Author: Benjamin

Lecturer: Till Bockemühl / Ronald Sladky
Fields: Neuroscience

Content

The brain, the cause of – and solution to – all of life’s problems. According to our brains it is the most fascinating structure in the known universe. Consisting of about 86 billion neurons where each can form thousands of connections to other neurons it is also the most complex structure in the known universe. In this course we would like to give you a rough guide and introduction to the basic principles, fundamental theories, and methods of neuroscience.
We will demonstrate that neuroscience can be seen as a multi-modal, multi-level, multi-disciplinary research framework that aims at addressing the challenges of this megalomaniac scientific endeavor. We will see that different frameworks and methods can lead to conflicting empirical evidence, theoretical assumptions, and heated debates. However, we argue that this might be the only way to uncover the mysteries of our brain.
In this course we will cover a variety of scopes and perspectives. We will teach some of the fundamentals of neuroscience in human and non-human animals, but we will also explore some explanatory gaps between the different levels of inference.
On a phenomenal level we will investigate the functions of individual neurons and small networks. We will discuss if and how we can learn from (genetically modified) model animals about neural functions. To what degree is this relevant for understanding human brain function, such as learning and decision making? On the other hand, we will also investigate the state of the art in human brain mapping and cognitive neuroscience. Can findings from neuroimaging tell us anything at all about neurobiology – or are they just fancy illustrations that are better suited for children’s books?

Objectives:
– To understand the anatomy and function of neurons
– To understand the interaction of neurons in a functional network
– To understand central methods and theories used in neurobiology and human cognitive neuroscience
– To understand the scope of different methods and theoretical frameworks

Literature

• Cacioppo JT, Berntson GG, Lorig TS, Norris CJ, Rickett E, Nusbaum H. Just because you’re imaging the brain doesn’t mean you can stop using your head: a primer and set of first principles. J Pers Soc Psychol. 2003 Oct;85(4):650-61. [Link]
• Park HJ, Friston K. Structural and Functional Brain Networks: From Connections to Cognition. Science, 2013 Nov;6158(342):1238411 [Link]
• Bear MF, Connors BW, Paradiso MA. Neuroscience: Exploring the Brain. Wolters Kluwer Health. 2015.

Lecturer

Till Bockemühl studied biology and philosophy at Bielefeld University. He did his diploma thesis as well as his doctoral thesis with Volker Dürr in the lab of Holk Cruse at Bielefeld University. Currently, he is a postdoctoral researcher in the lab of Ansgar Büschges at the University of Cologne. His main research interests comprise the motor control of locomotion, neuroethology, and computational neurobiology. To investigate these topics, he uses the fruit fly Drosophila and the ever-expanding toolkit of methodological opportunities this model organism has to offer.

Affiliation: University of Cologne
Homepage: http://www.zoologie.uni-koeln.de/bueschges-staff-tillbockemuehl.html

Ronald Sladky. My research focuses on the amygdala and emotion processing in the human brain. In addition, I am always working on new neuroimaging, data processing, and modeling methods. One of these new methods is real-time functional MRI, where people can learn to regulate their own brain states while they are inside the MRI scanner. This method is not only a promising therapeutic tool, it will also allow for completely new ways of discovering how our brains work.

Affiliation: University of Vienna
Homepage: http://sweetneuron.at

Lecturer: Ulrike Herrmann
Fields: Economics/Politics

Content

Humankind is ruining the planet, and the climate crisis is especially threatening. Hence, politics and economics hope for “green growth”. But this is an illusion. Green energy from solar panels and wind turbines will be insufficient to support permanent growth. The economy must shrink. Yet, shrinking would be the end of capitalism, because it is only stable as long as there is growth. Economic journalist Ulrike Herrmann describes what the future looks like – without growth, profit, cars, airplanes, banks, insurance companies and almost without meat.

