Computational Neuroscience

neuroscience

Course Overview

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/Octave/Python 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.

What you will learn from this course

  • Introduction & Basic Neurobiology (Rajesh Rao)
  • What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall)
  • Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)
  • Information Theory & Neural Coding (Adrienne Fairhall)
  • Computing in Carbon (Adrienne Fairhall)
  • Computing with Networks (Rajesh Rao)
  • Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao)
  • Learning from Supervision and Rewards (Rajesh Rao)

 

Course Duration: 26 Hours

Course Rating : 4.6

Corse Instructors :Rajesh P. N. Rao and Adrienne Fairhall

Offered By: University of Washington

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