BYF718 - BEHAVIOURAL and COMPUTATIONAL NEUROSCINECE
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| BEHAVIOURAL and COMPUTATIONAL NEUROSCINECE | BYF718 | Any Semester/Year | 2 | 2 | 3 | 8 |
| Prequisites | Mülakat | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Drill and Practice Project Design/Management | |||||
| Instructor (s) | ||||||
| Course objective | ||||||
| Learning outcomes |
| |||||
| Course Content | ||||||
| References | ||||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Futuristic technological developments and brief medical applications |
| Week 2 | General information on artificial intelligence, machine learning, zero-shot learning, and deep learning algorithms. |
| Week 3 | Basic principles and innovative developments in deep learning |
| Week 4 | Integration between decision support systems and deep learning |
| Week 5 | Behavioral neuroscience diseases and clinical criteria |
| Week 6 | Behavioral neuroscience disorders and clinical scales |
| Week 7 | Research oriented behavioral neuroscience paradigms and associated medical data formats |
| Week 8 | Domain-based transformations and corresponding deep learning applications driven by medical 1-D recordings |
| Week 9 | Domain-based transformations and corresponding deep learning applications driven by medical 1-D recordings |
| Week 10 | 2-Dimensional transformations and corresponding deep learning applications for diagnostic 2-D medical data |
| Week 11 | 2-Dimensional transformations and corresponding deep learning applications in 2-D medical data |
| Week 12 | Goal-oriented innovative experimental paradigm design principles |
| Week 13 | Constraints and opportunities in developing research-oriented experimental paradigms and corresponding deep learning methods with the aim of advanced level scientific research in behavioral neuroscience |
| Week 14 | Constraints and opportunities in developing research-oriented experimental paradigms and corresponding deep learning methods with the aim of advanced level scientific research in behavioral neuroscience |
| Week 15 | Term projects |
| Week 16 | Written Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | ||
| Laboratory | ||
| Application | ||
| Field activities | ||
| Specific practical training | ||
| Assignments | ||
| Presentation | ||
| Project | ||
| Seminar | ||
| Midterms | ||
| Final exam | ||
| Total | ||
| Percentage of semester activities contributing grade succes | ||
| Percentage of final exam contributing grade succes | ||
| Total | ||
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 0 | ||
| Laboratory | 0 | ||
| Application | 0 | ||
| Specific practical training | 0 | ||
| Field activities | 0 | ||
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 0 | ||
| Presentation / Seminar Preparation | 0 | ||
| Project | 0 | ||
| Homework assignment | 0 | ||
| Midterms (Study duration) | 0 | ||
| Final Exam (Study duration) | 0 | ||
| Total Workload | 0 | 0 | 0 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Graduates have knowledge related to the biophysical principles underlying all processes of life at the level of cell/tissue/organ/system | X | ||||
| 2. Has an ability use his/her higher intellectual processes such as critical thinking, problem solving decision development during his/her education period | X | ||||
| 3. Can take part in some research activities to contribute to the solution of a problem in the field of biophysics | X | ||||
| 4. Awaring of the fact that biophysics is a multidisciplinary field, follows the developments in other branches of the Medical&Basic Sciences | X | ||||
| 5. Can use computer software and laboratory equipment to produce appropriate stimulus, acquire the biological signals under the ideal conditions, quantitatively analyse the raw data | X | ||||
| 6. Acquired knowledge at an expertise level in statistical methods. Can choose the most suitable method for his/her research | X | ||||
| 7. Is aware of the importance of the ethical rules and regulations and perform laboratory research as defined by the GLP, Bio-Safety principles | X | ||||
| 8. Has the capacity of successfully preparing and presenting the report of the research work he/she takes part in, publishing at least one manuscript | X | ||||
| 9. Follows the activities of the national&international organizations related to his/her expertise and takes part in them | X | ||||
| 10. Shares the knowledge he/she acquired from biophysics with partners from all parts of the society; contributes to the formation of the knowledge-based society | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest