dendrites and particularly fast biking basket cells integrate inputs so to do that she used eight different morphologies of these neurons five from the hippocampus and three from the prefrontal cortex and came up with detailed biophysical models of these cells which are validated against the large number of experimental data that I don't have time to show but this work is on bio-archive so trust me it's a good model and we can talk about it afterwards so once she came up with a model she wanted to ask how do the dendrites of these cells integrate information and to her surprise she found that the dendrites of these cells have two distinct modes of integration so there are some dendrites that integrate their inputs in a sub-linear manner which are shown in purple here which is what we would expect based on the status of others but there are also some dendrites that exhibit the dridic non-linearity which is a generation of sodium spikes in this case and therefore they have super linear integration and you can see them here in two of the representative morphologies right so I have two modes of the dridic integration which is cool so what is the reason what is the main biophysical mechanism that underlies this type of integration well the obvious one the sodium spike so if she blocks the sodium channels in the dendrites in these super linear dendrites then their integration properties become purely sub-linear much like the other types of dendrites we have in these models cells which you may say it's obvious but why then have two types of the dridic non-linearities why aren't they all of them super linear or all of them sub-linear is this anything special apart from the conductance of the sodium channels and I should also mention that the conductance of the sodium channels is very low in these dendrites it's about 10 times lower than that of the cell body and it's according to experimental data so it's not that we have a lot of sodium in these cells we have a realistic amount of sodium so what else in addition to sodium determines the difference between these two type of integration modes so then we turn to morphology and what we found was that if you look at the length of the super linear versus the sub-linear dendrites in the hippocampus then there is some statistical difference if you look at the diameter then this difference is much higher if you look at the length and the diameter in the prefrontal cortex then you don't see the same thing here the diameter is about the same but the length is more significantly different so there seem to be different morphological rules that underlie this difference between the two integration modes in the two areas that we looked at in both of them however if you look at the volume which essentially is a combination of the length and diameter then this feature is statistically different between the two classes the volume and as a result the input resistance between the two branches is statistically different okay so we have in conclusion some kind of morphological determinants of these two type of integration modes which essentially is the volume the combination of length and diameter but is this difference causal so to answer this question we went on and since we have models we can causally manipulate the morphology in our models and what we did is essentially to look at the distribution of the two type of dendrites in our control condition and then go ahead and change their length and diameter so as to make them all look like super linear dendrites or all of them look like sub linear dendrites so what do we get this is a distribution in control conditions so you say you can see that you have about half super linear and half sub linear dendrites if you turn them all into super linear looking dendrites you have a change in this distribution which is substantial so more or less all of them become super linear if you do the opposite you again have a change so you have more sub linear dendrites but not all of them so what we can conclude from these manipulation experiments is that volume or the length and the diameter of the dendrites has a causal effect on the integration mode but it's not the only one right because if it was the only one then these two distributions would be a hundred percent changed so morphology plays an important role so we have sodium conductances and morphology as the key determinants of the two integration modes okay so then what we have two types of dendrites in fast piking intern neurons what does this mean for the way they process information can we learn anything new from what we know before so what we did next is to assess how the cells respond to synaptic input depending on how we arrange this input on to their dendrites since we have two types of non-linearities we expect to see differences in the responses of the cells so we did the experiment whereby we arranged synapses in a distributed manner so covering a lot of their dendrites or clustering them within a few dendrites for both the hippocampus and the prefrontal cortex and looked at the mean firing grade of the cells and this is just one of the representative morphologies and we found that across all of the eight morphologies that we simulated these cells preferred the dispersed arrangement so they had a higher firing grade when synapses were randomly located into their branches rather when they were clustered in a few dendrites this is the opposite from what we know about pyramidal cells pyramidal cells prefer to get clustered inputs because it's easier for them to fire dendritic spikes and increase the firing at the cell body okay so this is unintuitive let's say given the fact that we have supra-linear dendrites in these cells so why do these interneuron cells prefer a dispersed arrangement to answer this question which was indeed puzzling to us and the reviewers we had to go into the literature