Lecturer

Ulrike Herrmann is financial editor at the German newspaper „tageszeitung“ (taz). She is a trained bank clerk and completed an internship (Volontariat) at the Henri-Nannen-School of Journalism. Subsequently, she studied history and philosophy at Freie Universität Berlin. She is regular guest on radio and television and authored several bestsellers. Her latest book is titled “The End of Capitalism: Why Growth and Climate Protection Are Incompatible – and How We Will Live in the Future” (KiWi 2022).

Lecturer: Philipp Wicke
Fields: Artificial Intelligence / Creativity

Content

This course provides students with an overview on the research field of computational creativity. A focus of the systems covered is on language creativity, visual systems (diffusion models), and generative language models. The students learn various fundamental concepts of creativity research and generative computer models. In this context, corpora and systems are presented, their use is taught, and their application is practiced.

Lecturer

Philipp Wicke completed his B.Sc. at the University of Osnabrück and his PhD at University College Dublin.

Lecturer: Thomas Christaller

Content

Since a few decades human touch became a hot topic in behaviour cognition, brain science, and psychology. Since the antiquity in Greece touch was regarded as the most primitive, animalistic sense in humans far away from the visual sense and its importance for human intelligence. Only Aristotle in one of his texts wrote that touch is the sense which makes us human. Today there is a huge amount of publications about the different roles touch plays in our capabilities to build up reliable social relationships with other humans. In general the appropriate touch helps you to trust others, becoming more self-confident, learning and performing better at school or university as well as performing a task.

In a way the forms of touch used in a fight have the opposite effect. They should hurt and dominate another person up to the point where this person gives in and subdue to the stronger, mightier, more brutal one. The major part of the training in the martial arts concentrate on techniques to control or hurt another person. The smaller part of the training is how to endure pain. The better you are in this the higher your chances are that you can take in punches.

Aikido is a martial art and has many very effective powerful techniques. But important is that you start to harmonize the forces of the other person with yours in a way that the person looses balance and you can finish a proposed attack in a way where nobody is threaten, hurt, dominated. In a way it is stopping the fight before it is overwhelming you.

In this course you will get physical, bodily experiences how to redirect a (simulated) attack into a harmless movement. And which important role touch plays there and your inner posture and emotions to those who are attacking you. We will reflect your experiences on a more scientific level to get step by step a more complete and complex picture of human nature. There is no previous knowledge necessary, neither in the cognitive framework of embodiment nor in martial arts. All experiments are carefully chosen so that physical power or strength doesn’t play a role and therefore you can perform these experiments and expect a lot of fun and no pain 😎

Literature

• Amdur, Ellis. Hidden in Plain Sight. Esoteric Power Training within the Japanese Martial Traditions. Freelance Academy Press, Wheaton (IL), 2018.
• Böhme, Rebecca. Human Touch. Warum körperliche Nähe wichtig ist. C.H. Beck, 2019.
• Linden, David J. Touch: The Science of the Sense that Makes Us Human. Penguin, 2016.

Lecturer

Thomas Christaller was a professor in Artificial Intelligence at the University of Bielefeld and director of the Fraunhofer Institute IAIS until his retirement in 2010. Since then he is a professional teacher for the Japanese martial art Aikido and running a dojo in Bonn (Zentrum für Bewegung & Lebenskunst). During his time as researcher he focused on building artificial systems to figure out what human intelligence is. He started with Natural Language Processing, turning to Knowledge-based Systems, and finally taking mobile robots as means to study embodied cognition.

He started to practice Aikido in 1972, making his first teaching experiences in 1976 at the University of Bielefeld. Over the years he blended his insights in AI about cognition with those he gained in Aikido. He gave Aikido seminars in Russia, USA, England, and Germany invited by diverse groups and organizations. While running a small robotics research lab as a side job in Kitakyushu, Japan, he gave Aikido classes at the campus of Wakamatsu. He has the 6th Dan (“Black Belt”) awarded by the so-called Hombu Dojo of Aikido in Tokyo. Here you can look at some videos to get an idea about his interpretation of Aikido. 

