Master 2020/2021

Advanced Computational Neuroscience

Category 'Best Course for Career Development'

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Category 'Best Course for New Knowledge and Skills'

Type: Elective course (Cognitive Sciences and Technologies: From Neuron to Cognition)

Area of studies: Psychology

Delivered by: School of Psychology

Where: Institute for Cognitive Neuroscience

When: 2 year, 1, 2 module

Mode of studies: offline

Open to: students of all HSE University campuses

Instructors: Гуткин Борис Самуэль, Anush Ghambaryan, Nikita Novikov

Master’s programme: Cognitive Sciences and Technologies: From Neuron to Cognition

Language: English

ECTS credits: 4

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

• Know basic notions and definitions in computational neuroscience, its connections with other sciences.
• Know the mathematical methods used for the study of the nervous system
• Know the basic modeling techniques for reinforced behavior.
• 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 the basic models of network dynamics.
• 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
• Know the basic models of neural biophysics
• Know the basic models of neural encoding,

Course Contents

• Basic concepts of reinforcement learning
  Models of decision making; classical conditioning; operating conditioning, learning by reinforcement; neuroeconomics.
• Models of neural coding
  Sensory processing; linear filters and receptive fields; estimation of receptive fields; edge detectors; Hubel and Wiesel mode of visual processing; natural image statistics, information theory, independent component analysis, neural decoding, encoding by population.
• Models of network dynamics
  Biophysics of a neuron, Hudgkin-Huxley formalism, generating action potentials; feedforward and recurrent neural networks; attractors networks; energy functions, Liapunov energy.
• Models of Neuro Biophysics and plasticity
  Learning and synaptic plasticity; associative memories.

Assessment Elements

• Homework assignments
  The homework grade is given based on averaged score for all homework tasks (5 in total) completed by the student. Homework assignments will be given after each lecture and will include written and programming exercises. Assignments are designed to enable grading along correct/incorrect answers to specific questions in each exercise. Total number of questions will be 10. Each correct answer adds one point. The grade is calculated as the proportion of correct answers to the total number of questions. Criteria: 0 – not accepted (Less 5%, or the test was not taken); 1 – very bad (Not less than 5, but less than 15%), 2 – bad (Not less than 15, but less than 25%), 3 – no pass (Not less than 25, but less than 35%), 4 – pass (Not less than 35, but less than 45%), 5 – highly pass (Not less than 45, but less than 55%), 6 – good (Not less than 55, but less than 65%), 7 – very good (Not less than 65, but less than 75%), 8 – almost excellent (Not less than 75, but less than 85%), 9 – excellent (Not less than 85, but less than 95%), 10 – perfect (Not less than 95% and greater).
• Final exam (written)
  Criteria: 0 – not accepted (No answer), 1 – very bad (No criteria met), 2 – bad (Less then 2 criteria met), 3 – no pass (Less then 3 criteria met), 4 – pass (At least 3 criteria are partially met), 5 – highly pass (At least 3 criteria are met), 6 – good (At least 4 criteria are partially met), 7 – very good (At least 4 criteria are met), 8 – almost excellent (All criteria are met), 9 – excellent (All criteria are met, and at least 3 criteria are fully met), 10 – perfect (All criteria are fully met).

Interim Assessment

• Interim assessment (2 module)
  0.4 * Final exam (written) + 0.6 * Homework assignments

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