University of Washington

Computational Neuroscience

Taught in English

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131,473 already enrolled

Course

Gain insight into a topic and learn the fundamentals

Rajesh P. N. Rao
Adrienne Fairhall

Instructors: Rajesh P. N. Rao

4.6

(1,059 reviews)

Beginner level
No prior experience required
26 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace

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Assessments

9 quizzes

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There are 8 modules in this course

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

What's included

6 videos6 readings2 quizzes

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.

What's included

8 videos3 readings1 quiz

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.

What's included

6 videos2 readings1 quiz

This module will unravel the intimate connections between the venerable field of information theory and that equally venerable object called our brain.

What's included

5 videos2 readings1 quiz

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 learn how to model a neuron's structure, including those intricate branches called dendrites.

What's included

7 videos2 readings1 quiz

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 integrate-and-fire neurons which follow each other or dance in synchrony. In the second lecture, you will learn about firing rate models and feedforward networks, which transform their inputs to outputs in a single "feedforward" pass. The last lecture takes you to the dynamic world of recurrent networks, which use feedback between neurons for amplification, memory, attention, oscillations, and more!

What's included

3 videos2 readings1 quiz

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 ourselves sometimes cannot)! The next two lectures explore unsupervised learning and theories of brain function based on sparse coding and predictive coding.

What's included

4 videos2 readings1 quiz

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 bedrock of supervised learning, backpropagation, with whose help you will learn to back a truck into a loading dock.The second and third lectures focus on reinforcement learning. The second lecture will teach you how to predict rewards à la Pavlov's dog and will explore the connection to that important reward-related chemical in our brains: dopamine. In the third lecture, we will learn how to select the best actions for maximizing rewards, and examine a possible neural implementation of our computational model in the brain region known as the basal ganglia. The grand finale: flying a helicopter using reinforcement learning!

What's included

4 videos2 readings1 quiz

Instructors

Instructor ratings
4.5 (196 ratings)
Rajesh P. N. Rao
University of Washington
1 Course131,473 learners
Adrienne Fairhall
University of Washington
1 Course131,473 learners

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4.6

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Reviewed on Dec 1, 2018

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Reviewed on Jun 10, 2020

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