Lecturer: Alexander Hölken
Fields: Dynamical Systems Theory, Complexity Theory, Causality, Cognitive Science

Content

One of the central questions of cognitive science and neuroscience is that of how an organism’s intracranial states cause, and are in turn affected by, their behavior. Within cognitive science in particular, there exists a long-standing tradition of analyzing processes of cognition and behavior in terms of linear chains of cause and effect. For instance, computational theories of mind generally assume that the brain (or parts of it) operate similar to, or literally are, discrete-state machines (Chalmers, 2011). Thus, computational explanations of how cognitive processes cause behavior often refer to brain states as discrete entities, whose instantiation at some timestep t causes the instantiation of another discrete state at t+1, and so on, eventually realizing the behavior in question. While these assumptions are well-suited for explaining the development of abstract computational systems, they often fail when applied to real-life biological systems. This is because these systems don’t operate analogously to discrete-state machines:

1. Discrete-state systems are neatly decomposable into spatially separate entities that cause each other to change from one discrete timestep to the next. This need not be the case for complex natural systems (Kelso & Engstrom, 2008; Eigen, 2013; Lamb, 2015).

2. In discrete-state systems, temporality matters only in regards to the order in which system states happen in. In complex natural systems, both order and timing are causally relevant.

3. In discrete-state systems, the part/whole relationship is a mechanistically reductive one: The development of their larger-scale parts is explicable purely by reference to states of smaller-scale parts, along with their position on a causal chain. In complex natural systems, processes on the level of the whole system can causally influence processes on lower levels. This makes mechanistic reduction impossible: Even complete descriptions of lower-level interactions are not sufficient to explain the development of the whole (Bishop, 2008).

In this course, we will learn about the unique causal features of complex natural systems and how they explain their development. In particular, we will look at the relationship between wholes and their parts, and how system states at different levels constrain each other over time. In order to do so, we will become familiar with the concepts of dynamical regimes, attractor landscapes, and order/control parameters (Kelso, 1995) by looking at simple examples which do not presuppose a mathematical background. Many of these examples will come from experiments that explicitly aim to manipulate e.g. the control parameters of a dynamical regime, for instance in psychological studies about behavioral coordination (Schöner, Zanone & Kelso, 1992; Buchanan, 2004; Nalepka et al., 2021). Finally, we will wrap up by discussing possible solutions for contemporary problems that this methodology may offer, such as the creation of artificial general intelligence.

Literature

• Bishop, R. C. (2008). Downward causation in fluid convection. Synthese 160, 229 – 248. DOI: 10.1007/s11229-006-9112-2
• Buchanan, J. J. (2004). Learning a single limb multijoint coordination pattern: the impact of a mechanical constraint on the coordination dynamics of learning and transfer. Experimental Brain Research 156, 39 – 54. DOI: 10.1007/s00221-003-1763-3
• Chalmers, D. J. (2011). A Computational Foundation for the Study of Cognition. Journal of Cognitive Science 12, 323 – 357.
• Eigen, M. (2013). From Strange Simplicity to Complex Familiarity. Oxford University Press. • Kelso, J. A. S. (1995). Dynamic Patterns: The self-organization of brain and behavior. The MIT Press.
• Kelso, J. A. S. & Engstrøm, D. A. (2008). The Complementary Nature. The MIT Press.
• Lamb, M. (2015). Characteristics of Non-reductive Explanations in Complex Dynamical Systems Research. [Doctoral Thesis, University of Cincinnati]
• Nalepka, P., Silva, P. L., Kallen, R. W., Shockley, K., Chemero, A., Saltzman, E. & Richardson, M. J. (2021). Task dynamics define the contextual emergence of human corralling behaviors. PloS ONE 16, e0260046. DOI: 10.1371/journal.pone.0260046
• Schöner, G., Zanone, P. G., Kelso, J. A. S. (1992). Learning as Change of Coordination Dynamics: Theory and Experiment. Journal of Motor Behavior 24, 29 – 48

Lecturer

Alexander Hölken received his M.Sc. in Cognitive Science in March 2022 and is currently working on his PhD at the Ruhr-University Bochum. His research focuses on Dynamical Systems approaches to explaining cognitive behavior as a result of the coordination of intracranial states and processes with states and processes of the body and the environment. His doctoral thesis aims to develop a non-reductive physicalist theory of how mental states/processes can be genuinely causally efficacious.

