For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| GBE5057 | Advanced fMRI analysis methods | 3 | 6 | Major | Master/Doctor | - | No | ||
| In this class, we will review some advanced fMRI data analysis methods and try to actually run the analyses on sample datasets. | |||||||||
| GBE5058 | Advanced fMRI analysis methods 2 | 3 | 6 | Major | Master/Doctor | - | No | ||
| In this class, we will review some advanced fMRI data analysis methods and try to actually run the analyses on sample datasets. This will cover new topics that the “Advanced fMRI analysis methods” class could not cover. | |||||||||
| GBE5059 | Neuroscience SeminarⅢ | 3 | 6 | Major | Master/Doctor | - | No | ||
| 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. | |||||||||
| GBE5060 | Advanced Neurophysiology | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| advanced Neurophysiology of neuron characterizing brain function will be explained in molecular levels. Based on this understanding, brain system for cognition and behavior process will be examined. Structural and functional aspects of nervous system will be studied to understand etiology and pathophysiology of neuronal diseases. | |||||||||
| GBE5061 | Trends in Computational Psychiatry | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| his class is designed to introduce graduate students a recently emerging new field called ‘computational neuropsychiatry’, and mainly to educate the students to have a real sense how to bridge between neuroscience and the practical clinical fields. In particular, it aims at introducing trends in various topics regarding to theoretical models, computational approaches and research designs at the junction between cognitive and clinical neurosciences. It will also provide several important applications how the big data science and machine learning techniques have recently shape to advance this field. This class will proceed a periodical and interleaved course system between an instructor’s lecture for background introduction and graduate student’s presentation of seminal papers. This design is to maximize both a trainee’s understanding level of domain-specific knowledge and dicussion participation. The class will be an absolute grading system, determined by weekely article review assignments (1 page of pros-cons comments and summary) and formal presentation (1-2 times per semester) as well as class participation. | |||||||||
| GBE5062 | Trends in network neuroscience | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| In this class, we will discuss both basic theories and up-to-date models in network neuroscience within its historical contexts, in a mixed format of lectures and journal club. They will cover various topics related to cognitive neuroscience, statistics and mathematics based on the seminal papers in the field, while introducing multiscale approaches ranging from single neurons, local circuits and large-scale brain networks to understand their underlying structures and functional dynamics. Moreover, based on this knowledge, we will also study a current trend of modeling approaches for neuronal activities and their interactions along the brain network. This class aims that the graduate students could grasp and apply some of advanced concepts and techniques in network neuroscience to their own proejcts. | |||||||||
| GBE5063 | Neurophysiology seminar | 3 | 6 | Major | Master/Doctor | - | No | ||
| The main goal of this course is to introduce seminal research papers in neurophysiology for graduate students majoring in brain and cognitive science, and to provide insights into ongoing research conducted by students through a deep understanding of the seminal work. In particular, focusing on papers that used non-human primates as animal models, it will cover a wide range of papers from the time when neuroscience is born to the latest. This course will be conducted in the form of a seminar in which students read papers in advance, present key contents, and discuss the historical contributions and limitations of research papers. Through such a seminar format, students will learn and simulate how to present their research in academic environment and how to communicate through the question-and-answer process. Another expe | |||||||||
| GBE5064 | Advanced decision-making neuroscience | 3 | 6 | Major | Master/Doctor | - | No | ||
| The brain receives external sensory inputs, and decides specific actions to obtain nutrients and fun. In this course, students will learn recent advancements in neuroscience of decision-making by paper presentations and discussions. | |||||||||
| GBE5065 | Seminar on Numerical Cognition | 3 | 6 | Major | Master/Doctor | 1-8 | English | Yes | |
| The main goal of this course is to introduce seminal research papers in numerical cognition for graduate students majoring in brain and cognitive science, and to provide insights into ongoing research conducted by students through a deep understanding of the seminal work. It will cover a wide range of papers from human behavior and neuorimaging, developmental psychology, ecology, neurophysiology, and computational models based on deep learning as follows (related major authors are also listed). 1. Human behavior: Burr, Gallistel, Piantadosi, Butterworth, Kadosh 2. Human neuroimaging: Harvey, Dumoulin, Dehaene 3. Developmental psychology: Carey, Spelke, Feigenson 3. Ecology and comparative psychology: Brannon 4. Neurophysiology: Nieder 5. Computational models: Changuex, Zorzi, Nasr, Paik, Park This course will be conducted in the form of a seminar in which students read papers in advance, present key contents, and discuss the historical contributions and limitations of research papers. Through such a seminar format, students will learn and simulate how to present their research in academic environment and how to communicate through the question-and-answer process. Another expected effect of this course is to understand how the seminal studies in numerical have created new scientific ideas and made discoveries given technical limitations and theoretical backgrounds. | |||||||||
| GBE7002 | Advanced Neurobiology Research MethodsⅠ | 3 | 6 | Major | Bachelor/Master/Doctor | - | No | ||
| This class is an advanced class of neuroscience and neurobiology in graduate courses. This class discusses basic neurobiology and basic skills for carrying out graduate level researches. | |||||||||
| GBE7003 | Biomaterials Instrumental Analysis | 3 | 6 | Major | Bachelor/Master/Doctor | English | Yes | ||
| This subject consists of mechanism, applications, managing of Analysis equipments in the field of BME for under graduate students. This subject is mainly focusing on how to use analytical instruments. | |||||||||
| GBE7004 | Introduction of Neurological Disease | 3 | 6 | Major | Bachelor/Master/Doctor | 1-8 | English | Yes | |
| This class introduces basic knowledgements about neurological diseases. As we undergo dramatic advances and enhancement in technology and medicine, our average life expectation becomes extended and our society quickly turns into aging society. Therefore neuro-degenerative diseases prevail and many patients suffer from various types of neurological diseases. In order to develop therapeutic technology, it is important to understand basic mechanism of neurological diseases. This class delivers basic information about the underlying mechanism of various neurological diseases and discuss the possibility of developing new technology to treat them. | |||||||||
| GBE7005 | Principles of Magnetic Resonance Imaging | 3 | 6 | Major | Bachelor/Master/Doctor | 1-8 | - | No | |
| Physical Principles of Magnetic Resonance Imaging (MRI) aims to provide an advanced understanding of essential principles and various applications of MRI from the physical and mathematical perspectives. Physical Principles of Magnetic Resonance Imaging 1 covers the principles of MR signal and image generation and essential concepts and theories of data acquisition and image reconstruction at an advanced level. | |||||||||
| GBE7006 | Basics of Brain Network Modeling | 3 | 6 | Major | Bachelor/Master/Doctor | - | No | ||
| This class aims at educating graduate students about rudiments of biophysical neural network modeling, a computational neuroscience field to study multi-scale biological and mathematical principles of functional dynamics occuring in the brain circuit and network. The weekly lecture will teach biophysics from a single cell level to the inter-regional large-scale network level as well as applications of differential equations on neurodynamic modeling. The class will be offered in both online and offline format, although I highly recommend students to participate in the offline class for active discussion and efficient learning process. | |||||||||
| IPH4001 | Understanding Magnetic Resonance Imaging | 3 | 6 | Major | Bachelor/Master | Intelligent Precision Healthcare Convergence | English | Yes | |
| Understanding Magnetic Resonance Imaging (MRI) aims to provide an understanding of essential principles and concepts of MRI from the physical and mathematical perspectives. It deals with the principles of MR signal and image generation, as well as the essential concepts and theories of data acquisition and image reconstruction. | |||||||||