Master
2023/2024
Advanced Computational Neuroscience
Type:
Compulsory course (Cognitive Sciences and Technologies: From Neuron to Cognition)
Area of studies:
Psychology
Delivered by:
Institute for Cognitive Neuroscience
When:
2 year, 1, 2 module
Mode of studies:
offline
Open to:
students of all HSE University campuses
Master’s programme:
Cognitive Sciences and Technologies: From Neuron to Cognition
Language:
English
ECTS credits:
6
Contact hours:
24
Course Syllabus
Abstract
This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of behavior, vision, sensory-motor control, learning, and memory. Specific topics that will be covered include reinforcement learning models, representation of information by spiking neurons, processing of information in neural networks, and models of neuronal dynamics and biophysics. We will make use of Matlab demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. 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 and how to use mathematics to model brain processes. The course "Computational Neuroscience" is 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 specialist 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
- Models of network dynamics
- Models of Neuro Biophysics and plasticity
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