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.
Computational Neuroscience
Offered By
University of Washington
About this Course
4.6
463 ratings
Skills you will gain
Computational NeuroscienceArtificial Neural NetworkReinforcement LearningBiological Neuron Model
Syllabus - What you will learn from this course
Week
1Introduction & Basic Neurobiology (Rajesh Rao)
This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.
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 Programmings
Python Programmings
Week
2What 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.
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 Encodings
Week
3Extracting 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.
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
4Information 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.
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 Codings
4.6
102 Reviews22%
got a tangible career benefit from this course
Top Reviews
By CM•Jun 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 AT•May 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
Rajesh P. N. Rao
ProfessorComputer Science & Engineering
Adrienne Fairhall
Associate ProfessorPhysiology 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
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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.
What will I get if I purchase the Certificate?
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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