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Computational Neuroscience

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.

Sessions

Course at a Glance

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.

Beginning with the Spring 2015 offering, Signature Track and Verified Certificates are available for this class. The formatting on the verified certificate is very slightly different from those for courses from other institutions. An example certificate is here. (The date on the actual certificates will be different.)

Course Syllabus

Topics covered include:

1. Basic Neurobiology
2. Neural Encoding and Decoding Techniques
3. Information Theory and Neural Coding
4. Single Neuron Models (Biophysical and Simplified)
5. Synapse and Network Models (Feedforward and Recurrent)
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 Matlab examples of concepts we will learn in the course.

Course Format

The course will last 8 weeks and will consist of lecture videos and homework assignments, some of which will include programming in Matlab or Octave.

FAQ

Is Signature Track available for this class?
Yes, beginning with the Spring 2015 offering, Signature Track and Verified Certificates are available for this class. The formatting on the verified certificate is very slightly different from those for courses from other institutions. An example certificate is here. (The date on the actual certificates will be different.)

Will I get a Statement of Accomplishment after completing this class?
Yes, students who successfully complete the class will receive a Statement of Accomplishment signed by the instructors.

What resources will I need for this class?
An Internet connection, access to Matlab or Octave (downloadable for free from Octave website), a strong drive to learn, and an inquisitive mind.