| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits | Last Updated Date |
| 1 | EE440 | NEURAL ENGINEERING | 3+0+0 | 3 | 5 | 15.05.2026 |
|
Language of Instruction
|
English
|
|
Level of Course Unit
|
Bachelor's Degree
|
|
Department / Program
|
ELECTRICAL-ELECTRONICS ENGINEERING
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Type of Program
|
Formal Education
|
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Type of Course Unit
|
Elective
|
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Course Delivery Method
|
Face To Face
|
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Objectives of the Course
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Understand the close interface between studies of the nervous system and technology
|
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Course Content
|
Overview of the tight interface between neural engineering and neuroethological approaches in the field of neuroscience. Concepts of causal manipulations, such as the control of brain circuits using optics and genetic engineering.
|
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Course Methods and Techniques
|
|
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Prerequisites and co-requisities
|
None
|
|
Course Coordinator
|
None
|
|
Name of Lecturers
|
Asist Prof.Dr. DOOYOUNG HAH
|
|
Assistants
|
None
|
|
Work Placement(s)
|
No
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Recommended or Required Reading
|
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
|
In-Term Studies
|
|
Yarıl yılSonu Sınavı/Dönem Projesinin Başarı Notuna Katkısı
|
1
|
%
30
|
|
Ödev
|
5
|
%
20
|
|
Sunum/Seminer
|
1
|
%
20
|
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Final examination
|
1
|
%
30
|
|
Total
|
8
|
%
100
|
ECTS Allocated Based on Student Workload
|
Activities
|
Total Work Load
|
|
Yazılı Sınav
|
1
|
20
|
20
|
|
Ev Ödevi
|
5
|
1
|
5
|
|
Sınıf İçi Aktivitesi
|
5
|
2
|
10
|
|
Sunum için Hazırlık
|
1
|
20
|
20
|
|
Sunum
|
1
|
3
|
3
|
|
Okuma
|
5
|
4
|
20
|
|
Araştırma
|
5
|
1
|
5
|
|
Yüz Yüze Ders
|
14
|
3
|
42
|
|
Final Sınavı
|
1
|
25
|
25
|
|
Total Work Load
| |
|
Number of ECTS Credits 5
150
|
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
| No | Learning Outcomes |
|
1
| Understand neuroethological approaches |
|
2
| Describe utilization, development and implementation of a wide diversity of neural engineering technologies to the study of the brain |
|
3
| Explain bidirectional road between the two approaches |
Weekly Detailed Course Contents
| Week | Topics | Study Materials | Materials |
| 1 |
Introduction to Neural Engineering
|
|
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| 2 |
Neuroanatomy and Physiology
|
|
|
| 3 |
Bioelectricity and Neural Signaling
|
|
|
| 4 |
Neural Plasticity
|
|
|
| 5 |
Electrophysiology & Recording Techniques
|
|
|
| 6 |
Brain Imaging Modalities
|
|
|
| 7 |
Optogenetics
|
|
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| 8 |
Neural Data Preprocessing
|
|
|
| 9 |
Machine Learning in Neural Engineering
|
|
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| 10 |
Brain-Computer Interfaces (BCI)
|
|
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| 11 |
Neural Prosthetics
|
|
|
| 12 |
Deep Brain Stimulation
|
|
|
| 13 |
Neurorehabilitation Robotics
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|
|
| 14 |
Presentations
|
|
|
Contribution of Learning Outcomes to Programme Outcomes
Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant
https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=79018&lang=en