Author: Philipp Wicke

Lecturer: Ronald Sladky
Fields: Cognitive Modeling, Data Modeling

Content: Originally, the idea of free energy minimization has been used as a tool for data modeling, in particular, to model effective connectivity in the brain based on neuroimaging data. On top of this, Karl Friston has proposed the free energy principle as a general principle for understanding brain functions. A bold statement – but, if true, one of the biggest breakthroughs in cognitive science.

In the form of active inference, the free energy principle could provide a neuro-computational explanation for predictive processing, the Bayesian brain hypothesis, and enactive cognition. It could provide a unified conceptual and computational framework to link previously distant and isolated research fields in the cognitive sciences that could be compatible with how we understand self-organization of living systems in a physical world.

In this course we will talk about how we make sense of data using models. By looking at how cognitive neuroscientists study the human brain using fMRI and brain connectivity methods. We discuss how models shape the way we interpret the world. I will teach you how these models work so you will find out on your own what to do with them. To what degree is the free energy principle useful for you? Modeling data, modeling the brain – or modeling the mind?

Session 1 covers how we turn fMRI data into brain activation and connectivity models. Session 2 focusses on dynamic causal modeling to study effective connectivity in the brain. Session 3 extends the formalism used in DCM to describe brain functions and life as we know it. Session 4 will review state of the art applications, theoretical developments, and empirical evidence for the free energy principle and active inference in action. As an illustration, I will use my own research on amygdala functions and dysfunctions and its connectivity. So, we will also talk about fear, trust, and other emotions – not just data, brains, and methods.

Literature

Friston, K. (2009). The free-energy principle: a rough guide to the brain?. Trends in cognitive sciences, 13(7), 293-301.
Marreiros, A. C., Stephan, K. E., & Friston, K. J. (2010). Dynamic causal modeling. Scholarpedia, 5(7), 9568.
Friston, K. (2013). Life as we know it. Journal of the Royal Society Interface, 10(86), 20130475.
van Es, T. (2021). Living models or life modelled? On the use of models in the free energy principle. Adaptive Behavior, 29(3), 315-329.
Corcoran, A. W., Hohwy, J., & Friston, K. J. (2023). Accelerating scientific progress through Bayesian adversarial collaboration. Neuron, 111(22), 3505-3516.
Sladky, R., Kargl, D., Haubensak, W., & Lamm, C. (2023). An active inference perspective for the amygdala complex. Trends in Cognitive Sciences.

Lecturer

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
Website: http://sweetneuron.at

Lecturer: Ilaria Tiddi
Fields: Artificial Intelligence

Content

Hybrid Intelligence (HI) is a rapidly growing field aiming at creating collaborative systems where humans and intelligent machines cooperate in mixed teams towards shared goals. In this course, we will get to know the field by discussing the vision, and basics, and the solutions that have been proposed so far. In particular, we will focus how symbolic AI techniques (knowledge graphs and semantic technologies) have been proposed as complementary building blocks to the subsymbolic (machine learning) methods, and how this combination has been used to help solving the main challenges in the field of Hybrid Intelligence.

The course comprehends 3 lectures:
1) Introduction to Hybrid Intelligence (1.5h). Here we will introduce the main research questions for the field of HI, present some of the solutions proposed so far, and finally discuss open challenges.

2) Introduction to Knowledge Graphs (1.5h). Here we will introduce the basics of Knowledge Engineering, including modelling information and reason about it using the RDF/RDFS/OWL languages, principles of knowledge/ontology engineering, and methods to query knowledge graphs.

3) Knowledge Engineering for Hybrid Intelligence (1.5h). The last lecture will introduce how ontologies and knowledge engineering methods can be used to design Hybrid Intelligence applications.

Learning objectives:
– familiarise with Hybrid Intelligence challenges and methods
– get to know the basics of Knowledge Graphs (RDF, OWL, SPARQL, ML over graphs)
– apply the principles of knowledge engineering for the design for Hybrid Intelligence applications

Literature

• Hogan, Aidan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane et al. “Knowledge graphs.” ACM Computing Surveys (Csur) 54, no. 4 (2021): 1-37.
• Akata, Zeynep, Dan Balliet, Maarten De Rijke, Frank Dignum, Virginia Dignum, Guszti Eiben, Antske Fokkens et al. “A research agenda for hybrid intelligence: augmenting human intellect with collaborative, adaptive, responsible, and explainable artificial intelligence.” Computer 53, no. 8 (2020): 18-28.
• Ilaria Tiddi, Victor De Boer, Stefan Schlobach, and André Meyer-Vitali. 2023. Knowledge Engineering for Hybrid Intelligence. In Proceedings of the 12th Knowledge Capture Conference 2023 (K-CAP \’23). Association for Computing Machinery, New York, NY, USA, 75–82. https://doi.org/10.1145/3587259.3627541
• Tiddi, Ilaria, and Stefan Schlobach. “Knowledge graphs as tools for explainable machine learning: A survey.” Artificial Intelligence 302 (2022): 103627.

