Introduction to Computational Neuroscience

Introduction to Computational Neuroscience

1.

Subject title

Introduction to Computational Neuroscience

Вовед во пресметковна невронаука

2.

Code

m23_w_203

3.

Study program

Cloud Computing, Data science in computer science and engineering, Security, Cryptography and Coding, Еducation with ICT, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Security, Cryptography and Coding, Statistics and Data Analytics, Software Engineering, IT management, Eco-informatics, Bioinformatics, Bioinformatics,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

доц. д-р Илинка Иваноска

9.

Prerequisites for enrollment

10.

Subject goals and competencies:


Student will be capable of using calculating techniques and mathematical Models for modeling and analyzing neuronal systems.

11.

Subject content:


Neuronal coding and decoding: nerve impulses statistics, reversible correlation And visually receptive fields, neuronal decoding, information theory. Neurons And neuronal circuits: neuroelectronics, conductivity and morphology, network models. Adaptation and learning: plasticity and learning, learning methods, representative learning.

12.

Learning methods:


Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

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

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

15 points

17.2.

Seminar work / 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.

Condition for signature and taking final exam

реализирани активности 15

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

No.

Author

Title

Publisher

Year

7817

P. Dayan and L. F. Abbott

Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems

MIT Press

2001

7818

T. J. Sejnowski and J. L. van Hemmen

23 problems in systems neuroscience

Oxford University Press

2006

7819

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