Let's go to the 5th frontier. What I call the computer simulation of neuronal circuits or computer, computer or theoretical modeling of the brain. As I said in the beginning, my claim, the claim of many scientists, as it will be able to integrate data, all levels of data. And also, the anatomical and physiological data and set it on the standard system, we will need some theoretical tool, mathematical tool. And I'd like to site a, a very prominent scientist, a very, very prominent scientist, William Thomson or Lord Kelvin, a great physicist and mathematician. He said the following, I'm never content until I have constructed a mathematical model of what I'm studying. If I succeeded in making one, I understand. Otherwise, I do not. that's a very powerful claim, that understanding of complex systems, physical system or others, a complex system, biological system, requires mathematical formulation of the system. Not everybody agrees to that. Not always you need the mathematics to say I understand. But in terms of complex systems, I totally, absolutely, of course, agree with Lord Kelvin. And this is what brought us to the blue brain project. which is a project that I will discuss later on. But here, the idea was the following. As the idea was that we have so much data, we have a lot anatomical data, we have a lot of physiological data at the circuit level. How do we integrate it all together? What do we do in order to put this data, that we already have not complete? We need still to work very hard to get the whole data. We need to connect the whole brain. But what do we do? So, the idea was to take a large computer, here is the Blue-Gene Computer. It's blue, by the way, because IBM used to have blue colors as it's own color, today it's black. But still blue, so the term blue comes from the IBM machine that we are using. and blue brain has its all very good connotations, but it's also blue because of the IBM. And we're now using a very powerful IBM machine. It's now the EPFL Lausanne, in order to model, to simulate cells. But, what does it mean to model a cell? So, suppose somebody gives me a cell. This is a nerve cell. We'll talk a lot about the nerve cells because they are the unit. You give me a cell and you tell me that when you record from this cell, it has a particular pattern of firing. Let's say this is the case. So, this is the real recordings from this particular cell in isolation. So, this cell likes to do tu, tu, tu, tu, tu ,tu stop tu, tu, tu, tu, tu stop. We call it a burster. So, the type of cell that is called the burster because it bursts with spikes. I need to write mathematical models, and I'm not going to do it here because there would be a particular lesson to do the mathematics of spikes. But I want eventually, to write an equation or a set of equations, to describe this electrical activity. So, after writing these equations and you learn about these equations, you will see that I can replicate mathematically. I can replicate the activity or closely replicate the activity of this particular cell. This means that I have a mathematical model of this cell. And if I have another cell that fire differently, I need to write another equation. And so, I build different equations to describe different set types. This is what I mean, to model mathematically the cell. Then, I can start to assemble, to put the cells together and this is what we do. We take the supercomputer, each processor is now solving mathematical equation for this cell, and another processor for another cell. And then, we put them together, and also connects them together anatomically. And so, we need to model the connection too, not only the activity, but also the connection. And eventually, I have in the computer, a model, a simulation of the system that I want to simulate. It looks something like this. So, this is a real, a real graphical demonstration from the blue brain project of about 10,000 cells in a piece of about two cubic millimeter of cortex. In this case, of a rat. You see the cells modeled in the computer. You see all the wires. The axon and dendrites that we'll talk about. And so, this is a realistic replica. It's a realistic copy of the complexity of the anatomy of the cells in the computer. But this is just the anatomy. I need also to show the activity. That means that each cell has to really replicate its electrical activity that I just showed you before. This is just to show you the jungle in your brain, how complex a piece of a brain looks like. This is the blue brain project. So, let me show you the activity. So, you see the same piece of brain that you saw before. We call it cortical column. The same piece of brain, but now this piece of brain in the model is now active. So, in this case, you don't see the spikes, but each time it's red. It's color coded for spikes. So, red cell means that it fires a spike. Blue cell means that it did not fire a spike here. So, you can see that the network starts to, to act. This is a simulation of 10,000 cells which is a very small number relative to the top number. But it is a beginning. It is a beginning of a simulation of a whole brain. But why do I want to simulate a whole brain? Why do I want to do this modeling? As I said, I think that we will be able to understand the network. And when I say understand, I mean that when I see a particular activity, which is a result of many, many, many interacting elements, each one with its own music, so to speak. So, this is doing ta, ta, ta, this is doing ta,ta,ta, ta. Yes, everyone is doing another ta, ta, ta, ta. But eventually, the network as a whole is generating a whole behavior, a wave of activity, maybe a sleep activity, maybe activity related to Parkinson's. Maybe activity related to another disease. Maybe activity related to happiness, to emotions. How do these activity merges and what goes wrong when an activity becomes Parkinsonian activity? What goes wrong? I believe that this type of modeling technique, where you have a handle on each parameter, because you built it. And you can manipulate the parameter, so your computer will become sick with Parkinson. Your computer model will become sick with Alzheimer's. Then, I will be able to repair it in the computer. And then, come with ideas, sophisticated ideas, well-established ideas, based on what we did to develop medication or to manipulate this particular region or this particular cell. Maybe with optogenetics, maybe with other tools. So, this is what we call today, simulation based medicine and simulation based research. The research is based on a model that we built, on a computer that we built, the brain built computer, the brain built models, the brain built mathematics. In order to integrate an anatomical and physiological data to build a replica of a piece of a brain, in order to be able to eventually fix the replica and extend the replica. And then say, through this mathematical exercise to understand the brain. So, that's computer based simulation of the brain. And that, I think, we are going towards this, in our information age. So, thank you for the first lesson. This is the end of it. And in the next one, we'll go to study the ingredients. The next lesson will be about nerve cells, about the synapses, about the signals. You will go into the brain, into the brain and start to learn more and more about the ingredients of this system. Thank you very much.