Lecturer

Ilaria Tiddi is an Assistant Professor in Hybrid Intelligence at the Knowledge in AI (KAI) group of the Vrije Universiteit Amsterdam (NL). Her research focuses on creating systems that generate complex narratives through a combination of semantic technologies, open data and machine learning, applied mostly in scientific and robotics scenarios. She is Editor-in-Chief of the CEUR-WS publication, part of the Steering Committee for the Hybrid Human-AI Conference, and Coordinator of the international Staff Exchange for the Dutch Hybrid Intelligence consortium. Since 2014, she is regularly active in the OCs/PCs of the major venues in the KR field (ISWC/ESWC, HHAI, WWW, CIKM, IJCAI/ECAI).

Affiliation: Vrije Universiteit Amsterdam
Homepage: https://github.com/kmitd/

Lecturer: Stefan Kopp
Fields: Artificial Intelligence, Cognitive Science, Human-Agent/Robot Interaction

Content

With A.I. systems becoming more capable and being widely deployed in our everyday life and work environments, the question how human users can and should interact with intelligent systems becomes pivotal. The AAAI 20-year research roadmap (2019) pointed out that “meaningful interaction” between humans and AI should be a top research priority, encompassing questions like trust, transparency and responsibility, as well as online interaction, multiple interaction channels or collaboration. Going beyond “promptification”, humans and AI systems should be able to engage in forms of interaction that encompass mixtures of instructions, question answering, explanations, argumentation, or negotiation — all embedded in a flexible dialog, often multimodal and potentially even interwoven with physical action. In this course we will discuss how such advanced forms of interaction between AI systems and humans can be realized in an efficient and robust way. What they all have in common is that they hinge on the effect that systems with a certain degree of competence and autonomy are readily perceived and treated as having agency. Correspondingly, the interaction is framed as human-agent interaction (HAI), i.e. between two or more agentive entities, and applies to systems such as conversational personal assistants, socially assistive robots, conversational recommender systems, or autonomous vehicles.

The course will touch upon three questions: (1) What abilities do AI-based agents need to have in order to engage in robust and efficient HAI? (2) How can those abilities be modeled? (3) What are the possible effects on users? In discussing these questions we will focus on socio-cognitive core abilities for recognizing, reasoning about, and engaging in interactions with other social agents — what we call “Artificial Social Intelligence”. We start by learning about the basics of human-agent interaction and the emerging state of the art in modeling Artificial Social Intelligence in AI-based agents. We will then look at how robust and efficient HAI can be achieved through multimodal communication. In communication, participants continuously adjust their behaviors based on their interlocutor’s language, gestures, and facial expressions during social interaction. We will learn about modern approaches to create AI agents that can understand and participate in these dynamic, multimodal dialogue interactions. Finally, we will discuss computational approaches to realize “theory of mind” abilities such as recognizing or inferring another agent\’s intentions, beliefs, emotions or other mental states. Such abilities are indispensable for robust and efficient cooperation and have moved into the focus of much recent research on AI-based agents. For each topic, we will draw connections between insights from Cognitive Science, Psychology or Linguistics, and the modeling approaches in AI by means of cognitive modeling, model-based machine learning, deep learning, or lately LLMs.

Literature

• B. Lugrin, C. Pelachaud & D. Traum (2021). The Handbook on Socially Interactive Agents, Volume 2, pp. 77-111. ACM Press.
• Buschmeier, H., & Kopp, S. (2018). Communicative listener feedback in human–agent interaction: Artificial speakers need to be attentive and adaptive. Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018).
• Pöppel, J., & Kopp, S. (2018). Satisficing Models of Bayesian Theory of Mind for Explaining Behavior of Differently Uncertain Agents. Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018)
• Kopp, S., & Krämer, N. (2021). Revisiting Human-Agent Communication: The Importance of Joint Co-construction and Understanding Mental States. _Frontiers in Psychology: Human-Media Interaction_, _12_, 1-15. [https://doi.org/10.3389/fpsyg.2021.580955]
• Gurney, N., Marsella, S., Ustun, V., Pynadath, D.V. (2022). Operationalizing Theories of Theory of Mind: A Survey. In: Gurney, N., Sukthankar, G. (eds) Computational Theory of Mind for Human-Machine Teams. AAAI-FSS 2021. Lecture Notes in Computer Science, vol 13775. Springer, Cham. https://doi.org/10.1007/978-3-031-21671-8_1