Affiliation: Ruhr-Universität Bochum

Lecturer: Moritz Kriegleder
Fields: Cognitive Science, Information Theory, Philosophy of Science

Content

Today, most discussions of the mind and brain include ambigious terms such as information, memory, and models. While we often do not question their origin and usage, we need to be aware of the philosophical problems of the direct analogy of computation and mental dynamics.
In my talk I will present the historical and mathematical basics of information theory and discuss its influence on cybernetics and cognitive science. While it has proven especially useful in cognitive neuroscience, our use of information to describe cognition stands on shaky philosophical ground. I will discuss these problems of interpretation in detail by analysis of the free energy principle, a current hotly debated mathematical model of the mind that builds on information theory and cybernetics.

Literature

• Dupuy, Jean-Pierre (2000). The Mechanization of the Mind: On the Origins of Cognitive Science. Princeton University Press. Bruineberg, J., Dołęga, K., Dewhurst, J., & Baltieri, M. (2022). The Emperor\’s New Markov Blankets. Behavioral and Brain Sciences, 45, E183. doi:10.1017/S0140525X21002351 Di Paolo, E., Thompson, E., & Beer, R. (2022). Laying down a forking path: Tensions between enaction and the free energy principle. Philosophy and the Mind Sciences, 3. https://doi.org/10.33735/phimisci.2022.9187

Lecturer

Moritz Kriegleder, MSc is a Phd Student in the ERC Project Possible Life by Professor Tarja Knuuttila. With a background in both Cognitive Science and Physics he is interested in computational modelling of the mind from a mathematical and philosophical perspective.

Affiliation: University of Vienna
Homepage: twitter: @mokriegleder

Lecturer: Michael Bressler
Fields: Medicine/Neuroscience/Software Engineering

Content

Following an amputation, up to 90% of those affected experience sensations of their absent limb. These sensations can range from a simple sense of the limb\’s continued presence to the perception of a distorted or twisted phantom limb, which can result in significant pain and discomfort. The duration of phantom limb pain can persist for several years and negatively affects the individual\’s overall quality of life.

While the underlying mechanism of phantom limb pain is not fully understood yet, there are several theories that attempt to explain the phenomenon. To date, there is also no standard therapy for alleviating the pain. Treatment of phantom limb pain with conventional pain medications is often not effective. A frequent treatment method, but also not effective in all patients, is mirror therapy, in which the presence of the missing limb is visually simulated to the brain by placing a mirror in the sagital plane of the patient. To improve the effectiveness of this approach, researchers are exploring the use of immersive digital media, such as augmented reality (AR) or virtual reality (VR), as well as sensory feedback techniques.

This talk gives an overview of the phenomenon of phantom sensations and phantom limb pain, and addresses the proposed theories. The talk will present the development of a software tool called C.A.L.A., which is designed to facilitate the visualization and documentation of phantom limbs. Additionally, an augmented reality (AR) game, developed as a digital extension of mirror therapy, will be introduced. Finally, the course will discuss strategies for the long-term reduction of phantom limb pain by means of computer-aided technologies.

