Computational Neuroscience

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Description

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Understanding how the brain works is one of the fundamental challenges in science today. This course will introduce you to basic computational techniques for analyzing, modeling, and understanding the behavior of cells and circuits in the brain. You do not need to have any prior background in neuroscience to take this course.

About the Course

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 vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by …

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

Understanding how the brain works is one of the fundamental challenges in science today. This course will introduce you to basic computational techniques for analyzing, modeling, and understanding the behavior of cells and circuits in the brain. You do not need to have any prior background in neuroscience to take this course.

About the Course

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 vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.

About the Instructor(s)

Rajesh P. N. Rao is an associate professor in the Computer Science and Engineering department at the University of Washington, Seattle. He received his PhD from the University of Rochester and was a Sloan postdoctoral fellow at the Salk Institute for Biological Studies in San Diego. He is the recipient of an NSF CAREER award, an ONR Young Investigator Award, a Sloan Faculty Fellowship, and a David and Lucile Packard Fellowship for Science and Engineering. He is the author of the textbook Brain-Computer Interfacing (Cambridge University Press, 2013) and the co-editor of two volumes, Probabilistic Models of the Brain (MIT Press, 2002) and Bayesian Brain (MIT Press, 2007). His research spans the areas of computational neuroscience, artificial intelligence, and brain-computer interfacing.
Adrienne Fairhall is an Associate Professor in the Department of Physiology and Biophysics at the University of Washington. She received her Ph.D. degree in statistical physics from the Weizmann Institute of Science in 1998 and began her work in computational neuroscience in the research group of William Bialek. Dr Fairhall is the director of the University of Washington’s Computational Neuroscience Program and has also directed the prestigious Methods in Computational Neuroscience course at the Marine Biological Laboratory in Woods Hole. Dr Fairhall's research aims to discover the mathematical and physical principles that govern information coding and transmission in the nervous system.

Course Syllabus

Topics covered include:

1. Basic Neurobiology
2. Neural Encoding and Decoding Techniques
3. Information Theory and Neural Coding
4. Single Neuron Models: Integrate-and-Fire and Compartmental
5. Network Models: Feedforward and Recurrent Networks
6. Synaptic Plasticity and Learning

Recommended Background

Familiarity with basic concepts in linear algebra, calculus, and probability theory. Specifically, ability to understand simple equations involving vectors and matrices, differentiate simple functions, and understand what a probability distribution is. For the exercises, some familiarity with Matlab or Octave would be useful. No prior background in neuroscience is required.

Suggested Readings

The lectures will roughly follow topics covered in the textbook Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Peter Dayan and Larry Abbott (MIT Press). The other useful resource for the course is Tutorial on Neural Systems Modeling (Sinauer), which also contains useful Matlab examples of concepts we will learn in the course.

Course Format

The course will last 10 weeks and will consist of lecture videos and homework assignments, some of which will include programming in Matlab or Octave.

Provided by:

University: University of Washington

Instructor(s): Rajesh Rao, Adrienne Fairhall

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