Lecturer

Stefan Kopp is professor of Computer Science and head of the “Social Cognitive Systems” working group at the Faculty of Technology at Bielefeld University. He obtained his PhD in Artificial Intelligence for research on intelligent multimodal agents. After a postdoc stay in the US and a research fellowship at the Center for Interdisciplinary Research ZiF (Bielefeld), he has been deputy coordinator of SFB 673 “Alignment in Communication“, principal investigator in the Cluster of Excellence “Cognitive Interaction Technology“ (CTEC), and chairman of the German Cognitive Science Society (GK). Currently he is co-coordinator of the research center CITEC and a member of various other research centers and networks (TRR 318, CoAI, it’s OWL, CoR-Lab). He research interests are centered around the cognitive and interactive mechanisms of social interaction, communication and cooperation, and how such skills can be transferred to intelligent technical systems to enable new levels of human-technology cooperation.

Affiliation: Bielefeld University
Homepage: https://scs.techfak.uni-bielefeld.de

Lecturer: Thomas Stieglitz
Fields: Health and Wellbeing, Engineering, Material Sciences, Neuroscience, Technology Assessment

Content

Miniaturized neural implants cover the wet interface between electronic and biological circuits and systems. They need to establish stable and reliable functional interfaces to the target structure in chronic application in neuroscientific experiments but especially in clinical applications in humans. Proper selection of substrate, insulation and electrode materials is of utmost importance to bring the interface in close contact with the neural target structures, minimize foreign body reaction after implantation and maintain functionality over the complete implantation period. Silicon and polymer substrates with integrated thin-film metallization as core of stiff and flexible neural interfaces have been established as well as silicone rubber substrates with metal sheets. Micromachining and laser structuring are the main technologies for electrode array manufacturing. Different design and development aspects from the first idea to first-in-human studies are presented and challenges in translational research are discussed. Reliability data from long-term ageing studies and chronic experiments show the applicability of thin-film implants for stimulation and recording and ceramic packages for electronics protection. Examples of sensory feedback after amputation trauma, vagal nerve stimulation to treat hypertension and chronic recordings from the brain display opportunities and challenges of these miniaturized implants. System assembly and interfacing microsystems to robust cables and connectors still is a major challenge in translational research and transition of research results into medical products. Clinical translation raises questions and concerns when applications go beyond treatment of serious medical conditions or rehabilitation purposes towards life-style applications. The four sessions within the topic of “robust and responsible neural technology” will cover (1) neuroscientific and clinical applications of neural technology, (2) fundamentals on optogenetics, recording of bioelectricity and electrical stimulation, (3) the challenges of neural implant longevity and (4) ethical and societal considerations in neural technology use.

Literature

• Cogan SF. Neural stimulation and recording electrodes. Annu Rev Biomed Eng. 10:275-309 (2008). DOI: 10.1146/annurev.bioeng.10.061807.160518
• Hassler, C., Boretius, T., Stieglitz, T.: “Polymers for Neural Implants” J Polymer Science-Part B: Polymer Physics, 49 (1), 18-33 (2011). Erratum in: 49, 255 (2011); DOI: 10.1002/polb.22169
• Alt, M.T., Fiedler, E., Rudmann, L., Ordonez, J.S., Ruther, P., Stieglitz, T. “Let there be Light – Optoprobes for Neural Implants”, Proceedings of the IEEE 105 (1), 101-138 (2017); DOI: 10.1109/JPROC.2016.2577518
• Stieglitz, T.: Of man and mice: translational research in neuro¬technology, Neuron, 105(1), 12-15 (2020). DOI:10.1016/j.neuron.2019.11.030
• Stieglitz, T.: Why Neurotechnologies ? About the Purposes, Opportunities and Limitations of Neurotechnologies in Clinical Applications. Neuroethics, 14: 5-16 (2021), doi: 10.1007/s12152-019-09406-7
• Jacob T. Robinson, Eric Pohlmeyer, Malte Gather, Caleb Kemere, John E. Kitching, George G. Malliaras, Adam Marblestone, Kenneth L. Shepard, Thomas Stieglitz, Chong Xie. Developing Next-Generation Brain Sensing Technologies—A Review. IEEE Sensors Journal, 18(22), pp. 10163-10175 (2019) DOI: 10.1109/JSEN.2019.2931159
• Boehler, C., Carli, S., Fadiga, L., Stieglitz, T., Asplund, M.: Tutorial: Guidelines for standardized performance tests for electrodes intended for neural interfaces and bioelectronics. Nature Protocols, 15 (11), 3557-3578 (2020) https://doi.org/10.1038/s41596-020-0389-2

