Бакалавриат
2026/2027
Введение в вычислительную нейробиологию
Статус:
Курс по выбору (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
Язык:
английский
Кредиты:
6
Контактные часы:
60
Course Syllabus
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 computatiomodels.
- 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
- 2026/2027 4th module0.2 * Project + 0.2 * Test + 0.1 * Test + 0.1 * Test + 0.2 * Homework
Bibliography
Recommended Core Bibliography
- Churchland, P. S., & Sejnowski, T. J. (2016). Blending computational and experimental neuroscience. https://doi.org/10.1038/nrn.2016.114
- Computational Cognitive Neuroscience - CCBY4_019 - O'Reilly, Munakata, Hazy & Frank - 2022 - Open Educational Resources: libretexts.org - https://ibooks.ru/products/390536 - 390536 - iBOOKS
Recommended Additional Bibliography
- 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
- 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