About this course: 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.

Who is this class for: 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.

Created by: University of Washington

Taught by: Rajesh P. N. Rao, Professor
Computer Science & Engineering
Taught by: Adrienne Fairhall, Associate Professor
Physiology and Biophysics

Level	Beginner
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.6 stars Average User Rating 4.6See what learners said

Syllabus

WEEK 1

Introduction & Basic Neurobiology (Rajesh Rao)

This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.

6 videos, 6 readings, 2 practice quizzes

WEEK 2

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 neuron...

8 videos, 3 readings

Graded: Spike Triggered Averages: A Glimpse Into Neural Encoding

WEEK 3

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 ...

6 videos, 2 readings

Graded: Neural Decoding

WEEK 4

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.

5 videos, 2 readings

Graded: Information Theory & Neural Coding

WEEK 5

Computing in Carbon (Adrienne Fairhall)

This module takes you into the world of biophysics of neurons, where you will meet one of the most famous mathematical models in neuroscience, the Hodgkin-Huxley model of action potential (spike) generation. We will also delve into other models of neurons and ...

7 videos, 2 readings

Graded: Computing in Carbon

WEEK 6

Computing with Networks (Rajesh Rao)

This module explores how models of neurons can be connected to create network models. The first lecture shows you how to model those remarkable connections between neurons called synapses. This lecture will leave you in the company of a simple network of integ...

3 videos, 2 readings

Graded: Computing with Networks

WEEK 7

Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao)

This module investigates models of synaptic plasticity and learning in the brain, including a Canadian psychologist's prescient prescription for how neurons ought to learn (Hebbian learning) and the revelation that brains can do statistics (even if we ourselve...

4 videos, 2 readings

Graded: Networks that Learn

WEEK 8

Learning from Supervision and Rewards (Rajesh Rao)

In this last module, we explore supervised learning and reinforcement learning. The first lecture introduces you to supervised learning with the help of famous faces from politics and Bollywood, casts neurons as classifiers, and gives you a taste of that bedro...

4 videos, 2 readings

Graded: Learning from Supervision and Rewards

FAQs

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Coursework

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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.

Ratings and Reviews

Rated 4.6 out of 5 of 195 ratings

A good look at mathematical models focusing mainly at the synapse and neuron level. The math came a little fast and furious for my 30+ years antique math training.

Great overview of a really cool field, gives nice intuitions for ideas in computational neuroscience.

Extremely interesting subject, many ideas and methods presented. Basic disadvantage is a method of source which is closer to seminar rather than leacture. But, lost of details is acceptable due to a huge amount of material. Advanced mathematics from various areas is necessary to fully understand all the ideas. Anyway, I recommend the course.

Great course, if a little bit outdated today.

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