Lecturer

Thomas Stieglitz was born in Goslar in 1965. He received a Diploma degree in electrical Engineering from Technische Hochschule Karlsruhe, Germany, in 1993, and a PhD and habilitation degree in 1998 and 2002 from the University of Saarland, Germany, respectively. In 1993, he joined the Fraunhofer Institute for Biomedical Engineering in St. Ingbert, Germany, where he established the Neural Prosthetics Group. Since 2004, he is a full professor for Biomedical Microtechnology at the Albert-Ludwig-University Freiburg, Germany, in the Department of Microsystems Engineering (IMTEK) at the Faculty of Engineering and currently serves the IMTEK as managing director, is deputy spokesperson of the Cluster BrainLinks-BrainTools, board member of the Intelligent Machine Brain Interfacing Technology (IMBIT) Center and spokesperson of the profile neuroscience / neurotechnology of the university. He is further serving the university as member of the senate and as co-spokesperson of the commission for responsibility in research. His research interests include neural interfaces and implants, biocompatible assembling and packaging and brain machine interfaces.

Affiliation: Faculty of Engineering, IMTEK, LAboratory for Biomedical Microtechnology & BrainLinks-BrainTools, IMBIT//Neuroprobes
Homepage: https://www.imtek.de/laboratories/biomedical-microtechnology/bm_home?set_language=en

Lecturer: Brigitte Römmer-Nossek, Birgit Peterson
Fields: Academic skills, Higher Education, Writing Research and Didactics

Content

Resilience, Robustness and Responsibility in Academic Writing

The process of academic working and particularly the processes of reading and writing in academia is demanding, between maintaining a resilient writing behaviour and creating a robust piece of text. It is important to develop a routine in academic writing, which enables the author to become a responsible member of the respective discourse community and satisfy all requirements of scientific quality.

In this practical course we are going to work on aspects important for successful academic writing from 4 different perspectives. Although all 4 parts are connected to each other, it is possible to join for only one session as well.

A Robust Framework for Your Writing Project

In the first session we will introduce our approach to academic writing. We will provide a generic framework for orientation in writing projects and pinpoint the challenges many writers come across in the respective phases. We will use a writing exercise for „getting to the point“.

What am I Doing anyway? Developing as a Writer and Researcher

In this session we will explore the pains of placing what we are doing and writing about it. Finding a research question, positioning in the field of research what we are working on, and deciding what we want to say is hard work. We will look at reasons for that and discuss some strategies.

How to develop a resilient voice as a responsible academic writer

In the third session we focus on how to develop a strong academic voice out of your individual position as academic author. Therefore, you will explore you writing behaviour and the process of gradual formulation of ideas and voice through writing by different mind writing and specific revision strategies useful to embody thoughts within written products.

How to maintain robust choreographies for your academic working

In the last session, the focus will be on the “choreography of academic working”: how we use and adjust a variety of materialities, actions and spaces to our needs within the phases of the academic writing process. The juggling with diverse working materialities and spaces that we tend to involve for different purposes eases the rhythmic shift between different mindsets we need for an efficient and harmonic choreography of academic working.

Literature

• Davies, S., Pham, B-C., Dessewffy, E., Schikowitz, A., & Mora-Gámez, F. Pinboarding the Pandemic: Experiments in Representing Autoethnography. Catalyst: Feminism, Theory, Technoscience, (2022). 8(2). doi: 10.28968/cftt.v8i2.38868
• Elbow, P. Writing with Power: Techniques for Mastering the Writing Process. New York Oxford University Press, 1998.
• Gallagher, C W. “What Writers Do: Behaviors, Behaviorism, and Writing Studies.” College Composition and Communication 68, no. 2 (2016): 238–65. http://www.jstor.org/stable/44783561.
• Peterson, B: Die 99 besten Schreibtipps für die vorwissenschaftliche Arbeit, Matura und das Studium. 2., überarbeitete Auflage. Wien: Krenn, 2017
• Skinner BF. How to discover what you have to say-a talk to students. Behav Anal. (1981) Spring;4(1):1-7. doi: 10.1007/BF03391847.

Lecturer

Brigitte Römmer-Nossek is responsible for the team “Student Research and Peer Learning” at the University of Vienna’s Center for Teaching and Learning and has been a lecturer for 20 years. She studied Brain and Cognitive Science Science as a studium irregulare and was involved in the implementation of the the joint Middle European interdisciplinary master programme in Cognitive Sciences (MEi:CogSci). In her dissertation she engaged in sense-making of academic writing as a cognitive developmental process from an enactive perspective.

Affiliation: University of Vienna
Homepage: https://ctl.univie.ac.at/ueber-das-ctl/teams-und-mitarbeiterinnen/team-wissenschaftliches-arbeiten-und-peer-learning/

Mag.a Birgit Peterson studied Human Biology in Vienna and Rome and gave particular emphasis to Cognitive Science when the Mei:CogSCi Join Master started in Vienna in 2006. She is interested in human cognition and communication in context with learning scenarios. Her focus lies on the connection of human language, thinking, writing, reading and learning and she has been working as a “Learning Professional” and Trainer for scientific reading, writing and thinking for more than 10 years now. Peterson is an author, speaker and trainer in the field of education, from elementary school up to Higher Education. Additionally she is consulting different developmental programs in higher education, such as Support for Scientific Writing, Teaching Skills or Peer- and Alumni-Mentoring programs for young scientists.

Affiliation: University of Vienna

Lecturer: Thomas Christaller
Fields: Cognitive Psychology, Philosophy

Content

Obviously, in competitive sports, fighting systems, and martial arts you encounter stressful situations in which you don’t know if and how you can resolve them. Independently of the mastery of the techniques relevant for the specific body movement system the training always includes exercises which helps to overcome the stress in such situations and being able to act in an appropriate and meaningful way. They help to overcome fear as well as aggression. The basic insight is that specific techniques with regard to breathing, relaxation, and posture you can foster your resilience when things don’t happen as expected. This will be the core of the course. To understand why these techniques really work practically we will explore the neural, emotional basis for them. Our brain is mainly used as a forecasting system for the very next second as well as for hours, days, and weeks by rehearsing. But in stressful situations the brain doesn’t have the resources to explore the alternatives making a plausible prediction. Another topic is to stay calm to reduce stress and avoid a narrowed view of the world. The main basis of this is breathing. Usually this is done unconsciously and changes according to your bodily effort and the expectation of the effort in the very next moment. Especially in the martial arts breathing is a core element to be able to deal with the unforeseeable. The usual reflex if one experiences an unexpected situation is to become bodily tense. But then you may not be able to act fast enough. Relaxation is the secret which is different from being weak. All these different systems are connected in our body posture. But resilience plays also a role after you experienced a trauma or some other negative event. Here, too, these insights can help to recover or re-bounce and stand-up again. The main insight is, what you learn bodily for a possible physical fight or threat can be transmitted into non-physical conflicts like discussions, verbal assaults, or mobbing. Finally it may become your individual personality.

Literature

• Amdur, E. (2018 ) Hidden in Plain Sight. Esoteric Power Training within Japanese Martial Traditions. Freelance Academy Press, Wheaton (IL).
• Holiday, R, Hanselman, S. (2020) Lives of the Stoics: The Art of Living from Zeno to Marcus Aurelius. Penguin Publications.
• Kruszewski A. (2023) From Ancient Patterns of Hand-to-Hand Combat to a Unique Therapy of the Future. Int J Environ Res Public Health. Feb 17;20(4):3553.
• Krings, L. (2017) Leibliches Üben als Teil einer philosophischen Lebenskunst: Die Verkörperung von Kata in den japanischen Wegkünsten. European Journal of Japanese Philosophy (EJJP). pp 179-197.
• Moore B, Woodcock S, Dudley D. (2021) Well-being Warriors: A Randomized Controlled Trial Examining the Effects of Martial Arts Training on Secondary Students’ Resilience. Br J Educ Psychol. Dec;91(4): pp 1369-1394.
• Stockdale, J. (1993 ) Courage Under Fire: Testing Epictetus’s Doctrines in a Laboratory of Human Behavior. Hoover Institution Press.
• Strozzi-Heckler, R. (2007 ) The Leadership Dojo. Build Your Foundation as an Exemplary Leader. Frog Ltd., Berkeley.
• Yagyu, M. (2003 ) The Life-Giving Sword. Secret Teachings from the House of the Shogun. Kodansha Int., Tokyo.

