• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Advanced Computational Neuroscience

2024/2025
Academic Year
ENG
Instruction in English
6
ECTS credits
Course type:
Compulsory course
When:
2 year, 1, 2 module

Instructor

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

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

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

Course Contents

  • Basic concepts of reinforcement learning
  • Models of network dynamics
  • Models of Neuro Biophysics and plasticity
Assessment Elements

Assessment Elements

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

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

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
  • ZINCHENKO OKSANA OLEGOVNA
  • GUTKIN BORIS SAMUEL