Washington University - Computational Neuroscience
- Offered byCoursera
- Public/Government Institute
Computational Neuroscience at Coursera Overview
Duration | 26 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Computational Neuroscience at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level
- Approx. 26 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Computational Neuroscience at Coursera Course details
- 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 at Coursera Curriculum
Introduction & Basic Neurobiology (Rajesh Rao)
1.1 Course Introduction
1.2 Computational Neuroscience: Descriptive Models
1.3 Computational Neuroscience: Mechanistic and Interpretive Models
1.4 The Electrical Personality of Neurons
1.5 Making Connections: Synapses
1.6 Time to Network: Brain Areas and their Function
Welcome Message & Course Logistics
About the Course Staff
Syllabus and Schedule
Matlab & Octave Information and Tutorials
Python Information and Tutorials
Week 1 Lecture Notes
Matlab/Octave Programming
Python Programming
What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall)
2.1 What is the Neural Code?
2.2 Neural Encoding: Simple Models
2.3 Neural Encoding: Feature Selection
2.4 Neural Encoding: Variability
Vectors and Functions (by Rich Pang)
Convolutions and Linear Systems (by Rich Pang)
Change of Basis and PCA (by Rich Pang)
Welcome to the Eigenworld! (by Rich Pang)
Welcome Message
Week 2 Lecture Notes and Tutorials
IMPORTANT: Quiz Instructions
Spike Triggered Averages: A Glimpse Into Neural Encoding
Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)
3.1 Neural Decoding and Signal Detection Theory
3.2 Population Coding and Bayesian Estimation
3.3 Reading Minds: Stimulus Reconstruction
Fred Rieke on Visual Processing in the Retina
Gaussians in One Dimension (by Rich Pang)
Probability distributions in 2D and Bayes' Rule (by Rich Pang)
Welcome Message
Week 3 Lecture Notes and Supplementary Material
Neural Decoding
Information Theory & Neural Coding (Adrienne Fairhall)
4.1 Information and Entropy
4.2 Calculating Information in Spike Trains
4.3 Coding Principles
What's up with entropy? (by Rich Pang)
Information theory? That's crazy! (by Rich Pang)
Welcome Message
Week 4 Lecture Notes and Supplementary Material
Information Theory & Neural Coding
Computing in Carbon (Adrienne Fairhall)
5.1 Modeling Neurons
5.2 Spikes
5.3 Simplified Model Neurons
5.4 A Forest of Dendrites
Eric Shea-Brown on Neural Correlations and Synchrony
Dynamical Systems Theory Intro Part 1: Fixed points (by Rich Pang)
Dynamical Systems Theory Intro Part 2: Nullclines (by Rich Pang)
Welcome Message
Week 5 Lecture Notes and Supplementary Material
Computing in Carbon
Computing with Networks (Rajesh Rao)
6.1 Modeling Connections Between Neurons
6.2 Introduction to Network Models
6.3 The Fascinating World of Recurrent Networks
Welcome Message
Week 6 Lecture Notes and Tutorials
Computing with Networks
Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao)
7.1 Synaptic Plasticity, Hebb's Rule, and Statistical Learning
7.2 Introduction to Unsupervised Learning
7.3 Sparse Coding and Predictive Coding
Gradient Ascent and Descent (by Rich Pang)
Welcome Message
Week 7 Lecture Notes and Tutorials
Networks that Learn
Learning from Supervision and Rewards (Rajesh Rao)
8.1 Neurons as Classifiers and Supervised Learning
8.2 Reinforcement Learning: Predicting Rewards
8.3 Reinforcement Learning: Time for Action!
Eb Fetz on Bidirectional Brain-Computer Interfaces
Welcome Message and Concluding Remarks
Week 8 Lecture Notes and Supplementary Material
Learning from Supervision and Rewards
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