About this Course
4.6
451 ratings
98 reviews
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Beginner Level

Beginner Level

Hours to complete

Approx. 31 hours to complete

Suggested: 5 hours/week...
Available languages

English

Subtitles: English

Skills you will gain

Computational NeuroscienceArtificial Neural NetworkReinforcement LearningBiological Neuron Model
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Beginner Level

Beginner Level

Hours to complete

Approx. 31 hours to complete

Suggested: 5 hours/week...
Available languages

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
Hours to complete
4 hours to complete

Introduction & Basic Neurobiology (Rajesh Rao)

This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology. ...
Reading
6 videos (Total 89 min), 6 readings, 2 quizzes
Video6 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
Reading6 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
Quiz2 practice exercises
Matlab/Octave Programmings
Python Programmings
Week
2
Hours to complete
4 hours to complete

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

This module introduces you to the captivating world of neural information coding. You will learn about the technologies that are used to record brain activity. We will then develop some mathematical formulations that allow us to characterize spikes from neurons as a code, at increasing levels of detail. Finally we investigate variability and noise in the brain, and how our models can accommodate them....
Reading
8 videos (Total 167 min), 3 readings, 1 quiz
Video8 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
Reading3 readings
Welcome Message10m
Week 2 Lecture Notes and Tutorials10m
IMPORTANT: Quiz Instructions10m
Quiz1 practice exercise
Spike Triggered Averages: A Glimpse Into Neural Encodings
Week
3
Hours to complete
3 hours to complete

Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)

In this module, we turn the question of neural encoding around and ask: can we estimate what the brain is seeing, intending, or experiencing just from its neural activity? This is the problem of neural decoding and it is playing an increasingly important role in applications such as neuroprosthetics and brain-computer interfaces, where the interface must decode a person's movement intentions from neural activity. As a bonus for this module, you get to enjoy a guest lecture by well-known computational neuroscientist Fred Rieke. ...
Reading
6 videos (Total 114 min), 2 readings, 1 quiz
Video6 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
Reading2 readings
Welcome Message10m
Week 3 Lecture Notes and Supplementary Material10m
Quiz1 practice exercise
Neural Decoding30m
Week
4
Hours to complete
3 hours to complete

Information Theory & Neural Coding (Adrienne Fairhall)

This module will unravel the intimate connections between the venerable field of information theory and that equally venerable object called our brain....
Reading
5 videos (Total 98 min), 2 readings, 1 quiz
Video5 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
Reading2 readings
Welcome Message10m
Week 4 Lecture Notes and Supplementary Material10m
Quiz1 practice exercise
Information Theory & Neural Codings
4.6
98 ReviewsChevron Right

Top Reviews

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 ATMay 27th 2018

This is a wonderful start for a biologist , to get idea of concepts of learning . An advanced course focused more on brain circuitry is suggested.\n\nThanks a lot

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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