Neuroscience (DR) Preview
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

Course Introduction and Application Information

Course Code:

SBY6007

Course Name:

Computational Neuroscience and Human-Computer Interaction

Semester:

Spring

Course Credits:

ECTS

Language of instruction:

Turkish

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

Course Level:

PhD

TR-NQF-HE:8. Master`s Degree

QF-EHEA:Third Cycle

EQF-LLL:8. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator:

Dr. Öğr. Üy. GÖKÇER ESKİKURT

Course Lecturer(s):

Asst. Prof. Gökçer Eskikurt

Course Assistants:

Course Objective and Content

Course Objectives:	To provide basic and advanced knowledge on the application of innovative deep learning techniques in health sciences, to support them with computer applications, to introduce the principles of constructing scientific experiment paradigms for research in behavioral neuroscience and to encourage students to construct original scientific research.
Course Content:	Theoretical lectures introducing deep learning algorithms, clinical decision support systems and behavioral neuroscience diseases will be supported by practical computer applications using medical data. A guideline will be defined at the beginning of the semester for scientific research term projects combining the theoretical topics taught and computer applications.

Learning Outcomes

The students who have succeeded in this course;
1) Innovative developments in computer science are important for the generation of intelligent diagnostic support tools from medical data. The development of evidence-based and computational decision support systems for early diagnosis, definitive diagnosis and treatment monitoring in neuro-physiological, neuro-psychological and neuro-psychiatric diseases is a global requirement recommended by the World Health Organization.
2) Within the scope of this course, this topic will be taught and introduced in a broad and integrative range from innovative paradigm designs, computational modeling and deep learning adaptation to clinical goals and scales.

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction Examples of Neural Coding, Simple Linear Regression	-
2)	Evrişim ve Korelasyon 1	-
3)	Firing Rate	-
4)	Convolution and Correlation 2	-
5)	Wiener-Hopf Equations and White Noise Analysis	-
6)	Visual Receptive Fields 1: Basics of the Visual System, Center-surround Receptive Fields, Simple and Complex Cortical Cells	-
7)	Görsel Alıcı Alanlar 2	-
8)	Midterm	-
9)	Operant Matching 1	-
10)	Operant Matching 2	-
11)	Ion Channels, Nernst Equation, Passive Electrical Properties of Neurons	-
12)	The Action Potential, Hodgkin-Huxley Model 1	-
13)	Hodgkin-Huxley Model 2	-
14)	A-type Potassium Channels, Calcium-Dependent Potassium Channels	-

Sources

Course Notes / Textbooks:	An Introductory Course in Computational Neuroscience, İngilizce Baskı Paul Miller, Terrence J. Sejnowski
References:	Fundamentals of Computational Neuroscience, Thomas Trappenberg, Oxford

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2
Program Outcomes
1) 1) To be able to define the basic concepts of neuroscience, understand and express neurophysiological functions of brain and neuroanatomical structures, functional organization of central nervous system and basic principles of normal functioning.	2
2) 2) To have theoretical knowledge about etiopathogenesis of neurological and psychiatric diseases and to have knowledge of neurological and cognitive impairments and central nervous system pathology knowledge in these diseases.
3) 1) To be able to have basic theoretical knowledge about transcranial neuromodulation methods and to use these methods in the field of study, such as radiological and electrophysiological research and investigation methods used in neurological and psychiatric diseases such as electronomyfromography, electroencephalography, evoked potentials and neuroimaging methods.	2
4) 1) Ability to work within the team in the field of neuroscience research		2
5) 1) Transcribe and present the findings and research results verbally or in writing
6) 1) Ability to use communication and computer technologies efficiently in their work.	1
7) 2) Having a sense of ethical responsibility in research.		2
8) 1) Undertake the responsibility of the task alone and carry out independent work.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	1) To be able to define the basic concepts of neuroscience, understand and express neurophysiological functions of brain and neuroanatomical structures, functional organization of central nervous system and basic principles of normal functioning.
2)	2) To have theoretical knowledge about etiopathogenesis of neurological and psychiatric diseases and to have knowledge of neurological and cognitive impairments and central nervous system pathology knowledge in these diseases.	1
3)	1) To be able to have basic theoretical knowledge about transcranial neuromodulation methods and to use these methods in the field of study, such as radiological and electrophysiological research and investigation methods used in neurological and psychiatric diseases such as electronomyfromography, electroencephalography, evoked potentials and neuroimaging methods.
4)	1) Ability to work within the team in the field of neuroscience research	3
5)	1) Transcribe and present the findings and research results verbally or in writing
6)	1) Ability to use communication and computer technologies efficiently in their work.	3
7)	2) Having a sense of ethical responsibility in research.	2
8)	1) Undertake the responsibility of the task alone and carry out independent work.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Workload
Course Hours	16	32
Application	16	32
Study Hours Out of Class	16	32
Midterms	1	40
Final	1	60
Total Workload		196