PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
CENG 478	COMPUTATIONAL NEUROSCIENCE	3 + 0	8th Semester	5

COURSE DESCRIPTION

Course Level	Bachelor's Degree
Course Type	Elective
Course Objective	This course aims to provide a mathematical introduction to neural coding and neural dynamics.
Course Content	Basic assumption of computational neuroscience / Human brain physiology and function / Neurons, synaptic interaction models / Hodgkin-Huxley neuron model, Izhikevich neuron model, Hindmarsh–Rose neuron model, FitzHugh–Nagumo neuron model / Neural network simulation / Supervised, unsupervised and reinforcement learning / Computational models and applications in neuroscience / Prediction methods and applications
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Examines the way information is encoded in neural activity and other dynamic variables of the brain
2	Focuses on the biophysics of neurons and synaptic interaction patterns.
3	Focuses on the dynamics of networks related to phenomena arising from interactions between neurons.
4	Attempts to characterize how dynamic variables evolve over time.

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11	PO 12
LO 001	5	2	5	5
LO 002	5				5
LO 003	5	3	5	5					5
LO 004	5	3	5	5	5
Sub Total	20	8	15	15	10				5
Contribution	5	2	4	4	3	0	0	0	1	0	0	0

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	2	28
Mid-terms	1	30	30
Final examination	1	30	30
Total Work Load ECTS Credit of the Course			130 5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2023-2024 Spring	1	MERİÇ ÇETİN
Details	2022-2023 Spring	1	MERİÇ ÇETİN

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
CENG 478	COMPUTATIONAL NEUROSCIENCE	3 + 0	1	Turkish	2023-2024 Spring

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Assoc. Prof. Dr. MERİÇ ÇETİN	mcetin@pau.edu.tr		MUH A0435	%70

Goals		This course aims to provide a mathematical introduction to neural coding and neural dynamics.
Content		Basic assumption of computational neuroscience / Human brain physiology and function / Neurons, synaptic interaction models / Hodgkin-Huxley neuron model, Izhikevich neuron model, Hindmarsh–Rose neuron model, FitzHugh–Nagumo neuron model / Neural network simulation / Supervised, unsupervised and reinforcement learning / Computational models and applications in neuroscience / Prediction methods and applications

Topics

Weeks	Topics
1	Introduction to computational neuroscience
2	Human brain physiology and function, brain regions, neurophysiology
3	Neuron types, synaptic interaction models, neuron simulators
4	Hodgkin-Huxley neuron model, Izhikevich neuron model, Hindmarsh–Rose neuron model, FitzHugh – Nagumo neuron model
5	Neurological, neuropsychological, neurodegenerative diseases and their mathematical models
6	Neuropsychopharmacology and smart drug use (drug dosing), animal behavior model
7	Computational models in cognitive science
8	Midterm
9	Artificial intelligence methods
10	Neural network simulation
11	Supervised, unsupervised and reinforced learning
12	Bayesian decision theory, Kalman filters
13	Predictive methods and observer designs
14	Predictive methods and observer designs

Materials

Materials are not specified.

Resources

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student