Lecturer

Thomas Christaller studied Mathematics, Physics, and Computer Science, working in the field of Artificial Intelligence since 1976. First on natural language understanding, then knowledge-based systems, and finally cognitive robotics. He was institute director first at the GMD then Fraunhofer Society heading the institute for Autonomous intelligent Systems then Intelligent Analysis and Information Systems located at Schloss Birlinghoven in Sankt Augustin, Germany. He co-founded the German journal \”KI\”, was a member of the Wissenschaftsrat, and co-founded the Interdisciplinary College. Since 1972 he is practicing the Japanese martial art of Aikido, holding the 6. Dan (black belt), teaching at his dojo in Bonn and giving Aikido seminars worldwide. * My column in akido journal (German) https://www.aikidojournal.de/Kolumnen/Professor_Thomas_Christaller/ * My biography in wikipedia (German) https://de.wikipedia.org/wiki/Thomas_Christaller * Videos about Aikido & Much More https://vimeo.com/lebenskunst

Affiliation: Aikido Teacher
Homepage: www.lebenskunst-bonn.de

Lecturer: Franjo Weissing
Fields: Ecology & Evolution, Behavioural Biology

Content

Biological organisms have to cope with ever-changing environmental conditions. They have been ‘designed’ for this task in a long evolutionary history, but how evolution by natural selection has achieved this is far from clear. Two properties are crucial for long-term survival in a changing world: ‘robustness’ (the ability to build the same phenotype under very different conditions) and ‘evolvability’ (the ability to rapidly respond to changing conditions by adaptive evolution). The conundrum is that these properties seem to be contradictory: doesn’t a robust design impede evolvability, and doesn’t evolvability require a flexible design? A second problem is that ‘evolutionary design’ is fundamentally different from the ‘engineered design’. While an engineer has foresight, adaptive evolution resembles a ‘blind watchmaker’ (Dawkins 1986) in that it is driven by short-term selection pressures. We all know that following short-term incentives often has negative implications in the longer term. How, then, can long-term properties like robustness and evolvability be shaped by a myopic process like natural selection?
Questions like these will be addressed in four sessions. The first two sessions will illustrate the dynamic complexity of apparently simple ecological and evolutionary systems. We will see that such systems can be ‘fundamentally unpredictable’ and that adaptive evolution can, in principle, drive a population to extinction (‘evolutionary suicide’). In the last two sessions, we will sketch a new way of evolutionary thinking that may (partly) resolve issues like these. We will see that the evolution of ‘responsive strategies’ (strategies that respond to the local environmental conditions) is fundamentally different from the evolution of non-responsive strategies. The reciprocal causality inherent to these strategies speeds up evolution by orders of magnitude and leads to quite different evolutionary outcomes. In biological organisms, responsive strategies are often implemented via regulatory networks (e.g., gene regulation networks or neural networks). It turns out that the evolution of such networks shares various properties with learning (by machines or intelligent agents).

Literature

• Session 1: Out of equilibrium
  • Huisman, J. & Weissing, F.J. 1999. Biodiversity of plankton by species oscillations and chaos. Nature 402: 407-410, doi: 10.1038/46540.
  • Huisman, J. & Weissing, F.J. 2001. Fundamental unpredictability in multispecies competition. American Naturalist 157: 488-494, doi: 10.1086/319929.
• Session 2: Conflict and cooperation
  • Baldauf, S.A., Engqvist, L. & Weissing, F.J. 2014. Diversifying evolution of competitiveness. Nature Communications 5: 5233, doi: 10.1038/ncomms6233.
  • Long, X. & Weissing, F.J. 2023. Transient polymorphisms in parental care strategies drive divergence of sex roles. Nature Communications 14: 6805, doi: 10.1038/s41467-023-42607-6.
• Session 3: The reciprocal causality of responsive strategies
  • Quiñones, A.E., Van Doorn, G.S., Pen, I., Weissing, F.J. & Taborsky, M. 2016. Negotiation and appeasement can be more effective drivers of sociality than kin selection. Phil. Trans. R. Soc. B 371:20150089, doi:10.1098/rstb.2015.0089.
  • Netz, C., Hildenbrandt, H. & Weissing, F.J. 2022. Complex eco-evolutionary dynamics induced by the coevolution of predator-prey movement strategies. Evol. Ecol. 36: 1-17, doi: 10.1007/s10682-021-10140-x.
  • Gupte, P.R., Albery, G.F., Gismann, J., Sweeny, A.R. & Weissing, F.J. 2023. Novel pathogen introduction triggers rapid evolution in animal social movement strategies. eLife, 12: e81805, doi: 10.7554/eLife.81805.
• Session 4: Robustness and evolvability
  • Wagner, A. 2008. Robustness and evolvability: a paradox resolved. Proc. Royal Society B 275: 91-100, doi: 10.1098.rspb.2007.1137.
  • Watson, R.A. & Szathmáry, E. 2016. How can evolution learn? Trends in Ecology & Evolution 31: 147-157, doi: 10.1016/j.tree.2015.11.009
  • Van Gestel, J. & Weissing, F.J. 2016. Regulatory mechanisms link phenotypic plasticity to evolvability. Scientific Reports 6:24524, doi: 10.1038/srep24524.