Literature

• Bressler M, Merk J, Heinzel J, Butz MV, Daigeler A, Kolbenschlag J, Prahm C. Visualizing the Unseen: Illustrating and Documenting Phantom Limb Sensations and Phantom Limb Pain With C.A.L.A. Front Rehabil Sci. 2022 Feb 9;3:806114. doi: 10.3389/fresc.2022.806114. PMID: 36189032; PMCID: PMC9397903.
• Prahm C, Bressler M, Eckstein K, Kuzuoka H, Daigeler A, Kolbenschlag J. Developing a wearable Augmented Reality for treating phantom limb pain using the Microsoft Hololens 2. ACM Int Conf Proceeding Ser. 2022;1(1):309–12. doi:10.1145/3519391.3524031.
• Flor H. Phantom-limb pain: characteristics, causes, and treatment. Lancet Neurol. 2002 Jul;1(3):182-9. doi:10.1016/s1474-4422(02)00074-1. PMID: 12849487.
• Melzack R. Pain and the neuromatrix in the brain. J Dent Educ. 2001 Dec;65(12):1378-82. PMID: 11780656.
• Ortiz-Catalan M. The Stochastic Entanglement and Phantom Motor Execution Hypotheses: A Theoretical Framework for the Origin and Treatment of Phantom Limb Pain. Front Neurol. 2018 Sep 6;9:748. doi: 10.3389/fneur.2018.00748. PMID: 30237784; PMCID: PMC6135916

Lecturer

Michael Bressler finished his Master’s degree in Information Technology at the Technical University of Vienna with a focus on human-computer interfaces and user interface design. After working as a software engineer for several years in the private sector, he returned to research at the department for Hand, Plastic, Reconstructive and Burn Surgery at the University Clinic of Tuebingen/BG Hospital, Germany. His research mainly focuses on computer assisted rehabilitation, virtual and augmented reality, and serious games for health.

Affiliation: BG Hospital, University Clinic of Tuebingen, Department for Hand, Plastic, Reconstructive and Burn Surgery
Homepage: https://www.bg-kliniken.de/klinik-tuebingen/fachbereiche/detail/rekonstruktive-chirurgie/

Lecturer: Marius Klug
Fields: Cognitive Neuroscience

Content

Recent technological advancements in instrumentation and analysis methods of human brain imaging data such as electroencephalography (EEG) increasingly allow the measurement of mobile participants interacting with their environment. The new field of Mobile Brain/Body Imaging (MoBI) (Gramann et al., 2011; Jungnickel et al., 2019) combines these measurements with imaging methods regarding the body, such as motion or eye tracking, and analyzes the multimodal data in order to investigate natural cognition in action. These analyses require the synchronized import of all data streams, options to process body data modalities, reliable preprocessing of EEG data in light of the elevated amount of non-cortical contributions in mobile settings, and the combined functional analysis of all modalities.

To facilitate this process, the BeMoBIL Pipeline was created (Klug et al., 2022). This is an open-source MATLAB toolbox for fully synchronized, automatic, transparent, and replicable import, processing, and visualization of MoBI and other EEG data. It includes wrappers for EEGLAB functions, uses various existing EEGLAB plugins, and comes with additional new functionalities such as the extraction of events from the data. All parameters are configurable in central scripts and everything is additionally stored in the data itself, facilitating the report and replication of MoBI studies. Throughout the process, plots are generated to keep the researchers informed.

This course will introduce the concept of MoBI, explain EEG analysis in the BeMoBIL Pipeline with details and parameter choices, and give an outlook on example applications and future prospects.

Literature

• Gramann, K., Gwin, J. T., Ferris, D. P., Oie, K., Jung, T. P., Lin, C. T., Liao, L. D., & Makeig, S. (2011). Cognition in action: Imaging brain/body dynamics in mobile humans. Reviews in the Neurosciences, 22(6), 593–608.
• Jungnickel, E., Gehrke, L., Klug, M., & Gramann, K. (2019). MoBI-Mobile Brain/Body Imaging. In H. Ayaz & F. Dehais (Eds.), Neuroergonomics: The Brain at Work and in Everyday Life (1st ed., pp. 59–63). Elsevier.
• Klug, M., Jeung, S., Wunderlich, A., Gehrke, L., Protzak, J., Djebbara, Z., Argubi-Wollesen, A., Wollesen, B., & Gramann, K. (2022). The BeMoBIL Pipeline for automated analyses of multimodal mobile brain and body imaging data. In bioRxiv.

