For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| BSE5001 | Introduction to Cognitive AI | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course will provide an advanced introduction to the emerging field at the intersection of cognitive neuroscience and artificial intelligence, focusing on how insights from human cognition can inform next-generation AI systems, and how AI can in turn advance our understanding of the mind. During the course, we will cover key topics such as reinforcement learning and decision-making, probabilistic reasoning, attention and working memory, abstraction, and metacognition. Students will critically examine computational models inspired by human cognition and deep learning architectures, while discussing their neuroscientific and psychological foundations. Special emphasis will be placed on current debates around interpretability, generalisation, and the scaling limits in AI. The course will also address applications to neuroscience, psychiatry, and human–machine interaction. Through reading, discussion, and group-led presentations, students will gain an in-depth understanding and knowledge to evaluate and implement cognitive models of intelligence, assess their biological relevance for the brain, and consider implications for building robust and human-aligned AI systems. | |||||||||
| BSE5002 | Causal approaches in human neuroscience | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| This course will provide a comprehensive overview of experimental approaches that move beyond correlation to establish causal links between brain activity and behaviour. The course will provide an advanced introduction to contemporary methods including noninvasive brain stimulation (TMS, tDCS, tACS), real-time fMRI, and machine learning-based neurofeedback, alongside advanced computational modelling. It will emphasise the strengths and limitations of each technique, the logic of experimental design, and the ethical considerations of interventions on the human brain. Students will learn how causal approaches are used to test mechanistic theories of perception, decision-making, learning, and emotion, as well as their applications in clinical research and translational neurotechnology. Students will engage with primary research articles, participate in methodological debates, and design their own causal experiment proposal. By the end of the course, participants will be equipped to critically assess causal claims in neuroscience and to integrate these methods into their own research programs. | |||||||||
| BSE5003 | The Neuroscience of Metacognition | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| This class explores recent and ongoing cutting-edge research on the neuroscience of metacognition—the capacity to monitor and regulate one’s own cognitive processes. The course offers an advanced introduction to: i) how metacognition emerges from distributed brain networks and how it might shape learning and decision-making. The course integrates evidence from neuroimaging, computational modelling, lesion studies, and neurostimulation, with a focus on domains such as memory, perception, and value-based choice. ii) special attention will be given to current debates, including whether metacognition is domain-general or domain-specific, how metacognitive dysfunction might contribute to psychiatric and neurological disorders, and whether we can develop artificial intelligence with metacognitive functions. Students will critically evaluate recent empirical and theoretical work, engage with the literature, and develop skills in synthesising findings across methods and disciplines. Through presentations and discussions, participants will gain a deep understanding of state-of-the-art approaches to metacognition and their implications for cognitive neuroscience, psychiatry, and artificial intelligence. | |||||||||
| BSE5004 | Seminar on Systems Neuroscience | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This course aims to conduct an in-depth analysis and discussion of major scholarly articles in the field of systems neuroscience through a journal club format. Each week, students will present recent research papers, while other participants are expected to read the assigned articles in advance and actively contribute to discussion and critique. Through this process, students will gain a broader understanding of the background and latest research trends in systems neuroscience and expand their knowledge of various experimental methodologies, such as electrophysiology, calcium imaging, and optogenetics. Ultimately, this course is designed to help students critically evaluate research papers, assess the validity of experimental designs and analytical methods, and identify both the academic significance and the limitations of research findings. Through these activities, participants will cultivate critical thinking skills, communication abilities, and research creativity necessary for conducting independent research in the future. | |||||||||