we did a lot of reading and we found some interesting information that mentioned the following facts first these cells do not have a lot of an NMDA receptors their non-linearities come from sodium spikes so these non-linearities are really fast they don't capitalize on the temporal integration properties of NMDA receptors then the dendrites of these cells are primarily thin the diameter is very thin the input resistance is high coupled to thin diameters which means that if you induce a fast depolarizing effect then a fast depolarizing EPSP then this EPSP that doesn't some may temporarily is very likely to activate hyperpolarizing conductances which brings me to the third fact these branches and the disinterneurons have a lot of A type potassium channel so if you have a strongly depolarization that is fast rising it will also activate the potassium channels very strongly so to see whether this hypothesis that was proposed theoretically in the literature is true we went into our model and essentially closed all the A type potassium channels and increased the diameter of the dendrites to 2 microns to counter to changes let's say effect and what we found is that these manipulations made the response into two types of arrangements identical so there was no longer a preference to a dispersed arrangement so from this the take home message is that yes it is highly likely that the morphological properties and the biophysical properties of these cells make them prefer a dispersed arrangement opposite to the pyramidal neurons so we have this information the question that comes naturally next is what is a good reduction of a fast piking interneuron that it looks like a pyramidal in the sense that he has super linear and sublinear dendrites let's say but he doesn't like clustered inputs so how do I describe the cell so to answer this question we took all of the aid biophysical models and try to approximate their firing rates using abstractions that are described as artificial neuronal networks this is the point neuron hypothesis that we've known so far so some making our inputs and passing them through some kind of linearity or super linearity at the cell body and this is a modular artificial neural network where we separated two layers into dendrites that have super linear integration properties and dendrites that have sublinear integration properties and these are essentially hidden layer neurons with these features and then once we have these two types of hidden layer neurons we pass them through the somatic nonlinearity and we tried a lot of different configuration and transfer functions for these cells in order to come up with the ones that better match the experimental data so what we found was that these two layer modular an n explains the firing rate of these cells to a very large set of different inputs much better than the linear an n which would be the point neuron right so this is the two layer modular an n for the hippocampus and the prefrontal cortex in blue this is the linear an n which as you can see is not as good but it's not terrible I mean the correlation coefficient here is 0.75 0.85 compared to 0.96 and 95 in the hippocampus and the prefrontal cortex respectively so the linear point neuron assumption is not terrible why is that the case given that we know that dendrites of these cells are not linear oh sorry I forgot one slide then I'll come back to this question again so the first thing we wanted to find out is whether both types of these nonlinearities are necessary or just one will be sufficient because UE essentially increased the flexibility of the network so we looked at similar such an n models which could be only with sublinear hidden neurons or only with supra linear hidden neurons and the response was to this question was that yes they are this is the supra linear an n this is the sublinear an n and this is the modular that has both and in all of the morphologies we tested the modular did a slightly better job okay so I'm coming back to the question as to what is it that these other models explain well but the modular an n is ideal so we have two types of variabilities in the data sets that we create we have a variability that is generated because of the number of synapses that are activated and this is a kind of variability that is easy to detect by very simple models so if I stimulate the cell with five synapses or I stimulate the cell with 50 synapses then I expect that the firing rate will be much higher for 50 than for 5 so this is a kind of variability that a simple linear model would be good at detecting and then we have a type of variability that is very challenging for a linear model and this is where I just change the arrangement of synapses but not the number so the power the strength of the input is the same but where they are placed changes and this is a kind of nonlinearity that a linear model should not be able to detect because the location here could be from a supra linear to a sub linear dendrite and a linear model does not have that information so to understand whether this is what the modular network is explaining better what we did is to look at how well we can explain sets of equal power so we only have 20 synapses and we change the allocation 40 synapses we change their location 60 synapses we change their location and we look at how good these models do for these different datasets and then we can see that the too much the too hidden layer network model is doing a superior job at this particular task this is the performance of this model compared to everything else and look at how poorly the linear model is doing in this case okay so now we have a detailed biophysical model a reduction of it into a two-stage a nm that is a better description of the processing of the cell in terms of mean firing rates but then the important question to ask is what is the functional