 Okay, so now I will present you the software and its architecture. I will start with some general slides. So, TVB is a free open source. The source code is available on GitHub. It is entirely written in Python. You can either use it on your local laptop, on a web browser for the graphical interface, or even on a high performance cluster. So, TVB is quite a large effort. The framework development started in ten years ago. The first public release was published in 2012. From a collaboration, it became a large software project. Since then, we improved it and developed it with a new model. For example, since 2014, there's the Epileptome model that is available and we're still using. We have also external collaborations. So, we are participating in the Google Summer of Code since 2014. But as you see, TVB is also available in the HVP Collaboratory. The latest release that you all installed, I think, was released last May. So, you can either get your copy of TVB on the official website, but you can also use it directly from the HVP Collaboratory or, as I say, the Google Collabor. It comes with two main components. You have the full TVB framework that you get from the website, and you have also the TVB library that is the Lightweight Python Package that you get from the Public Repository GitHub. So, a TVB library, you can use it on its own. But for the TVB framework, it depends completely on the library, but it offers a graphical user interface but also a database management. So, as I say, the TVB library is a standalone package. It contains all the necessary to perform the simulations. It is responsible for the scientific computations related to the brain models and also for the data. It comes also with a data type. So, it's similar to the usual data type. And some analyzers. So, we have a set of classic time series and network analysis. So, if we are coming back to the TVB framework, this is just an overview of its architecture. So, it provides two interfaces. One is the graphical user interface on the web browser, or you have the console interface. So, of course, you are more constrained with the graphical user interface as it depends on the latest release. With the code, you can add your own model, modify the code. It's more... I don't have the word. Yes. Two other important components are the adapters and the storage. So, the adapters work like the plugins. So, you know which type of input you have to provide and which type of data type will be the output. And for the uploader, that is also really important because you can bring your own data that you preprocess with other tools. Okay, so... See? So, if you have everybody download the TVB distribution on the website, because now we will open the graphical interface and I will show you the different components inside it. No. No, yeah. Okay. So, normally when you download TVB, you have all these folders and the most important is the bin where you can start the graphical interface but also the Python notebook. So, for the graphical interface, you just have to click here and launch the TVB start. Okay. So, this is the graphical user interface and here you have... Oh, okay. It's not here. Okay, so the graphical user interface has six main modules. The first one is user where you can manage the setting of your account but also of the software. So, one thing that you will do this afternoon is certainly to change the capacity of the software and the storage. You have the project module where you can create, import, manage your project and here is the sub-module. So, here you have the list of all your projects. Data structure, it's where you can see the different data inside its project. Import or created by the simulations. Operations. So, here you can see all the... what you have done inside TVB for the simulation, for applying some metrics, analysis. Basic property is only for editing your project and we have also a module where you can save the visualizations. Then, the main component of TVB is the simulator. So, the simulator has five main components. If you remember the equation of the large-scale brain network modeling, you have different components to bring inside. The third thing is the large-scale connectivity. So, the connector that you get from the MRI scan. To couple the region together, you have to select functions. You can modify the conduction speed. If you want to simulate on the surface level, you can import the cortex file. Then you have to import the stimulus. So, there is another module where you can create either your different stimulus. You have the local dynamic model that, for example, here you have a list of models, including the Gensen Ritz model you just have seen with Andreas and Paul, and the different parameters that you can modify, either for all the brain or also for a separate region. So, you can create an heterogeneous brain. So, to integrate your equation, we have several shames available. Either a deterministic or a stochastic to introduce the noise, and the output of the simulation, the monitor. So, either if you want to see at the source level, like the synaptic activities, the LFP of your model, or we have some forward modeling where you can see or simulate EEG, EEG, EEG and even fMRI bold signals. And here you have the length of your simulations. The left column is all the different simulations you have done, and on the right you can pre-define some visualizers. Then we have some typical analysis tools for time series at the network level. But just to remind TVB, it's for simulations. So, we are not providing all the already existing tools for analysis. So, you can export your data. So, having with TVB, you export in H5 format. And then you can apply your usual metric. Then you can apply a stimulus. So, either at the region level, so when you have your parcelations, you have a certain number of cortical and sub-cortical regions, or you apply at the surface level, on different vertex. And then you have the connectivity module. So, either you can modify your large-scale connectum so that you get from the parcelations, you can create the local connectivity for the surface simulations. And we have also the allen connectum builder where you can import tracer data from the allen website and create your connectum for the mouse. So, for example, in the connectivity, you will see it this afternoon in the end zone, but you have your connectum and you can apply different operations. You can create a lesion, so removing some nodes. You can modify the weights. You have also the track length matrices and different visualization tools. For the Python tool, we provide also, you can use the console interface and we provide the Python notebook that you see with Paul. It's where you can have visualized directly your data and had some comments. So, to launch it, you have the Python notebook command. Okay. So, here you arrive directly in the demo script folder where you can find different tutorials, region simulation at the surface, modeling resting state, epilepsy. And here you have some other example to use particular tools. So, what you do in the graphical interface, you can do exactly the same and more with the code, with the Python. So, as the same for the simulation, you need to select your model. So, here you have to select your large-scale connectivity, the coupling functions, the integration shame, the monitors, and then you can configure your simulations and run it. And then you have... Okay. And then you can visualize your simulations. Some important resources. So, we have a website where we can find all the documentation of TVB and again some tutorials and demonstrations either with the graphical user interface or with the Python notebook. On GitHub, you have the source code so you can see the different Python scripts. And you have also, if you have some problems or questions about how to deal with TVB or if you remark some errors in the code in the graphical user interface, we have a Google group. And it's pretty active. And it's whole.