| BSE5005 | Recent trends of cognitive computatioanl neuroscience | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| Cognitive neuroscience is an integrative field that spans psychology, artificial intelligence, and physics. Researchers from diverse backgrounds publish papers at a rapid pace, each pursuing different aims. In this environment, the course will guide students to analyze where recent papers sit within their historical context, what impact they may have, and what core innovationsthey introduce. By doing so, the course aims to cultivate students’ discernmentand big-picture understanding of the field. | |||||||||
| BSE5006 | Seminar on neurophysiology and neuropathology | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| This course is conducted in a journal club format, focusing on seminal and cutting-edge research papers in neurophysiology and brain disorders. Students are required to thoroughly read the assigned papers prior to each session. One designated presenter delivers a detailed, critical analysis of the paper’s background, methods, results, and conclusions, after which all participants actively engage in in-depth discussion. Through repeated practice, students develop the ability to rigorously scrutinize experimental data, identify methodological strengths and limitations, and critically evaluate interpretations and claims. The selection of papers traces both historical milestones and the latest advances in the field, enabling students to understand the evolution of key concepts and current research frontiers in neurophysiology and neurological diseases. Students also become proficient in state-of-the-art methodologies and experimental approaches used to test contemporary hypotheses. By fostering an environment that emphasizes constructive critique over uncritical acceptance of published findings, the course trains students to think analytically and independently. Ultimately, participants acquire the critical insight and scientific rigor necessary to formulate, refine, and robustly test their own research hypotheses. | |||||||||
| BSE5007 | Neuroscience SeminarⅠ | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| This course is composed of series of seminars from leading researchers in the field of biomedical engineering. Speakers from medical device, biomaterials, neuroimaging, and neuroengineering will be invited. | |||||||||
| BSE5008 | Neuroscience SeminarⅡ | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| Neuroscience seminar II class covers the most advanced and cutting-edge science in neuroscience field. The students have a seminar of the experts and discuss together to understand the top research and the fields. In addition, students have a time to read and discuss papers related to the seminar topics. This class will help broaden the student's vision. | |||||||||
| BSE5009 | Artificial Human Intelligence | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| This course will provide an advanced introduction to the emerging field of human artificial intelligence. At the intersection of artificial intelligence, cognitive neuroscience and psychology, the course will focus on how insights from human cognition can inform in-silico study of AI systems, and how these experiments with AI can advance our understanding of the human mind. During the course, we will cover key topics such as perception, introspection and reasoning in AI. Through reading of published work, student-led presentations, and in class discussions, participants will gain in-depth understanding and knowledge to critically evaluate claims about AI and human alignment, better understand the nascent field of NeuroAI, but also see new ways of studying human behaviour beyond typical experiments. | |||||||||
| BSE7001 | Introduction to Systems Neuroscience | 3 | 6 | Major | Bachelor/Master/Doctor | English | Yes | ||
| This course covers the field of systems neuroscience, which investigates how the signals generated by numerous neurons distributed across different regions of the animal brain contribute to encoding sensory information, thought, memory, and the modulation of behavior. Systems neuroscience focuses not only on the coding properties of individual neurons but also on unraveling the principles by how population-level activity patterns represent and process information. Recent advances in neuro-techniques enable the simultaneous recording of activity from hundreds to thousands of neurons in behaving animals. These methods have made it possible to analyze brain function beyond the level of single cells, extending to populations and networks of neurons. Such technological progress has provided a new foundation for systematically understanding the mechanisms underlying complex brain functions and information processing. This course introduces both traditional approaches in systems neuroscience and the latest research trends and analytical methodologies to undergraduate, master‘s, and doctoral students. The aim is to help students develop a balanced perspective, ranging from the fundamental concepts of systems neuroscience to its most recent applied research. | |||||||||
| BSE7002 | Brain Cell Biology | 3 | 6 | Major | Bachelor/Master/Doctor | English | Yes | ||
