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.
Neurociência Computacional
oferecido por
Universidade de Washington
Informações sobre o curso
4.6
476 classificações
Habilidades que você terá
Computational NeuroscienceArtificial Neural NetworkReinforcement LearningBiological Neuron Model
Programa - O que você aprenderá com este curso
Semana
14 horas para concluir
Introduction & Basic Neurobiology (Rajesh Rao)
This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.
6 vídeos (total de (Total 89 mín.) min), 6 leituras, 2 testes
6 videos
1.2 Computational Neuroscience: Descriptive Models11min
1.3 Computational Neuroscience: Mechanistic and Interpretive Models12min
1.4 The Electrical Personality of Neurons23min
1.5 Making Connections: Synapses20min
1.6 Time to Network: Brain Areas and their Function17min
6 leituras
Welcome Message & Course Logistics10min
About the Course Staff10min
Syllabus and Schedule10min
Matlab & Octave Information and Tutorials10min
Python Information and Tutorials10min
Week 1 Lecture Notes10min
2 exercícios práticos
Matlab/Octave Programmings
Python Programmings
Semana
24 horas para concluir
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 vídeos (total de (Total 167 mín.) min), 3 leituras, 1 teste
8 videos
2.2 Neural Encoding: Simple Models12min
2.3 Neural Encoding: Feature Selection22min
2.4 Neural Encoding: Variability23min
Vectors and Functions (by Rich Pang)30min
Convolutions and Linear Systems (by Rich Pang)16min
Change of Basis and PCA (by Rich Pang)18min
Welcome to the Eigenworld! (by Rich Pang)24min
3 leituras
Welcome Message10min
Week 2 Lecture Notes and Tutorials10min
IMPORTANT: Quiz Instructions10min
1 exercício prático
Spike Triggered Averages: A Glimpse Into Neural Encodings
Semana
33 horas para concluir
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 vídeos (total de (Total 114 mín.) min), 2 leituras, 1 teste
6 videos
3.2 Population Coding and Bayesian Estimation24min
3.3 Reading Minds: Stimulus Reconstruction11min
Fred Rieke on Visual Processing in the Retina14min
Gaussians in One Dimension (by Rich Pang)30min
Probability distributions in 2D and Bayes' Rule (by Rich Pang)13min
2 leituras
Welcome Message10min
Week 3 Lecture Notes and Supplementary Material10min
1 exercício prático
Neural Decoding30min
Semana
43 horas para concluir
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 vídeos (total de (Total 98 mín.) min), 2 leituras, 1 teste
5 videos
4.2 Calculating Information in Spike Trains17min
4.3 Coding Principles19min
What's up with entropy? (by Rich Pang)25min
Information theory? That's crazy! (by Rich Pang)16min
2 leituras
Welcome Message10min
Week 4 Lecture Notes and Supplementary Material10min
1 exercício prático
Information Theory & Neural Codings
22%
consegui um benefício significativo de carreira com este curso
Melhores avaliações
por 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.
por AT•May 27th 2018
This is a wonderful start for a biologist , to get idea of concepts of learning . An advanced course focused more on brain circuitry is suggested.\n\nThanks a lot
Instrutores
Rajesh P. N. Rao
ProfessorComputer Science & Engineering
Adrienne Fairhall
Associate ProfessorPhysiology and Biophysics
Sobre Universidade de 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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