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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计算神经科学
华盛顿大学課程信息
您將獲得的技能
- Computational Neuroscience
- Artificial Neural Network
- Reinforcement Learning
- Biological Neuron Model
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华盛顿大学
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.
授課大綱 - 您將從這門課程中學到什麼
Introduction & Basic Neurobiology (Rajesh Rao)
This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.
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.
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.
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.
審閱
- 5 stars71.41%
- 4 stars22.21%
- 3 stars4.03%
- 2 stars1.59%
- 1 star0.74%
來自计算神经科学的熱門評論
A very nice introduction to Computational Neuroscience world. The main course advantage is the matching between theory and practice (programming).
This course is an excellent introduction to the field of computational neuroscience, with engaging lectures and interesting assignments that make learning the material easy.
As a self-paced student, I like this kind of course. I hope to see a whole specialization in this field with final capstone project. Thanks.
interesting instructor and interesting content. Now I know more about the theoretical research related to neuro function and its connection to machine learning now.
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