{ "links":{}, "photo":"https://s3.amazonaws.com/coursera/topics/compneuro/large-icon.png", "courseFormat":"The course will last 8 weeks and will consist of lecture videos and homework\nassignments, some of which will include programming in Matlab or Octave.", "smallIcon":"https://d1z850dzhxs7de.cloudfront.net/topics/compneuro/small-icon.hover.png", "universityLogo":"", "video":"exBbVX0pYxo", "smallIconHover":"https://d1z850dzhxs7de.cloudfront.net/topics/compneuro/small-icon.hover.png", "shortDescription":"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.", "id":97, "estimatedClassWorkload":"6-8 hours/week", "universityLogoSt":"", "targetAudience":1, "courseSyllabus":"Topics covered include:\n
\n
1. Basic Neurobiology\n
2. Neural Encoding and Decoding Techniques\n
3. Information Theory and Neural Coding\n
4. Single Neuron Models (Biophysical and Simplified)\n
5. Synapse and Network Models (Feedforward and Recurrent)\n
6. Synaptic Plasticity and Learning", "aboutTheCourse":"This course provides an introduction to basic computational methods for\nunderstanding what nervous systems do and for determining how they function.\nWe will explore the computational principles governing various aspects\nof vision, sensory-motor control, learning, and memory. Specific topics\nthat will be covered include representation of information by spiking neurons,\nprocessing of information in neural networks, and algorithms for adaptation\nand learning. We will make use of Matlab demonstrations and exercises to\ngain a deeper understanding of concepts and methods introduced in the course.\nThe course is primarily aimed at third- or fourth-year undergraduates and\nbeginning graduate students, as well as professionals and distance learners\ninterested in learning how the brain processes information.

Beginning with the Spring 2015 offering, Signature Track and Verified \nCertificates are available for this class. The formatting on the \nverified certificate is very slightly different from those for courses \nfrom other institutions. An example certificate is here. (The date on \nthe actual certificates will be different.)", "largeIcon":"https://d15cw65ipctsrr.cloudfront.net/0f/bc0be0352d11e4a4351b48ac74e750/large-icon.png", "suggestedReadings":"The lectures will roughly follow topics covered in the textbook Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems\n\nby Peter Dayan and Larry Abbott (MIT Press). The other useful resource\n for the course is Tutorial on Neural Systems Modeling\n\n (Sinauer), which also contains Matlab examples of concepts we will learn\n in the course.", "videoId":"exBbVX0pYxo", "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\nAccomplishment signed by the instructors.\n
\n
What resources will I need for this class?
An Internet\nconnection, access to Matlab or Octave (downloadable for free from Octave\nwebsite), a strong drive to learn, and an inquisitive mind.", "shortName":"compneuro", "instructor":"Rajesh P. N. Rao and Adrienne Fairhall", "name":"Computational Neuroscience", "subtitleLanguagesCsv":"en", "recommendedBackground":"Familiarity with basic concepts in linear algebra, calculus, and probability\ntheory. Specifically, ability to understand simple equations involving\nvectors and matrices, differentiate simple functions, and understand what\na probability distribution is. For the exercises, some familiarity with\nMatlab or Octave would be useful. No prior background in neuroscience is\nrequired.", "aboutTheInstructor":" 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\u2019s 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." }