Lecturer

Marius studied Cognitive Science in Tübingen, Germany, and received his PhD in Cognitive Neuroscience at the TU Berin, Germany. His research focused on methodological considerations and advancements of Mobile Brain/Body Imaging data analysis. In his new research group at BTU Cottbus, Germany, he will investigate the application of physiological data as user interfaces in virtual reality.

Affiliation: TU Berlin, BTU Cottbus, Zander Labs
Homepage: https://discord.gg/7MJjQ3f

Lecturer: Birgit Peterson
Fields: Scientific Writing

Content

The epistemological process of academic working and particularly the processes of scientific reading and writing involve a variety of materialities, mindsets and spaces. So, the ability to adapt these complex processes in a situated way, combining different materialities and different mindsets methodologically to create harmonic rhythms and step sequences of working is crucial for further development. This “choreography”, the arrangement of materialities over time and space, influences the results and the success of scientific writing and academic work.
In this practical course we are going to reflect and share our individual “choreographies” of academic working. Although all 4 parts built on each other, it is possible to join for only one topic as well.

In the 1st lecture, the focus will be, on how we create spaces and adjust it to the needs of our diverse academic working processes. What are these spaces constituted of? How do we arrange stuff and staff to design a prosperous atmosphere? And how do these different spaces and influences our cognitive processes, enabling different choreographies of thinking and working?

In The 2nd lecture we explore our reading behaviour. First, we discuss, how we are framed by different reading space and materialities, Then we focus on how we switch them and our mindsets to better interact with literature and data for different purposes. Finally, we look on promising strategies to thrive our successful reading processes.

In the 3rd lecture we will extend these explorations to our writing behaviour, focussing on the one hand on the diverse materialities that are involved in embodying thoughts in verbal and non-verbal products, and how we change our choreographies when shifting between drafting, rewriting and revision processes.

The 4th Lecture draws form the former ones but additionally stresses the roles of rhythms, patterns and the scrambling of elements for the whole composition: How does the harmonic compilation of spaces, materialities and mindsets, and the rhythm of switches within them overall, constitute a successful choreography for our personal academic working processes?

Lecturer

Birgit Peterson

Affiliation: University of Vienna
Homepage: https://www.germ.univie.ac.at/birgit-peterson/

Originally, this slot was intended to host a keynote lecture by Georg von Wichert. Unfortunately, this had to be cancelled. Instead, we offer:

Lecture Hall 2 – Small discussion rounds on the current war situation

Lecturers: Ihor Arkhypchuk & Jutta Kretzberg

After two years of pandemic, the world faces a new challenge, the war in Ukraine. Many of us feel helpless in view of the shocking news of millions of people suffering. In this meeting, we want to provide a forum to the IK participants for speaking in small groups about our personal perspectives on the current situation: How do I feel in this situation and what is my way of coping with it? What can I do to improve the situation for myself or / and for other people?
In this way, we hope to encourage the IK participants to think about their own ways out of that paralyzing feeling of helplessness.
To get the discussion started, Ihor will share his perspective of a Ukrainian student in Germany and give some practical advice on how to welcome refugees from his country.

Lecture Hall 3 – Artificial Intelligence in Education

Lecturer: Benjamin Paaßen

Teachers are notoriously overworked and yet are confronted with ever higher demands. To support teachers, AI researchers have tried to automate parts of the teaching process for 40 years now. Yet, education remains a challenging application domain for AI. I will provide a brief overview over the vision of AI in education, introduce some of the typical tools, and provide some insight on the practical and ethical challenges. Then, I want to open the floor to discussion, e.g. with respect to the following questions:

• Do you think AI could/should be applied in your own educational context?
• What are the practical and ethical challenges to achieve high-quality AI systems for education?
• In which direction should AI in education research go in the future?