Lecturer

After studying mathematics and biology at the University of Bielefeld, I did my PhD work at the Centre for Interdisciplinary Studies (ZiF Bielefeld), where I co-organised the research year ‘Game Theory in the Behavioural Sciences’. Together with the later Nobel laureate Elinor Ostrom, I pioneered the introduction of ‘evolutionary thinking’ into the political sciences. In 1989, I moved to the University of Groningen (Netherlands), where I tackled a wide variety of eco-evolutionary questions with a combined theoretical and empirical approach. My area of expertise lies in the development and analysis of mathematical and computational models. Our emphasis is on ‘mechanistic models of intermediate complexity’ that lead to insights and predictions that can be tested in close collaboration with empirical biologists. In my research, I strive to understand the emergence of diversity at all levels of biological organisation (e.g., differences between cells, individuals, the sexes, groups, species, and ecosystems) and the implications of diversity for the evolution and functioning of biological systems. In the last ten years, I have broadened my research again to other disciplines. As a Distinguished Lorentz Fellow, I spent a research year at the Institute of Advanced Study in the Humanities and Social Sciences in Amsterdam, where I critically investigated the foundations of cultural evolution theory. I became convinced that new approaches are needed that do justice to the differences between genetic and cultural evolution. To this end, we are currently working on a new framework for the evolution of individual and social learning, which is based on the evolution of neural networks.

Affiliation: University of Groningen
Homepage: https://research.rug.nl/en/organisations/weissing-group-theoretical-biology

Lecturer: Giulia Belgiovine
Fields: Artificial Intelligence, Human-Robot Interaction

Content

The study of human-robot interaction is a vast and intricate field that adopts a multidisciplinary approach and encompasses numerous challenges. On the one hand, the quest to comprehend and model the intricate mechanisms underlying human cognitive and social abilities; on the other hand, the problem of how to replicate a comparable level of intelligence in cognitive interactive agents. Achieving this necessitates the integration of sensory and motor capabilities, along with memory, reasoning, and learning mechanisms, to develop artificial agents endowed with adaptation and generalization skills.
In these lectures, we will explore the interdisciplinary nature of this field, discussing the challenges and opportunities that lie ahead.

Lecturer

Giulia Belgiovine is a postdoctoral researcher at the COgNiTive Architectures for Collaborative Technologies (CONTACT) unit of the Italian Institute of Technology, Genoa, Italy. Her research investigates how to develop cognitive architectures for social robots to promote better human-robot interactions and how to foster robots’ autonomous learning and adaptive behavior. Her research interests include multiparty interactions, assistive robotics, and lifelong learning.

Affiliation: Italian Institute of Technology

Lecturer: Martin Tik, Anna-Lisa Schuler
Fields: Cognitive Neuroscience, Clinical Neuroscience, Neuroimaging

Content

In this course we will discuss basics, applications and hot topics for two of the most popular magnets in brain research: TMS and MRI. While fMRI allows for the depiction of neural underpinning underlying task processing, TMS as a neuromodulation technique allows for the targeted manipulation of these processes. This course will be dedicated to give an overview about both of the techniques and the advantages of their combination.

Session 1: In session one we will give an overview about the history of magnets in cognitive neuroscience and the evolution of the methods of interest (TMS, fMRI). We will furthermore explain the technical basics and the composition of these devices.

Session 2: In session two we will discuss the physiological mechanisms of action underlying the techniques including blood oxygenation and neuronal action potentials. Then we will give an overview about different applications of TMS and fMRI including exemplary research.

Session 3: In this session we will discuss specific applications of combining TMS with fMRI in cognitive neuroscience and clinical medicine. In the second part of this session, participants will have the opportunity to plan their hypothetical own TMS and fMRI experiments in small groups.

Session 4: Participants will discuss their projects in a plenum. Finally, there will be a summary, question round and wrap up of the course.

Learning goals:

– Basic principles of TMS and fMRI
– Applications of TMS and fMRI
– Direct transfer of these contents to own research

Literature

• Chen, J. E., & Glover, G. H. (2015). Functional magnetic resonance imaging methods. Neuropsychology review, 25, 289-313.
• Pitcher, D., Parkin, B., & Walsh, V. (2021). Transcranial magnetic stimulation and the understanding of behavior. Annual Review of Psychology, 72, 97-121.
• Burke, M. J., Fried, P. J., & Pascual-Leone, A. (2019). Transcranial magnetic stimulation: Neurophysiological and clinical applications. Handbook of clinical neurology, 163, 73-92.

