Syllabus ( BENG 520 )
| Basic information | ||||||
| Course title: | Computational Neuroscience | |||||
| Course code: | BENG 520 | |||||
| Lecturer: | Prof. Dr. Muhammet UZUNTARLA | |||||
| ECTS credits: | 7.5 | |||||
| GTU credits: | 3 (3+0+0) | |||||
| Year, Semester: | 1/2, Fall | |||||
| Level of course: | Second Cycle (Master's) | |||||
| Type of course: | Area Elective | |||||
| Language of instruction: | English | |||||
| Mode of delivery: | Face to face | |||||
| Pre- and co-requisites: | None | |||||
| Professional practice: | No | |||||
| Purpose of the course: | The 100 billion nerve cells in the human brain support complex cognitive processes by gradually processing large amounts of information from the outside world. Each stage in the highly advanced visual perception process is a computational unit that extracts visual features for object identification from images falling on the retina. Therefore, localizing the different stages of the visual cortex in the brain and determining the types of information processed at each stage are among the most important neuroscience problems of our time. We develop innovative functional magnetic resonance imaging (fMRI) and machine learning techniques to answer these important questions. With the unique technology we have developed, the neural responses that occur during natural vision, hearing, and language perception can be detected with unprecedented sensitivity. In addition to the detailed examination of the structure and function of the brain's subsystems, these techniques also make it possible to read the brain - to analyze the content of perception back from brain activity. An exemplary field of application is the extraction and mapping of mathematical models of the visual cortex in the human brain. Although real-world scenes are mixed with many different objects, humans are extremely adept at finding target objects in natural settings and quickly switching their attention between different targets. The precise neural mechanisms mediating this extraordinary ability are not yet known. Previous studies have reported relatively simple mechanisms that improve the quality of brain activity evoked by the objects involved, without affecting the way information is represented in each brain region. However, as there are a limited number of cortical neurons, it seems highly unlikely that all brain regions hold stable representations regardless of behavioral demands. To examine the nature of neural representations during image search, we develop a powerful computational modeling technique to describe complex natural movies and the relationship between visual information and brain activity. We analyze the correlations between high-dimensional imaging data and high-dimensional visual stimuli at the same level with advanced machine learning techniques. This course aims to evaluate the mathematical models and statistical analysis methods required for the encryption and decoding of neural information. |
|||||
Learning outcomes
|
||||||
Upon successful completion of this course, students will be able to:
-
Use the terminology associated with the anatomy, physiology and functional organizations of the neural systems
Contribution to Program Outcomes
- Develop their knowledge in the fields of Bioengineering and Biotechnology at the level of expertise based on undergraduate qualifications.
Method of assessment
- Written exam
-
Model the electrical behaviors of neurons at the cellular level and population level through computational approaches
Contribution to Program Outcomes
- Define, model and solve engineering problems in the field of bioengineering at a higher level.
- Use up-to-date techniques and computational tools for advanced engineering applications.
- Solve problems that require expertise in the field of bioengineering by using scientific research methods.
Method of assessment
- Written exam
-
Explain the concept of synaptic plasticity and the neurophysiology of cognitive brain functions
Contribution to Program Outcomes
- Develop their knowledge in the fields of Bioengineering and Biotechnology at the level of expertise based on undergraduate qualifications.
- Construct an experiment for a problem in the field of Bioengineering and Biotechnology, develop a solution method, solve it, evaluate the results and to have synthesis skills.
- Have investigative, productive and entrepreneurial capacity by using high-level mental processes such as creative and critical thinking, taking initiative and decision making.
Method of assessment
- Written exam
|
Assessment
|
||
| Method of assessment | Week number | Weight (%) |
| Mid-terms: | 6 | 30 |
| Other in-term studies: | - | 0 |
| Project: | 5-14 | 20 |
| Homework: | - | 0 |
| Quiz: | - | 0 |
| Final exam: | 16 | 50 |
| Total weight: | (%) |
|