Computational Neuroscience
от партнера
Computational Neuroscience
University of Washington
Об этом курсе
4.6
423 ratings
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.
Приобретаемые навыки
Computational NeuroscienceArtificial Neural NetworkReinforcement LearningBiological Neuron Model
Программа курса: что вы изучите
1
Раздел
Introduction & Basic Neurobiology (Rajesh Rao)
This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.
6 видео (всего 89 мин.), 6 материалов для самостоятельного изучения, 2 тестов
6 видео
1.2 Computational Neuroscience: Descriptive Models11мин
1.3 Computational Neuroscience: Mechanistic and Interpretive Models12мин
1.4 The Electrical Personality of Neurons23мин
1.5 Making Connections: Synapses20мин
1.6 Time to Network: Brain Areas and their Function17мин
6 материала для самостоятельного изучения
Welcome Message & Course Logistics10мин
About the Course Staff10мин
Syllabus and Schedule10мин
Matlab & Octave Information and Tutorials10мин
Python Information and Tutorials10мин
Week 1 Lecture Notes10мин
2 практического упражнения
Matlab/Octave Programmingмин
Python Programmingмин
2
Раздел
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.
8 видео (всего 167 мин.), 3 материалов для самостоятельного изучения, 1 тест
8 видео
2.2 Neural Encoding: Simple Models12мин
2.3 Neural Encoding: Feature Selection22мин
2.4 Neural Encoding: Variability23мин
Vectors and Functions (by Rich Pang)30мин
Convolutions and Linear Systems (by Rich Pang)16мин
Change of Basis and PCA (by Rich Pang)18мин
Welcome to the Eigenworld! (by Rich Pang)24мин
3 материала для самостоятельного изучения
Welcome Message10мин
Week 2 Lecture Notes and Tutorials10мин
IMPORTANT: Quiz Instructions10мин
1 практическое упражнение
Spike Triggered Averages: A Glimpse Into Neural Encodingмин
3
Раздел
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.
6 видео (всего 114 мин.), 2 материалов для самостоятельного изучения, 1 тест
6 видео
3.2 Population Coding and Bayesian Estimation24мин
3.3 Reading Minds: Stimulus Reconstruction11мин
Fred Rieke on Visual Processing in the Retina14мин
Gaussians in One Dimension (by Rich Pang)30мин
Probability distributions in 2D and Bayes' Rule (by Rich Pang)13мин
2 материала для самостоятельного изучения
Welcome Message10мин
Week 3 Lecture Notes and Supplementary Material10мин
1 практическое упражнение
Neural Decoding30мин
4
Раздел
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.
5 видео (всего 98 мин.), 2 материалов для самостоятельного изучения, 1 тест
5 видео
4.2 Calculating Information in Spike Trains17мин
4.3 Coding Principles19мин
What's up with entropy? (by Rich Pang)25мин
Information theory? That's crazy! (by Rich Pang)16мин
2 материала для самостоятельного изучения
Welcome Message10мин
Week 4 Lecture Notes and Supplementary Material10мин
1 практическое упражнение
Information Theory & Neural Codingмин
4.6
Лучшие рецензии
автор: JR•Apr 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
автор: CM•Jun 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.
Преподавателя
Rajesh P. N. Rao
ProfessorComputer Science & Engineering
Adrienne Fairhall
Associate ProfessorPhysiology and Biophysics
О 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.
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When will I have access to the lectures and assignments?
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.
What will I get if I purchase the Certificate?
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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Is financial aid available?
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