consequence of having this type of intern neurons do they contribute something to information processing to learning and memory at the circuit level so to answer this question we built a computational network model of let's say the hippocampus it's a generic model so it doesn't matter it's a model that is capable of forming associative memories most of the data that was used are based on the hippocampus and this is a published model that was published in 2016 so we took this micro circuit model and we extended it by incorporating fast biking interneurons with the two types of dendrites sorry with the two types of dendrites so sigmoidal dendrites and sublinear dendrites and also the dritik targeting interneurons so some interneurons with dendrites but not so fancy properties the pyramidal neurons in this model are all equipped with supra linear dendrites based on our initial work so we have this micro circuit model and then we train it to learn multiple multiple memories and we ask what is the contribution of interneurons into this process so how do we learn different memories essentially by incorporating various plasticity rules so this model has four types of plasticity rules the classical l t p l t d that depends on protein synthesis the protein synthesis can be somatic or the dritik or both this was published in 2016 it has homeostatic plasticity so synaptic scaling in the entire network plasticity of neuronal excitability which essentially a change in the somatic excitability after learning as well as axonal rewiring so synapses are allowed to touch and remove the dendrites of the cells until they find suitable partners and we trained this network model to form one memory by presenting a memory as a random poisson spike train that lasts for about one second repeatedly to the network and letting the plasticity rules operate and after the next day after 24 hours we present to the network a small part of the memory and we look at the activity of the neurons and if the same neurons light up that were activated during learning or are a big proportion of them then we consider that the memory has been learned and we end up with a microcircuit so a small portion of the network that encodes each one of the memories that we learn okay so what do these two-stage interneurons do for memory encoding in their network model to answer this question we look at the properties of a given memory one of the properties is the size of the n-gram so typically a size of an n-gram is between 20 and 30 percent of the neurons in the network as reported by alzino silva and we look at the size of the n-gram when we have linear dendrites or non-linear non-linear is both supra and sap in this case and we find that the size of the n-gram is reduced when we have non-linear dendrites so we have smaller n-grams we have n-grams with lower firing rates this is the non-linear and the linear model and we have n-grams that are sparser which means that the activity of the population is lower in the networks is higher sparsity lower activity so all of these findings essentially what they tell us is that two-stage interneurons bring resource savings so we don't see a major change in the way we encode the memory we just see that we encode the memory with using fewer resources fewer neurons lower firing rates sparser networks okay so this is the advantage that an interneuron with non-linear dendrites brings to the circuit so what about more complex functions like interaction of memories a few years ago the lab of alcino-silva presented a very cool study where they showed that if you learn memories in a sequence separated by a few hours then these memories interact with one another and the interaction is because they are stored in the same population of neurons so if you learn memories separated by five hours then the overlap between the two memories is in the order of about 20 percent whereas if you separate them by 24 hours or seven days there is no overlapping the population that encodes the two memories okay so there was a suggestion that the cellular principle with which we link memories over time is by directing them into the same population of neurons so you want we wanted to ask whether non-linear interneurons contribute something to memory linking so we did the same experiment into our model where we presented two memories separated by different time intervals and looked at the overlap of neurons that encoded these memories the experimental data report around 15 to 20 percent overlap and what we find is that if you separate the two memories by one hour then the non-linear model has a similar overlap nearly 20 percent whereas the linear model has a much higher overlap and if you separate these memories by 24 hours then there is virtually no overlap in any of the cases so the take-home message from here is that if you have non-linear interneurons then the interaction between memories is achieved by more physiological lower amount of overlaps which is also a way for avoiding confusing the two memories with one another so to conclude what i presented to you today is a list of predictions that came out from multiple models of fast-piking interneurons and the first prediction is that we have two major modes of the duty integration that coexist in these cells and so far there is no experiment that has demonstrated this we're waiting for someone to do it we find that the morphological features and the sodium spikes are gating supra-linearity in the dendrites of these cells we find that these cells are model tells us not that we find this is all predictions that are contrary to pyramidal neurons interneurons prefer the scatter arrangement of inputs rather than a clustered ones as a result of these features these cells are better represented by two-stage ANNs with the two sub-population of dendrites well incorporated in these models this two-stage integration