Author: Philipp Wicke

Lecturer: Achim Stephan
Fields: Philosophy (of emotions)

Content

The course offers (1) an introduction to affective phenomena such as emotions and moods, in general; (2) it also introduces to the key notions of situated affectivity such as 4E, user-resource interactions, mind shaping, mind invasion, and scaffolds; (3) next, we will apply these notions to the study of cases of mind invasion from individuals to nations; (4) finally, we will explore whether we can trust our (own) emotions.

Literature

• Stephan, Achim, Sven Walter & Wendy Wilutzky (2014). Emotions Beyond Brain and Body. Philosophical Psychology 27(1), 65-81.
• Stephan, Achim (2017). Moods in Layers. Philosophia 45, 1481-1495. doi: 10.1007/s11406-017-9841-0
• Stephan, Achim & Sven Walter (2020). Situated Affectivity. In: T. Szanto & H. Landweer (eds.) The Routledge Handbook of Phenomenology of Emotion. Abingdon: Routledge, pp. 299-311.
• Coninx, Sabrina & Achim Stephan (2021). A Taxonomy of Environmentally Scaffolded Affectivity. Danish Yearbook of Philosophy 54, 38-64. doi: https://doi.org/10.1163/24689300-bja10019

Lecturer

Achim Stephan’s current research is mainly focused on human affectivity, particularly from a situated perspective. He was head of the Philosophy of Mind and Cognition group at the Institute of Cognitive Science at Osnabrück University (2001-2023) and co-speaker of the bi-local DFG-funded research training group on Situated Cognition (2017-2023). In his PhD thesis, he worked on meaning theoretic aspects in the psychoanalysis of Sigmund Freud (1988); his habilitation thesis covers various theories of emergence and their applications (1998). From 2012 to 2015, he was president of the German Society for Analytic Philosophy (GAP); from 2017 to 2020 he was president of the European Philosophical Society for the Study of Emotions (EPSSE).

Affiliation: Osnabrück University, Institut of Cognitive Science

Lecturer: Andreas Stöckel
Fields: Computational Neuroscience / Neuromorphic Computing

Content

While the brain does perform some sort of computation to produce cognition, it is clear that this sort of computation is wildly different from traditional computers, and indeed also wildly different from traditional machine learning neural networks. In this course, we identify the type of computation that biological neurons are good at (in particular, dynamical systems), and show how to build large-scale neural models that realize basic aspects of cognition (sensorimotor, memory, symbolic reasoning, action selection, learning, etc.). These models can either be made to be biologically realistic (to varying levels of detail) or mapped onto energy-efficient neuromorphic hardware.

Literature

• Eliasmith, C. and Anderson, C. (2003). Neural engineering: Computation, representation, and dynamics in neurobiological systems. MIT Press, Cambridge, MA.
• Eliasmith, C. et al., (2012). A large-scale model of the functioning brain. Science, 338:1202-1205.
• Stöckel, A. et al., (2021). Connecting biological detail with neural computation: application to the cerebellar granule-golgi microcircuit. Topics in Cognitive Science, 13(3):515-533.
• Dumont, N. S.-Y. et al., (2023) Biologically-based computation: How neural details and dynamics are suited for implementing a variety of algorithms. Brain Sciences, 13(2):245, Jan 2023.

Lecturer

Andreas Stöckel received his PhD in computer science at the University of Waterloo, Canada, in 2021. During his PhD, his research focused on integrating biological detail into the Neural Engineering Framework, a method for constructing large-scale models of neurobiological systems. His work specifically focused on harnessing nonlinear synaptic interactions and temporal tuning as computational resources. Today, he is a senior research scientist at Applied Brain Research Inc., where he co-designed the TSP1 time-series processor, a low-power neural-network accelerator chip that utilizes some of the techniques that he investigated during his PhD.

Affiliation: Applied Brain Research
Homepage: https://compneuro.uwaterloo.ca/people/andreas-stoeckel.html

Lecturer: Heike Felzmann
Fields: AI Ethics, Philosophy

Content

To be announced.

Literature

• To be announced

Lecturer

Heike Felzmann

Affiliation: –
Homepage: – 

Lecturer: Robin Bekrater-Bodmann
Fields: Psychology, Neuroscience, Rehabilitation

Content

The amputation of a limb represents the most serious breach of a person’s physical integrity and requires extensive psychological and behavioral adjustment. In addition, many people are confronted with special perceptual phenomena after an amputation: the presence of a phantom of the lost body part, which in many cases is experienced as painful. Physiological and brain imaging results show that characteristic changes in the central nervous system correlate with the experience of pain. The perceived restoration of physical integrity, e.g. through prostheses, mirrors, or virtual reality, can normalize the functioning of the central nervous system which can have positive effects on the perception of pain. In this workshop, participants learn the sensory basics of bodily self-experience firsthand. The psychobiological changes after an amputation and their connections to the experience of post-amputation pain are examined. And finally, the implications of the findings for tailored treatments are discussed.

Literature

• Bekrater-Bodmann, R. (2021). Factors associated with prosthesis embodiment and its importance for prosthetic satisfaction in lower limb amputees. Frontiers in Neurorobotics, 14, 604376.
• Bekrater-Bodmann, R., Reinhard, I., Diers, M., Fuchs, X., & Flor, H. (2021). Relationship of prosthesis ownership and phantom limb pain: results of a survey in 2383 limb amputees. Pain, 162(2), 630-640.
• Foell, J., Bekrater‐Bodmann, R., Diers, M., & Flor, H. (2014). Mirror therapy for phantom limb pain: brain changes and the role of body representation. European Journal of Pain, 18(5), 729-739.
• Fuchs, X., Flor, H., & Bekrater-Bodmann, R. (2018). Psychological factors associated with phantom limb pain: a review of recent findings. Pain Research and Management, 2018(1), 5080123.
• Rothgangel, A., & Bekrater-Bodmann, R. (2019). Mirror therapy versus augmented/virtual reality applications: towards a tailored mechanism-based treatment for phantom limb pain. Pain Management, 9(2), 151-159.

Lecturer

I studied Psychology at the Braunschweig Technical University and completed my studies with a diploma in 2007. From 2008 to 2023, I worked at the Central Institute of Mental Health in Mannheim: first as a doctoral student and then as a postdoc at the Institute of Cognitive and Clinical Neuroscience and the Department of Psychosomatic Medicine and Psychotherapy, interrupted by a stay abroad at the Department of Psychology, Royal Holloway, London University, in 2017. Since 2023, I have been working as full professor for ‘Psychobiology of chronic pain’ at the Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, Aachen University. My team and I are interested in the neurobiological mechanisms underlying bodily self-experiences in chronic pain and the interaction between experimentally altered body perception and central nociceptive processes. We take advantage of and develop paradigms for the induction of bodily illusions and assess its cognitive, behavioral and (neuro)physiological effects, either in our pain lab or in the environment of a magnetic resonance imaging scanner.

Affiliation: Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
Homepage: https://www.ukaachen.de/kliniken-institute/klinik-fuer-psychiatrie-psychotherapie-und-psychosomatik/team/alle-personen-a-g/bekrater-bodmann-robin/

Lecturer: Marius Klug, Michael Bressler
Fields: Neuroscience, Computer Science, Cognitive Science, Biology, Human-Computer Interaction

Content

This is a hands-on course on the fascinating world of physiological user interfaces. A quick introduction to the body and brain signals that can be measured—such as heart rate, muscle activity, and brain activity—plus a short tutorial on the provided Unity game engine prefabs will get you up to speed. Equipped with this, the course will be a series of supervised hands-on hackathon-style practical sessions where you will explore how various body signals can be harnessed to create innovative applications. We provide devices like the Polar H10, Myo Armband, and Muse S, which can read different physiological signals. Teams of a maximum of three participants will then ideate, design, and prototype interactive systems that use the provided body and mind signals as input for applications in the Unity game engine. The applications can be for desktop, mobile, and even XR platforms (we provide a few Meta Quest devices) and they should be designed to be entertaining and fun. The focus is on fostering creativity and collaboration while gaining deeper insights into how our bodies can interact with technology. Provided and generated software is expected to be uploaded to public code repositories. Finally, teams will pitch their ideas and demo applications to a jury and to the other participants. Aspiring participants are encouraged to visit the Unity tutorials at https://learn.unity.com/pathway/unity-essentials.

