Advanced Computational Neuroscience

2024/2025

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

Institute for Cognitive Neuroscience

Course type:

Compulsory course

When:

2 year, 1, 2 module

Instructor

Zakharov, Denis

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 network dynamics
• Models of Neuro Biophysics and plasticity

Assessment Elements

• analysis of reinforcement learning processes
• Bifurcation analysis of a neuron model
• the study of synchronization processes in neuronal networks
• Exam
• entrance test
  an entrance test showing the required level of mathematical knowledge for successful completion of the course

Interim Assessment

• 2024/2025 2nd module
  0.2 * Bifurcation analysis of a neuron model + 0.399 * Exam + 0.2 * analysis of reinforcement learning processes + 0.001 * entrance test + 0.2 * the study of synchronization processes in neuronal networks

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

Authors

• ZAKHAROV DENIS GENNADEVICH
• NOVIKOV NIKITA ALEKSANDROVICH
• GAMBARYAN ANUSH VACHAGANOVNA
• GUTKIN Boris Samuel
• ZINCHENKO OKSANA OLEGOVNA