Subject

Introduction to Computational Neuroscience

Course Title

Introduction to Computational Neuroscience
Introduction to Computational Neuroscience

Code

m23_w_203

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

Second cycle

Academic year / semester

9 / Winter

Number of ECTS credits

Teacher

Ilinka Ivanoska

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

Студентот ќе биде оспособен за користење на пресметувачки техники и математичките
модели за моделирање и анализа на невронските системи.

11.

Course content

Невронско кодирање и декодирање: статистика на нервните импулси, реверзна корелација
и визуелно рецептивни полиња, невронско декодирање, теорија на информации. Неврони
и невронски кола: невроелектроника, проводливост и морфологија, мрежни модели.
Адаптација и учење: пластичност и учење, методи на учење, репрезентирачко учење.

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, invited guest lecturers, independent preparation and defense of a project assignment and seminar paper, learning in an electronic environment (forums, consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	60 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	0 hours

16.

Other forms of activities

16.1.	Project assignments	45 hours
16.2.	Independent assignments	45 hours
16.3.	Home study	30 hours

17.

Assessment method

17.1.	Tests	15 points
17.2.	Seminar paper / project (presentation: written and oral)	45 points
17.3.	Activities and learning	15 points
17.4.	Final exam	0 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

Activities completed 15

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature 1. P. Dayan and L. F. Abbott \| Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems \| MIT Press \| 2001 2. T. J. Sejnowski and J. L. van Hemmen \| 23 problems in systems neuroscience \| Oxford University Press \| 2006 3. M. A. Arbib, Shun-ichi Amari, P. H. Arbib \| The Handbook of Brain Theory and Neural Networks \| MIT Press \| 2002
22.2.	Additional literature No. Author Title Publisher Year