| This course provides a systematic overview of cell biology for neuroscience researchers, from foundational knowledge to the latest insights. The course covers the gross anatomy and microscopic anatomy of the brain. Students first learn the diverse structural and functional classifications of neurons, covering morphological features of the soma, dendrites, and axons, as well as their electrophysiological properties. Glial cells, long regarded merely as supportive elements, are now recognized through recent research as active, essential participants in brain function. The course explores the structural characteristics and functional diversity of astrocytes, microglia, and other glia, incorporating cutting-edge findings. The neurovascular unit is examined in depth along with mechanisms of cerebral blood flow regulation and their tight coupling with neuronal activity. Pathological changes in each cell type are studied in major neurological disorders such as Alzheimer’s disease, Parkinson’s disease, and stroke, linking cellular-level understanding to disease mechanisms and therapeutic strategies. Modern core methodologies are introduced. Through this course, students will gain an integrated understanding of the structure and function of diverse brain cells, acquire the ability to approach their own research questions from a cell-biological perspective, and develop the skills to design original experiments, ultimately growing into independent researchers. | |||||||||
| BSE7003 | Optical imaging for neuroscientist | 3 | 6 | Major | Bachelor/Master/Doctor | Korean | Yes | ||
| This course provides a systematic introduction to optical imaging methodologies for neuroscience researchers. It comprehensively covers optical imaging technologies—a core tool in modern neuroscience—ranging from classical techniques to cutting-edge technologies, with balanced instruction on the scientific principles and practical applications of each method. Students will understand the basic operating principles of diverse imaging techniques, from foundational methods such as brightfield and fluorescence microscopy to advanced technologies including confocal microscopy, two-photon microscopy, light-sheet microscopy, and super-resolution microscopy. Beyond simply cataloging techniques, the course develops critical judgment through comparative analysis of each method's resolution limits, optical properties, and effects on biological specimens, enabling students to independently select the most appropriate imaging tools for their research questions. Students will perform hands-on analysis using real imaging data. Additionally, they will learn about the development process and applications of various recently developed fluorescent proteins. Through analyzing and discussing research employing state-of-the-art optical imaging techniques, students will cultivate their ability to select appropriate imaging methods for hypothesis testing and synthesize experimental results. Through this comprehensive training, students will grow into independent, self-directed researchers. | |||||||||
| DHC5041 | Neuroscience for Medical AI | 3 | 6 | Major | Master/Doctor | 1-4 | Digital Health | Korean | Yes |
| This class will introduce basic neuroanatomy, higher cortical function, neuroimaging, and major neurological diseases, therefore let students understand how digital health (bigdata, genomics, and AI) can be applied to clinical practice in neuroloy. | |||||||||
| DHC7005 | Mathematical Statistics for Healthcare AI | 3 | 6 | Major | Bachelor/Master/Doctor | 1-4 | Digital Health | Korean | Yes |
| This course focuses on equipping students with essential skills in basic mathematical statistics for the use of artificial intelligence in healthcare. It aims to cultivate the ability to effectively analyze and interpret medical data using mathematical and statistical techniques. Starting from the mathematical and statistical concepts that are the foundation of machine learning and artificial intelligence, this course covers core topics such as big data and machine learning, computational predictive modeling, and medical AI. Through this course, students will strengthen their foundation in mathematical statistics and acquire a strong ability to grow and play an essential role in the field of medical AI. | |||||||||
| ERC7003 | AI-Based Engineering Research Methods | 1 | 2 | Major | Bachelor/Master/Doctor | Engineering | Korean | Yes | |
| AI-Based Engineering Research Methods is a one-credit course for undergraduate, master’s, and doctoral students across disciplines. It builds research competencies for the AI era by treating AI not as a simple tool, but as a research assistant to be used critically, transparently, and responsibly. The course covers changes in methodology, principles, and limits of generative AI; topic exploration; literature search; review of prior studies; gap identification; questions and hypotheses; research design; data collection; analysis planning; visualization; proposal writing; AI-use documentation; ethics; and data security. It also addresses risks such as hallucinations, bias, fabricated citations, outdated information, privacy concerns, and data leakage. Students apply AI tools by developing topics, search strategies, literature maps, research questions, and data collection and analysis plans. The final outcome is an AI Research Portfolio documenting topic exploration, gap identification, methodology, output verification, ethical review, and reflection. The course supports responsible AI-assisted research while preserving critical thinking, integrity, and the researcher’s role. | |||||||||