 Zato sem tajnil neuroinformatijski v površenju, kako je vsefakulija fizika, ki sem ne boš površenj vsefakulji, neuroinformatijski vsefakulji, neko je to neko posledno, neko je zelo mennega, neko je zelo vsefakulji. Prvom sem da boš površenj vsefakulji. As a scientific activity, we are mainly involved in EG signal processing, modeling of brain circuits and BCI and assistive technologies. As far as signal processing is related, we do standard event related potentials, analysis, time frequency, analysis, event related synchronization and desynchronization, functional connectivity, and recently cross frequency coupling. As far as modeling is concerned, we do population level models for epilepsy and sleep spindles, and also modeling of brain circuits on the compartmental levels. And in this respect we have developed model for epilepsy, at least some versions of this, because there is really many, many different varieties of things called epilepsy, and SSVP and also high gamma oscillations. As for the BCI and assistive technologies, we worked on the main free paradigms, which are SSVP, P300 and ERDRS, that is motor imaginary. And with this background, we view neuroinformatics as the application to the neurosciences of methods for measurements, analysis and modeling derived from the physical sciences, as we are physicists from the origin. Now for the neuroinformatics education. Most often the scenario is, as it was already said, that people come from different fields, like biology, psychology, mathematics, informatics and physics, and they try to join this community at the level of PhD studies, usually. And when they join some laboratories and they get involved in real studies. And in our faculty it began, as I told with modeling, some signal analysis, so this is a picture from, let's say, a little bit more than 10 years ago. And then in 19200, 2009, there was a program from European Union called Human Capital Program, and we had some extra money to start developing the new Syriklum. And at that time, Piotr Durka was our boss, so that's why he is here. And what was this funding for? We were founded for creating Syriklum at the bachelor level, and there were some extra money also for building laboratories for students for recording and analysis of biosignals. And we have created also complete online scripts for students for the BC level. So if there would be some more time, we could go to this address and have a look. Unfortunately, so far it is still only in Polish, so maybe not very useful to the full community, but quite useful for our students. And one of the nice things that was already founded, at that time was that students had the opportunity to participate in research and also to go to conferences to meet real world researchers. So not only here in our institution, but also, so, for example, this is, these are some students from us at Neuroinformatics Congress in Monachium, Munich, and some of them also publish with us recently, so they started at that time and they are still working with us. Okay, so what do we teach at the first cycle? We focus on knowledge and skills necessary to record and analyze bioelectrical signals. So mainly we are focusing on EEG, but also we teach them how to measure and analyze EMG, EOG, ECG. And we teach physical basis of these signals, so they can understand how those signals arise in the organism and how to record these signals, and then how to process them and draw some conclusions. So for this we teach also statistical inference. And of course we need to develop programming skills because most of these techniques, although we have already running toolboxes for this, but we try to make them understand what is beneath. So some of them they have to code from the scratch. Also we found out that it is very useful to teach them some machine learning, and in fact I must say that we see growing demand and pressure from students that they want to have more and more courses on machine learning. And we think that the foundations should be also very strong on physics and mathematics, that is what our faculty can support the students very well. And how do we divide the time that they have for different types of courses? So on the first level as you can see the majority is probably physics and math, so this goes for first three semesters, is mainly math and physics and programming. Then we start joining some bio things, we have cell biology and histology, they can talk to people in other laboratories and they know how to look into their microscope. And there is also some courses or psychology for example because some of them will probably work in hospitals or maybe have contact with patients, so this is the things that we wanted to teach them. Of the informatics stuff we have general IT and especially programming and we have chosen at that time Python and it turned out to be a good solution. Original at that time most of the people in our lab were using MATLAB. But we have found that there is a new tool that seems to be growing popular and that is free. And that's why we have switched to Python and right now it seems that it was a very good decision. And of course we have some courses on databases as well. And from what I call special, courses that I think are somehow special to neuroinformatics, this is statistics, signal processing, biosignal acquisition and biomedical imaging and machine learning. This biosignal acquisition in fact is quite a big portion of real laboratories. So they in fact measure on themselves those biosignals. So we work with human signals not on the cell level but on the surface level. And what I think is also valuable that they learn how to process those signals that they have collected. So they can see what they did during the measurement, influences how they have to fight with those signals later. So how to mark all the stuff that they have done, how to try not to make too many artifacts and how to manage also, I think this works nice. And what can do students after these studies? So after this first degree, most popular choice is just to continue on the second cycle. And fortunately it turned out that it also works quite nice that we have two cycles. So that after this first cycle, people can choose the second cycle, not only here in our university, but we found that there is quite a number of people that tries to find it somewhere else in Europe, for example, so they just move with their ASMUS programs or with some other programs to other universities. What do we teach at the second cycle? So our idea was that the first cycle we have mainly focus on signal recording and analysis and the second cycle goes for mathematical modeling of neural processes. So we teach the modeling in Neuron, in MATLAB, and general mathematical modeling so with just equations and, for example, also graphical analysis and quantitative analysis of equations. There are also some further developments in physics, like courses on electrodynamics and quantum mechanics. And there is also a significant individualization at this level. So we take advantage of the fact that we have other faculties around. So quite often people take some courses at math or at informatics or at psychology. So that depends on their interests. Even some of them take courses at the Technical University of Warsaw because we have agreement that the students can migrate between the universities. Ok, and the composition is that we have still some part of physics, but it is quite a lot of this can be self-selected, but we suggest strongly that they take electrodynamics. Then there are some, again, self-selected biochem or psychology courses and these are offered mainly at their faculties here. And we have these special courses for neuroinformatics that we have this modeling in biology, modeling of neural systems, further development of statistics, programming. And of course there are seminars where we ask different specialists on different topics, so they just give talks on their own research, current research. And what happens after these studies with our alumni? So I would say that it's more or less half and half. Half of them goes to further research and they recruit for PhD programs either here or, for example, at Nenski or in other countries. And the other half tries to find the work in commercial companies and in particular there are companies that have R&D departments that use signal analysis and require understanding of data science, programming and skills of mathematical modeling. And recently to address the real world demands, we started to think and we will introduce this next year. In fact, student group projects are already running, so these are projects that require three to five students to form a group. The tutor gives them a topic. In fact, it is the way that the tutor presents some topics in advance and then groups of students come to them and say, okay, we want to take this topic. And then the funny thing is that the tutor is not meant to help them to solve the problem from the rhetorical side, but he should just try to point them how to correct sources, but also watch as they develop the soft skills. That means they should try to structure the problem, to divide the problem into who takes which part and then finally to come up with the solution. And also there is, I think, quite an important part that they have partial self-assessment. So the final grade for the project is not what the tutor thinks, but it has some weight also from what the people think that each person in the project did. And the other thing that we'll introduce next year is something we call group programming because we found that academic work tries to teach students one by one. So they do not, in fact, we do not promote cooperation between them. And in this case, we would like to promote the cooperation. This will be a little bit bigger programming tasks that they have to learn how to structure this project, how to make, how to progress with the project, so how to watch how it develops and what are the issues, how to also use version control. And I think quite important is the part of code reviews so that they learn that they do not code only for themselves. They have to write such a code that a colleague can understand and make use of this. And finally, I would like to tell that we have already graduated 67 students on the bachelor level and 35 on the master level. And that's mainly all. The last list that I have, this is the list of places where our alumni are now, those that I could find on some links in contacts or in my contacts. So that's what they do right now. Okay, thank you very much for the attention.