About this Course
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Flexible deadlines

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Beginner Level

Approx. 30 hours to complete

English

Subtitles: English

Skills you will gain

Computational NeuroscienceArtificial Neural NetworkReinforcement LearningBiological Neuron Model

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

Approx. 30 hours to complete

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
4 hours to complete

Introduction & Basic Neurobiology (Rajesh Rao)

6 videos (Total 89 min), 6 readings, 2 quizzes
6 videos
1.2 Computational Neuroscience: Descriptive Models11m
1.3 Computational Neuroscience: Mechanistic and Interpretive Models12m
1.4 The Electrical Personality of Neurons23m
1.5 Making Connections: Synapses20m
1.6 Time to Network: Brain Areas and their Function17m
6 readings
Welcome Message & Course Logistics10m
About the Course Staff10m
Syllabus and Schedule10m
Matlab & Octave Information and Tutorials10m
Python Information and Tutorials10m
Week 1 Lecture Notes10m
2 practice exercises
Matlab/Octave Programming1h
Python Programming1h
Week
2
4 hours to complete

What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall)

8 videos (Total 167 min), 3 readings, 1 quiz
8 videos
2.2 Neural Encoding: Simple Models12m
2.3 Neural Encoding: Feature Selection22m
2.4 Neural Encoding: Variability23m
Vectors and Functions (by Rich Pang)30m
Convolutions and Linear Systems (by Rich Pang)16m
Change of Basis and PCA (by Rich Pang)18m
Welcome to the Eigenworld! (by Rich Pang)24m
3 readings
Welcome Message10m
Week 2 Lecture Notes and Tutorials10m
IMPORTANT: Quiz Instructions10m
1 practice exercise
Spike Triggered Averages: A Glimpse Into Neural Encoding1h
Week
3
3 hours to complete

Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)

6 videos (Total 114 min), 2 readings, 1 quiz
6 videos
3.2 Population Coding and Bayesian Estimation24m
3.3 Reading Minds: Stimulus Reconstruction11m
Fred Rieke on Visual Processing in the Retina14m
Gaussians in One Dimension (by Rich Pang)30m
Probability distributions in 2D and Bayes' Rule (by Rich Pang)13m
2 readings
Welcome Message10m
Week 3 Lecture Notes and Supplementary Material10m
1 practice exercise
Neural Decoding30m
Week
4
3 hours to complete

Information Theory & Neural Coding (Adrienne Fairhall)

5 videos (Total 98 min), 2 readings, 1 quiz
5 videos
4.2 Calculating Information in Spike Trains17m
4.3 Coding Principles19m
What's up with entropy? (by Rich Pang)25m
Information theory? That's crazy! (by Rich Pang)16m
2 readings
Welcome Message10m
Week 4 Lecture Notes and Supplementary Material10m
1 practice exercise
Information Theory & Neural Coding1h
4.7
124 ReviewsChevron Right

19%

got a tangible career benefit from this course

Top reviews from Computational Neuroscience

By CMJun 15th 2017

This course is an excellent introduction to the field of computational neuroscience, with engaging lectures and interesting assignments that make learning the material easy.

By JBMay 25th 2019

I really enjoyed this course and think that there was a good variety of material that allowed people of many different backgrounds to take at least one thing away from this.

Instructors

Avatar

Rajesh P. N. Rao

Professor
Computer Science & Engineering
Avatar

Adrienne Fairhall

Associate Professor
Physiology and Biophysics

About University of Washington

Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world....

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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