Neuroscience (Master) (with Thesis) Preview
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code:

SBY5010

Course Name:

Computational Neuroscience

Semester:

Fall

Course Credits:

ECTS

Language of instruction:

Turkish

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

Course Level:

Master

TR-NQF-HE:7. Master`s Degree

QF-EHEA:Second Cycle

EQF-LLL:7. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator:

Dr. Öğr. Üy. GÖKÇER ESKİKURT

Course Lecturer(s):

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 to Matlab and Simulink
2)	Population models - alpha rhythm model of Lopes da Silva
3)	Introduction to Neuron, building simple models using graphical user interfaces
4)	Theory - resting membrane potential
5)	Simulation of resting membrane potential
6)	Theory - action potential
7)	Simulations of action potential
8)	Membrane currents and their influence on neuron's firing pattern
9)	Theory - synaptic transmission
10)	Simulations of synaptic transmission
11)	Simple models in hoc language
12)	NMDL language
13)	Object oriented programming in hoc
14)	Realistic neuronal neurons

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) 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.	3	3
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	3
3) 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.	3	3
4) Ability to use communication and computer technologies efficiently in their work.	2	3
5) Transcribe and present the findings and research results verbally or in writing.	1	2
6) Ability to work within the team in the field of neuroscience research
7) Undertake the responsibility of the task alone and carry out independent work.
8) Having a sense of ethical responsibility in research.	2	2

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
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.	3
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
3)	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.	3
4)	Ability to use communication and computer technologies efficiently in their work.	2
5)	Transcribe and present the findings and research results verbally or in writing.
6)	Ability to work within the team in the field of neuroscience research
7)	Undertake the responsibility of the task alone and carry out independent work.
8)	Having a sense of ethical responsibility in research.	2

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	3	% 30
Midterms	1	% 25
Final	1	% 45
total		% 100
PERCENTAGE OF SEMESTER WORK		% 55
PERCENTAGE OF FINAL WORK		% 45
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Workload
Course Hours	14	28
Study Hours Out of Class	15	155
Homework Assignments	5	5
Midterms	1	2
Final	1	2
Total Workload		192