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Computational Neuroscience
  • Level Foundation
  • المدة 26 ساعات hours
  • الطبع بواسطة University of Washington
  • Offered by

عن

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.

الوحدات

Reading for Week 1

    • 6 Readings
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6 Readings

  • Welcome Message & Course Logistics
  • About the Course Staff
  • Syllabus and Schedule
  • Matlab & Octave Information and Tutorials
  • Python Information and Tutorials
  • Week 1 Lecture Notes

Course Introduction

    • 3 Videos
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3 Videos

  • 1.1 Course Introduction
  • 1.2 Computational Neuroscience: Descriptive Models
  • 1.3 Computational Neuroscience: Mechanistic and Interpretive Models

The Electrical Personality of Neurons

    • 1 Videos
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1 Videos

  • 1.4 The Electrical Personality of Neurons

Making Connections: Synapses

    • 1 Videos
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1 Videos

  • 1.5 Making Connections: Synapses

Time to Network: Brain Areas and their Function

    • 1 Videos
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1 Videos

  • 1.6 Time to Network: Brain Areas and their Function

Practice Quiz

    • 2 Assignment
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2 Assignment

  • Matlab/Octave Programming
  • Python Programming

Reading for Week 2

    • 2 Readings
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2 Readings

  • Welcome Message
  • Week 2 Lecture Notes and Tutorials

The Neural Code and Neural Encoding

    • 2 Videos
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2 Videos

  • 2.1 What is the Neural Code?
  • 2.2 Neural Encoding: Simple Models

Neural Encoding: Feature Selection

    • 1 Videos
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1 Videos

  • 2.3 Neural Encoding: Feature Selection

Neural Encoding: Variability

    • 1 Videos
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1 Videos

  • 2.4 Neural Encoding: Variability

Supplementary Video Tutorials

    • 4 Videos
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4 Videos

  • 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)

Graded Quiz

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Spike Triggered Averages: A Glimpse Into Neural Encoding

1 Readings

  • IMPORTANT: Quiz Instructions

Reading for Week 3

    • 2 Readings
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2 Readings

  • Welcome Message
  • Week 3 Lecture Notes and Supplementary Material

Neural Decoding and Signal Detection Theory

    • 1 Videos
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1 Videos

  • 3.1 Neural Decoding and Signal Detection Theory

Population Coding and Bayesian Estimation

    • 1 Videos
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1 Videos

  • 3.2 Population Coding and Bayesian Estimation

Reading Minds: Stimulus Reconstruction

    • 1 Videos
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1 Videos

  • 3.3 Reading Minds: Stimulus Reconstruction

Guest Lecture

    • 1 Videos
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1 Videos

  • Fred Rieke on Visual Processing in the Retina

Supplementary Video Tutorials

    • 2 Videos
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2 Videos

  • Gaussians in One Dimension (by Rich Pang)
  • Probability distributions in 2D and Bayes' Rule (by Rich Pang)

Graded Quiz

    • 1 Assignment
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1 Assignment

  • Neural Decoding

Reading for Week 4

    • 2 Readings
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2 Readings

  • Welcome Message
  • Week 4 Lecture Notes and Supplementary Material

Entropy and Spike Trains

    • 2 Videos
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2 Videos

  • 4.1 Information and Entropy
  • 4.2 Calculating Information in Spike Trains

Coding Principles

    • 1 Videos
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1 Videos

  • 4.3 Coding Principles

Supplementary Video Tutorials

    • 2 Videos
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2 Videos

  • What's up with entropy? (by Rich Pang)
  • Information theory? That's crazy! (by Rich Pang)

Graded Quiz

    • 1 Assignment
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1 Assignment

  • Information Theory & Neural Coding

Reading for Week 5

    • 2 Readings
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2 Readings

  • Welcome Message
  • Week 5 Lecture Notes and Supplementary Material

Modeling Neurons and Spikes

    • 2 Videos
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2 Videos

  • 5.1 Modeling Neurons
  • 5.2 Spikes

Simplified Model Neurons and Dendrites

    • 2 Videos
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2 Videos

  • 5.3 Simplified Model Neurons
  • 5.4 A Forest of Dendrites

Guest Lecture

    • 1 Videos
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1 Videos

  • Eric Shea-Brown on Neural Correlations and Synchrony

Supplementary Video Tutorials

    • 2 Videos
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2 Videos

  • Dynamical Systems Theory Intro Part 1: Fixed points (by Rich Pang)
  • Dynamical Systems Theory Intro Part 2: Nullclines (by Rich Pang)

Graded Quiz

    • 1 Assignment
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1 Assignment

  • Computing in Carbon

Reading for Week 6

    • 2 Readings
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2 Readings

  • Welcome Message
  • Week 6 Lecture Notes and Tutorials

Modeling Connections Between Neurons

    • 1 Videos
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1 Videos

  • 6.1 Modeling Connections Between Neurons

Introduction to Network Models

    • 1 Videos
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1 Videos

  • 6.2 Introduction to Network Models

The Fascinating World of Recurrent Networks

    • 1 Videos
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1 Videos

  • 6.3 The Fascinating World of Recurrent Networks

Graded Quiz

    • 1 Assignment
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1 Assignment

  • Computing with Networks

Reading for Week 7

    • 2 Readings
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2 Readings

  • Welcome Message
  • Week 7 Lecture Notes and Tutorials

Synaptic Plasticity, Hebb's Rule, and Statistical Learning

    • 1 Videos
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1 Videos

  • 7.1 Synaptic Plasticity, Hebb's Rule, and Statistical Learning

Introduction to Unsupervised Learning

    • 1 Videos
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1 Videos

  • 7.2 Introduction to Unsupervised Learning

Sparse Coding and Predictive Coding

    • 1 Videos
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1 Videos

  • 7.3 Sparse Coding and Predictive Coding

Supplementary Video Tutorial

    • 1 Videos
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1 Videos

  • Gradient Ascent and Descent (by Rich Pang)

Graded Quiz

    • 1 Assignment
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1 Assignment

  • Networks that Learn

Reading for Week 8

    • 2 Readings
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2 Readings

  • Welcome Message and Concluding Remarks
  • Week 8 Lecture Notes and Supplementary Material

Neurons as Classifiers and Supervised Learning

    • 1 Videos
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1 Videos

  • 8.1 Neurons as Classifiers and Supervised Learning

Reinforcement Learning

    • 2 Videos
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2 Videos

  • 8.2 Reinforcement Learning: Predicting Rewards
  • 8.3 Reinforcement Learning: Time for Action!

Guest Lecture

    • 1 Videos
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1 Videos

  • Eb Fetz on Bidirectional Brain-Computer Interfaces

Graded Quiz

    • 1 Assignment
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1 Assignment

  • Learning from Supervision and Rewards

Auto Summary

Dive into Computational Neuroscience, an engaging course offered by Coursera, focusing on computational methods to understand nervous system functions. Explore vision, sensory-motor control, learning, and memory through Matlab/Octave/Python exercises. Ideal for advanced undergraduates, graduate students, and professionals in Data Science & AI. Choose from Starter, Professional, or Paid subscription options. Duration: 1560 minutes.

Rajesh P. N. Rao
Instructors

Rajesh P. N. Rao

Adrienne Fairhall
Instructors

Adrienne Fairhall