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Computational Neuroscience

Курс от University of Washington
Начальный≈ 23.9 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

MatlabArtificial Neural NetworksReinforcement LearningSupervised LearningDifferential EquationsMathematical ModelingRecurrent Neural Networks (RNNs)Network ModelBiologyElectrophysiologyNeurologyMachine Learning AlgorithmsProbability DistributionMachine Learning MethodsNetwork AnalysisPhysiologySensory Systems Analysis

Программа курса

8 модулей · 65 учебных материалов

01Introduction & Basic Neurobiology (Rajesh Rao)14 материалов

Reading for Week 1

Welcome Message & Course LogisticsЧтениеAbout the Course StaffЧтениеWeek 1 Lecture NotesЧтение

Course Introduction

1.1 Course IntroductionВидео

Учитесь у экспертов

Rajesh P. N. Rao

Professor

Adrienne Fairhall

Associate Professor

Computational Neuroscience
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Обучение на Coursera

≈ 23.9 ч

8 модулей

Язык: Английский

Субтитры: Арабский, Французский, Бенгальский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Exploring How the Brain ComputesDIALOGUE
1.2 Computational Neuroscience: Descriptive ModelsВидео
1.3 Computational Neuroscience: Mechanistic and Interpretive ModelsВидео

The Electrical Personality of Neurons

1.4 The Electrical Personality of NeuronsВидео

Making Connections: Synapses

1.5 Making Connections: SynapsesВидео

Time to Network: Brain Areas and their Function

1.6 Time to Network: Brain Areas and their FunctionВидео

Course Pre-Requisites: Matlab/Octave/Python

Matlab Information and TutorialsЧтениеMatlab/Octave ProgrammingЗаданиеPython Information and TutorialsЧтениеPython ProgrammingЗадание
02What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall)10 материалов

Reading for Week 2

Week 2 Lecture Notes and TutorialsЧтение

The Neural Code and Neural Encoding

2.1 What is the Neural Code?Видео2.2 Neural Encoding: Simple ModelsВидео

Neural Encoding: Feature Selection

2.3 Neural Encoding: Feature SelectionВидео

Neural Encoding: Variability

2.4 Neural Encoding: VariabilityВидео

Supplementary Video Tutorials

Vectors and Functions (by Rich Pang)ВидеоConvolutions and Linear Systems (by Rich Pang)ВидеоChange of Basis and PCA (by Rich Pang)ВидеоWelcome to the Eigenworld! (by Rich Pang)Видео

Graded Quiz

Spike Triggered Averages: A Glimpse Into Neural Encoding Задание
03Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)8 материалов

Reading for Week 3

Week 3 Lecture Notes and Supplementary MaterialЧтение

Neural Decoding and Signal Detection Theory

3.1 Neural Decoding and Signal Detection TheoryВидео

Population Coding and Bayesian Estimation

3.2 Population Coding and Bayesian EstimationВидео

Reading Minds: Stimulus Reconstruction

3.3 Reading Minds: Stimulus ReconstructionВидео

Guest Lecture

Fred Rieke on Visual Processing in the RetinaВидео

Supplementary Video Tutorials

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

Graded Quiz

Neural DecodingЗадание
04Information Theory & Neural Coding (Adrienne Fairhall)7 материалов

Reading for Week 4

Week 4 Lecture Notes and Supplementary MaterialЧтение

Entropy and Spike Trains

4.1 Information and EntropyВидео4.2 Calculating Information in Spike TrainsВидео

Coding Principles

4.3 Coding PrinciplesВидео

Supplementary Video Tutorials

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

Graded Quiz

Information Theory & Neural CodingЗадание
05Computing in Carbon (Adrienne Fairhall)9 материалов

Reading for Week 5

Week 5 Lecture Notes and Supplementary MaterialЧтение

Modeling Neurons and Spikes

5.1 Modeling NeuronsВидео5.2 SpikesВидео

Simplified Model Neurons and Dendrites

5.3 Simplified Model NeuronsВидео5.4 A Forest of DendritesВидео

Guest Lecture

Eric Shea-Brown on Neural Correlations and SynchronyВидео

Supplementary Video Tutorials

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

Graded Quiz

Computing in CarbonЗадание
06Computing with Networks (Rajesh Rao)5 материалов

Reading for Week 6

Week 6 Lecture Notes and TutorialsЧтение

Modeling Connections Between Neurons

6.1 Modeling Connections Between NeuronsВидео

Introduction to Network Models

6.2 Introduction to Network ModelsВидео

The Fascinating World of Recurrent Networks

6.3 The Fascinating World of Recurrent NetworksВидео

Graded Quiz

Computing with NetworksЗадание
07Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao)6 материалов

Reading for Week 7

Week 7 Lecture Notes and TutorialsЧтение

Synaptic Plasticity, Hebb's Rule, and Statistical Learning

7.1 Synaptic Plasticity, Hebb's Rule, and Statistical LearningВидео

Introduction to Unsupervised Learning

7.2 Introduction to Unsupervised LearningВидео

Sparse Coding and Predictive Coding

7.3 Sparse Coding and Predictive CodingВидео

Supplementary Video Tutorial

Gradient Ascent and Descent (by Rich Pang)Видео

Graded Quiz

Networks that LearnЗадание
08Learning from Supervision and Rewards (Rajesh Rao)6 материалов

Reading for Week 8

Week 8 Lecture Notes and Supplementary MaterialЧтение

Neurons as Classifiers and Supervised Learning

8.1 Neurons as Classifiers and Supervised LearningВидео

Reinforcement Learning

8.2 Reinforcement Learning: Predicting RewardsВидео8.3 Reinforcement Learning: Time for Action!Видео

Guest Lecture

Eb Fetz on Bidirectional Brain-Computer InterfacesВидео

Graded Quiz

Learning from Supervision and RewardsЗадание