课程信息
4.6
414 ratings
90 reviews
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....
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100% 在线课程

立即开始,按照自己的计划学习。
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Beginner Level

初级

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建议:5 hours/week

完成时间大约为30 小时
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English

字幕:English

您将获得的技能

Computational NeuroscienceBiological Neuron ModelReinforcement LearningArtificial Neural Network
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Beginner Level

初级

Clock

建议:5 hours/week

完成时间大约为30 小时
Comment Dots

English

字幕:English

教学大纲 - 您将从这门课程中学到什么

1

章节
Clock
完成时间为 4 小时

Introduction & Basic Neurobiology (Rajesh Rao)

This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology. ...
Reading
6 个视频(共 89 分钟), 6 个阅读材料, 2 个测验
Video6 个视频
1.2 Computational Neuroscience: Descriptive Models11分钟
1.3 Computational Neuroscience: Mechanistic and Interpretive Models12分钟
1.4 The Electrical Personality of Neurons23分钟
1.5 Making Connections: Synapses20分钟
1.6 Time to Network: Brain Areas and their Function17分钟
Reading6 个阅读材料
Welcome Message & Course Logistics10分钟
About the Course Staff10分钟
Syllabus and Schedule10分钟
Matlab & Octave Information and Tutorials10分钟
Python Information and Tutorials10分钟
Week 1 Lecture Notes10分钟
Quiz2 个练习
Matlab/Octave Programming分钟
Python Programming分钟

2

章节
Clock
完成时间为 4 小时

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 个视频(共 167 分钟), 3 个阅读材料, 1 个测验
Video8 个视频
2.2 Neural Encoding: Simple Models12分钟
2.3 Neural Encoding: Feature Selection22分钟
2.4 Neural Encoding: Variability23分钟
Vectors and Functions (by Rich Pang)30分钟
Convolutions and Linear Systems (by Rich Pang)16分钟
Change of Basis and PCA (by Rich Pang)18分钟
Welcome to the Eigenworld! (by Rich Pang)24分钟
Reading3 个阅读材料
Welcome Message10分钟
Week 2 Lecture Notes and Tutorials10分钟
IMPORTANT: Quiz Instructions10分钟
Quiz1 个练习
Spike Triggered Averages: A Glimpse Into Neural Encoding分钟

3

章节
Clock
完成时间为 3 小时

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 个视频(共 114 分钟), 2 个阅读材料, 1 个测验
Video6 个视频
3.2 Population Coding and Bayesian Estimation24分钟
3.3 Reading Minds: Stimulus Reconstruction11分钟
Fred Rieke on Visual Processing in the Retina14分钟
Gaussians in One Dimension (by Rich Pang)30分钟
Probability distributions in 2D and Bayes' Rule (by Rich Pang)13分钟
Reading2 个阅读材料
Welcome Message10分钟
Week 3 Lecture Notes and Supplementary Material10分钟
Quiz1 个练习
Neural Decoding30分钟

4

章节
Clock
完成时间为 3 小时

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 个视频(共 98 分钟), 2 个阅读材料, 1 个测验
Video5 个视频
4.2 Calculating Information in Spike Trains17分钟
4.3 Coding Principles19分钟
What's up with entropy? (by Rich Pang)25分钟
Information theory? That's crazy! (by Rich Pang)16分钟
Reading2 个阅读材料
Welcome Message10分钟
Week 4 Lecture Notes and Supplementary Material10分钟
Quiz1 个练习
Information Theory & Neural Coding分钟
4.6

热门审阅

创建者 JRApr 8th 2018

Extremely enlightening course on how Neuron's work and the science of computational neuroscience. Even if you don't want to get into the complex mathematics you can get a lot out of the course

创建者 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.

讲师

Rajesh P. N. Rao

Professor
Computer Science & Engineering

Adrienne Fairhall

Associate Professor
Physiology and Biophysics

关于 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....

常见问题

  • 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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