2026/2027
Компьютерная нейронаука
Статус:
Маго-лего
Кто читает:
Институт когнитивных нейронаук
Где читается:
Институт когнитивных нейронаук
Когда читается:
1, 2 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Захаров Денис Геннадьевич
Язык:
английский
Кредиты:
6
Контактные часы:
24
Course Syllabus
Abstract
This course provides an introduction to contemporary computational approaches for understanding how nervous systems process information and how they determine behavior. We will explore the computational principles underlying neural information processing, from the level of single neurons and synapses to entire populations of interacting cells. We will examine models of decision making, classical and operant conditioning, learning by reinforcement, and the emerging field of neuroeconomics. We will study sensory processing, with a focus on linear filters and receptive fields, and their estimation. We will investigate spiking neuron models and neuronal excitability, and the Hodgkin–Huxley formalism for modeling spiking and bursting neurons. Finally, we will consider synapses and synaptic plasticity, as well as synchronization in neuronal populations, together with the characteristics used to quantify the level of synchronization. We will make use of Python demonstrations and exercises to gain a deeper understanding of the concepts and methods introduced in the course, and an introduction to mathematical techniques will be given as needed. The course is primarily aimed at masters graduate students interested in learning how the brain processes information through computation and how to use mathematics to model brain processes. The course "Computational Neuroscience" is a new and unique discipline within the educational programs of the National Research University Higher School of Economics. The course is based on contemporary scientific research in computational neuroscience and related scientific areas. It is essential in training competent specialists in the areas of cognitive sciences and technologies.
Learning Objectives
- Understand the principles of information processing in brain circuits and networks
- Gain understanding of the mathematical techniques necessary to develop models of brain dynamics
- Gain skills in developing computational models of learning and neuronal plasticity
- Gain skills and knowledge for modeling motivated behavior
- Gains knowledge and skills in applying mathematical models in neuroscience
Expected Learning Outcomes
- Be able to distinguish the capacities and restrictions for models considered
- Be able to relate mathematical models to the functioning of the nervous system.
- Know basic notions and definitions in computational neuroscience, its connections with other sciences.
- Know the basic modeling techniques for reinforced behavior.
- Know the basic models of network dynamics.
- Know the basic models of neural biophysics
- Know the basic models of neural encoding,
- Know the mathematical methods used for the study of the nervous system
- Possess skills for choosing appropriate computational neuroscience methods for psychological research.
- Possess skills for choosing appropriate computational neuroscience methods for psychological research.
- Possess skills for translation between the various levels of models to describe psychological and physiological levels of interpretation of experimental data
Course Contents
- Basic concepts of reinforcement learning
- Models of neural coding
- Neuron models
- Neuronal networks
Assessment Elements
- analysis of reinforcement learning processes
- Bifurcation analysis of a neuron model
- the study of synchronization processes in neuronal networks
- Exam
- entrance testan entrance test showing the required level of mathematical knowledge for successful completion of the course
Interim Assessment
- 2026/2027 2nd module0.2 * analysis of reinforcement learning processes + 0.001 * entrance test + 0.2 * the study of synchronization processes in neuronal networks + 0.2 * Bifurcation analysis of a neuron model + 0.399 * Exam
Bibliography
Recommended Core Bibliography
- Naldi, G., & Nieus, T. (2018). Mathematical and Theoretical Neuroscience : Cell, Network and Data Analysis. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1737030
- Wiering, M., & Otterlo, M. van. (2012). Reinforcement Learning : State-of-the-Art. Berlin: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=537744
Recommended Additional Bibliography
- Pouget, A., Dayan, P., & Zemel, R. (2000). Information Processing with Population Codes. Nature Reviews Neuroscience, 1(2), 125–132. https://doi.org/10.1038/35039062