Lecturer

Martin Tik is a Group Leader at the Medical University of Vienna. His main research interests lie in advancing the method of interleaving TMS with fMRI and the improvement of depression treatment using the combination of these methods.

Affiliation: Medical University of Vienna
Homepage: https://www.martintik.at/

Anna-Lisa Schuler is a Post-Doctoral researcher at the Max Planck Institute for Human Cognitive and Brain Sciences. She is mainly interested in the combination of TMS with fMRI in cognitive and clinical neuroscience including language processing and plasticity in healthy and neuropsychiatric populations.

Affiliation: Max Planck Institute for Human Cognitive and Brain Sciences
Homepage: https://twitter.com/AnnaLisaSchule1

Lecturer: Erika Covi
Fields: Emerging memory devices, Neuromorphic computing

Content

In recent years, the cloud-based approach to data classification has been challenged by the edge computing paradigm, which has enabled real-time data processing at the network edge, ideally next to the sensor collecting data. This paradigm poses severe constraints on the systems in terms of power-efficiency, compactness, and latency [1, 2]. Therefore, we need to explore unconventional hardware solutions able to meet these stringent requirements.
Brain-inspired architectures, particularly Spiking Neural Networks (SNNs), are promising candidates to achieve low-latency computation, and stateful, energy-efficient operations [3]. However, their current implementations primarily rely on digital or mixed-signal Complementary Metal-Oxide-Semiconductor (CMOS) technologies, which pose challenges in meeting the demanding memory, area, and power constraints of computing on the edge [1].
In this context, the integration of embedded memristive technologies holds a significant promise to enhance the capabilities of CMOS technology [2, 4] for the development of neuromorphic hardware [5]. Memristive devices are nanoscale devices able to change their conductivity upon application of proper electrical stimuli. They offer fast and energy-efficient tuneable volatile and non-volatile storage, and are therefore well-suited for storing SNN parameters. The exploitation of their unique properties, such as operation voltages compatible with current CMOS technology as well as analogue, neural-/synaptic-like behaviour, offer an attractive opportunity for realizing energy-efficient and massively parallel computing architectures in conjunction with CMOS technology [3, 5]. Indeed, these features enable efficient computation, neural dynamics, and synaptic plasticity, which are essential traits for emulating the brain’s functionality in hardware [4, 6].
In this course, we explore the role of memristive devices in emulating the functionality of neural networks, enabling edge systems to classify and recognise patterns or process sensory inputs. We emphasize the need for co-developing memristive devices with CMOS circuits to enable seamless integration and to exploit the strengths of both technologies. Furthermore, we discuss how the co-development of memristor devices, CMOS circuits, and innovative learning algorithms can facilitate edge computing paradigms. Moreover, the intrinsic physical characteristics of memristive devices, if correctly exploited, enable analogue computing, thus offering a compelling alternative to traditional digital approaches. We also discuss the challenges and opportunities of developing memristive-CMOS hardware neuromorphic architectures. The co-design of devices, circuits, and algorithms indeed requires to identify and address issues related to device variability, scalability, and system integration.
In conclusion, the synergistic co-development of memristive devices, CMOS circuits, and innovative algorithms can pave the way for intelligent edge devices capable of performing complex cognitive tasks.

Literature

• [1] E. Covi et al. Front. in Neurosci., 15, 611300 (2021).
• [2] D. V. Christensen et al. Neuromorph. Comput. Eng., 2, 022501 (2022).
• [3] E. Chicca et al. Proc. of the IEEE, 102, pp. 1367-1388 (2014).
• [4] D. Ielmini and S. Ambrogio, Nanotech., 31, 092001 (2019).
• [5] A. Amirsoleimani et al. Adv. Intell. Sys., 2, 2000115 (2020).
• [6] E. Covi et al. Neuromorph. Comput. Eng., 2, 012002 (2022).

Lecturer

Dr. Erika Covi is Assistant Professor at the Zernike Institute for Advanced Materials & Groningen Cognitive Systems and Materials Center (Groningen, the Netherlands). She received her PhD in Microelectronics in 2014 from the University of Pavia (Italy), where she worked on designing integrated systems for the characterisation of memristive devices. She also worked at the National Research Council (CNR) of Italy, at Politecnico di Milano (Italy), and at NaMLab gGmbH (Dresden, Germany). Her research interests lie at the intersection of emerging devices, circuit design, and brain-inspired computing. More specifically, they focus on exploiting the intrinsic physical characteristics of memristive devices to reproduce computational primitives of the brain in mixed neuromorphic-memristive systems.

Affiliation: University of Groningen