provides resource savings with respect to the encoding of a single memory and the binding of at least two memories over time and this work comes with a set of tools that include detailed biophysical models reduced single cell models and artificial neuronal network models as well as a large-scale circuit model all of which are freely available to the community and we hope that people will find them interesting and use them and with that i'd like to thank you all for your attention including all of the people that did the work and i just highlighted the work of george and alexandra in this presentation our collaborators and all of our funders for their generous support and of course you for your attention thank you i hope i didn't go over time can i request you to use your uh the mic sitting in front of you as the questions answers would be recorded so no questions yeah i've gone hey uh so i mean i think one of the things that i find interesting about some of these models and there was a similar paper out from maté langil and uh tego branco about this as well is that although there's obviously some components that you need the dendritic non-linearities to explain at the end of the day there's a surprising amount of variance that you're explaining with a purely linear model as well and so to what extent do you think that the sort of non-linearities are effectively kind of adding details to what's going on as opposed to the kind of core computations that are occurring in the circuit and and to what extent can you maybe capture a lot of the core computations that are occurring even in the absence of those non-linearities you know like say with the n-grams you you might not have a realistically sparse level of n-grams but you're still getting a similar kind of principle of n-gram formation for example um so i'm kind of curious about your thoughts on that that's a very good question i also get that all the time for pyrrhamdal neuron uh non-linearities so i think that in the brain the reason for having this kind of non-linearities is essentially uh to optimize let's say storage because of our size of whatever constraints energy and so on and so forth so from what i've shown today as well there's nothing that you actually miss in terms of the critical computations by thinking of these cells as point neurons but having said that maybe we haven't looked into all the kind of computations that are required or that our brain is actually doing in order to find this missing link so i would i would not like to say that linear is okay i mean it's okay for some aspects of what we're interested in and some of the questions we're asking but maybe it's not okay for others so yes yeah great talk very very interesting stuff so the um my question is really just to think it a little bit further um so as you know there are now real really clear data that show uh that there is a heterogeneity in for example the principal cells so you look at ca1 now ca3 is clearly a heterogeneous the pyramidal cells and critically the ca1 pyramidal cells for example are contacting the basket cells very differentially depending on their subtype so for example if you are a pyramidal cell in the ca1 and you project to the medial prefrontal cortex you are much more likely to innovate the fast packing cells than if you are neighboring pyramidal cell that projects to the amygdala we only know though about the differences in the numbers of these connections from the excitatory cells to the interneurons but i wonder could you take what we know about the behavior of these pathways um and uh using your modeling approaches and to figure out whether there is to make a prediction whether in addition to the differences in the connectivity numbers there is also a differential distribution for example of how the pyramidal cells innovate the fast packing baskets yes that's a that's a great question i wish there were data out there to help us out but there are none to my knowledge but you could make predictions yes you could certainly make predictions and actually i haven't shown anything but we we have i mean depending on whether the inputs land or do sublinear let's say versus supra linear dendrites then you will have a different response at the summer and you may be able to play with these two modes of integration depending on you know the population let's say of inputs that come in and this would essentially allow the cell to operate under different regimes so if we have the most of the sublinear dendrites being engaged with distributed inputs then you more easily go into the gamma range of fighting that these cells are optimized for whereas if you have most of the supra linear dendrites being activated with clustered inputs then you have lower firing rates and more targeted outputs so it allows the cells to exhibit two different let's say operation modes depending on the inputs yes so that's a very good point we should probably incorporate this in the study as well i just wanted just add i guess a comment just multiplexed computation is in other way perhaps of saying that and one of the things that we showed is actually you can do sort of linear computation at the same time as doing computation with sort of scato versus cluster of synaptic locations so yeah that's true yes yes excellent just wanted a bit more clarity on on the last part of the overlap of patterns so why what was changing in the model to get less overlap as a function of time nothing we just started the non-linear dendrites in the two-stage intern neurons in one case they were so it's just a comparison of those two sorry i missed that yeah so so this is when the dendrites are linear purely linear and this is where the dendrites are non-linear meaning the two modes and if you have no linear dendrites you have a smaller overlap than if you have linear dendrites okay yeah that's it we haven't changed anything