1. SECOND CYCLE - MASTER'S DEGREE
  2. THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
  3. COMPUTER ENGINEERING DEPARTMENT
  4. 1281 Computer Engineering
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
CENG 532COMPUTATIONAL AND COGNITIVE NEUROSCIENCE3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Master's Degree
Course Type Elective
Course Objective The aim of this course is to evaluate the necessary mathematical models and statistical analysis methods for coding and decoding of neural information
Course Content The focus of investigations is on the phenomena of cognition – perception, attention, memory, reasoning, thinking and behaviour – from an interdisciplinary perspective: anthropology, artificial intelligence, biology, linguistics, neuroscience, philosophy, and psychology have contributed to the course's development as core disciplines.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Explains the principles of neural information encryption.
2Defines the brain regions and structure of neurons.
3Expresses the computational principles of neural networks in mathematical formulas
4Interpret statistical relationship between experimental data and behaviour and / or stimulation
5Compares the experimental data with the dynamics of the simulated neural network
6predicts methods for improving the realism of neural network simulations.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 001            
LO 002            
LO 003            
LO 004            
LO 005            
LO 006            
Sub Total            
Contribution000000000000

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14570
Assignments5840
Mid-terms11515
Final examination12828
Total Work Load

ECTS Credit of the Course





195

7,5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2021-2022 Spring1MERİÇ ÇETİN
Details 2019-2020 Spring1MERİÇ ÇETİN


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
CENG 532 COMPUTATIONAL AND COGNITIVE NEUROSCIENCE 3 + 0 1 Turkish 2021-2022 Spring
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Assoc. Prof. Dr. MERİÇ ÇETİN mcetin@pau.edu.tr MUH A0257 %
Goals The aim of this course is to evaluate the necessary mathematical models and statistical analysis methods for coding and decoding of neural information
Content The focus of investigations is on the phenomena of cognition – perception, attention, memory, reasoning, thinking and behaviour – from an interdisciplinary perspective: anthropology, artificial intelligence, biology, linguistics, neuroscience, philosophy, and psychology have contributed to the course's development as core disciplines.
Topics
Materials
Materials are not specified.
Resources
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes