Бакалавриат
2025/2026
Введение в вычислительную нейробиологию
Статус:
Курс обязательный (Когнитивная нейробиология)
Кто читает:
Базовая кафедра Института биоорганической химии им. академиков М.М. Шемякина и Ю.А. Овчинникова РАН
Где читается:
Факультет биологии и биотехнологии
Когда читается:
3-й курс, 3, 4 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Осетрова Мария Станиславовна
Язык:
английский
Кредиты:
6
Контактные часы:
60
Course Syllabus
Abstract
The course is intended for third-year undergraduate students with a degree in Cognitive Neuroscience. The purpose of the Introduction to Computational Neuroscience course is to provide students with knowledge in various fields related to computational modeling of neural systems and their applications to understanding brain functions. The course covers topics such as model types, generalized linear models, data dimensionality reduction techniques, and the fundamentals of machine learning, which are fundamental to computational neuroscience. The course takes place in the format of lectures with homework aimed at putting the acquired knowledge into practice.
Learning Objectives
- The aim of the course is to provide students with knowledge in various areas related to computational modeling of neural systems and its applications for understanding brain function. The course covers topics such as types of models, generalized linear models, dimensionality reduction methods, and the fundamentals of machine learning, which are foundational to computational neuroscience. The course is delivered in the format of lectures and seminars, with homework assignments aimed at putting the acquired knowledge into practice.
Expected Learning Outcomes
- Acquired analytical skills for the evaluation and interpretation of computational models.
- Demonstrates the ability to describe differences between model types in computational neuroscience, including where they apply and where they fall short.
- Ability to apply computational and mathematical methods to solve problems in computational neuroscience.
- Ability to use linear models and dimensionality reduction methods in practice within computational neuroscience.
- Ability to critically evaluate and select appropriate modeling approaches for specific research questions in computational neuroscience.
- Experience in implementing and analyzing computational models using appropriate software tools.
Course Contents
- Introduction to Computational Neuroscience
- Generalized linear models. Dimensionality reduction methods
- Machine Learning Applications in Computational Neuroscience. Dynamical Systems.
Interim Assessment
- 2025/2026 4th module0.2 * Домашнее задание + 0.2 * Домашнее задание + 0.2 * Защита проекта + 0.1 * Лекционные тесты + 0.1 * Лекционные тесты + 0.2 * Промежуточный тест
Bibliography
Recommended Core Bibliography
- Churchland, P. S., & Sejnowski, T. J. (2016). Blending computational and experimental neuroscience. https://doi.org/10.1038/nrn.2016.114
- Dayan, P., & Abbott, L. F. (2001). Theoretical Neuroscience : Computational and Mathematical Modeling of Neural Systems. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=74918
Recommended Additional Bibliography
- Computational Cognitive Neuroscience - CCBY4_019 - O'Reilly, Munakata, Hazy & Frank - 2022 - Open Educational Resources: libretexts.org - https://ibooks.ru/bookshelf/390536 - 390536 - iBOOKS
- Oyana, T. J., & Margai, F. M. (2015). Spatial Analysis : Statistics, Visualization, and Computational Methods. Boca Raton, Fla: CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1045131