Literature

• https://github.com/MariusKlug/mind-and-body-UIs-hackathon
• https://learn.unity.com/pathway/unity-essentials
• Gramann, K. & Schandry, R. (2009). Psychophysiologie (4. Auflage). Basel, Switzerland: Beltz
• Andreassi, J. (2007). Psychophysiology: Human Behavior and Physiological Response (5th Edition). New York, NY, USA: Psychology Press

Lecturer

Marius Klug studied cognitive science in Tübingen and was already in contact with EEG as a measurement method and brain-computer interface during that time. He subsequently earned his doctorate in the field of mobile brain research under Prof. Klaus Gramann at TU Berlin. There, he extensively dealt with EEG analysis methods and virtual reality as an experimental method. Specifically, the application of EEG in a mobile context, the cleaning of data, and their interpretation in conjunction with other measurements, such as body and eye movements, were the focus of the research. The continuation of this research can now be found at BTU in the form of the practical use of psychophysiological measurement methods as an interface for real-time applications.

Affiliation: BTU Cottbus-Senftenberg
Homepage: https://discord.gg/7MJjQ3f

Michael Bressler finished his Master’s degree in Information Technology at the Vienna University of Technology with a focus on human-computer interfaces and user interface design. In his research, he mainly focuses on computer-assisted rehabilitation, virtual and augmented reality, and serious games for health.

Affiliation: BG Klinik Tuebingen, Clinic for Hand, Plastic, Reconstructive and Burn Surgery, University of Tuebingen, Tuebingen, Germany
Homepage: https://michaelbressler.at/

Lecturer: Margot Ernst
Fields: Neuropharmacology

Content

The molecular foundation for nervous system (NS) function will be introduced in part 1, ranging from the smallest vital units (water, salt, fat) to the largest biochemicals (DNA, RNA, proteins). Selected so-called transmitter systems will be introduced in order to convey a feeling for the molecular complexity and how food and drugs act on the NS. Molecules relevant for nociception and pain will be in the focus.
In part 2, the vast complexity of cells that comprise the NS will be introduced, alongside with the genetic basis of the cellular pre- and postnatal development. The basis for adapting to the environment is provided by cellular and molecular traces made by physical, chemical and complex (e.g. social) stimuli that act on the organism. Cellular key players that contribute to pain perception and memory will be highlighted.
In part 3, the small parts will be put together and selected so-called “systems” will be introduced. Nociception and pain, and the concept of “aversive responses” will be examined in some detail, as well as the neural correlates of pain memory and phantom pain.
In part 4, selected means to interact with the NS are examined, and some of the challenges and chances for future development highlighted. Progressing from non invasive to invasive (e.g. implants), various selected means to influence nociception and pain perception, as well as the aversive reactions to painful stimuli, illustrate some basic principles.

Literature

• Sapolsky, “Behave” ISBN 978-1-59420-507-1
• Bear et al., Exploring the Brain, ISBN 10: 0781760038

Lecturer

Margot Ernst holds a PhD from Georgia Tech (US) and currently works at the Medical University of Vienna. Her research focus is on the neuropharmacology of substances that act on GABA-A receptors.

Affiliation: Medical University of Vienna
Homepage: https://www.meduniwien.ac.at/web/forschung/researcher-profiles/researcher-profiles/detail/?res=margot_ernst&cHash=4e3cbaeed73c322fd75003c0ef8d6061

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