Computational Neuroscience
University of Washington
Informações sobre o curso
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.
Habilidades que você terá
Artificial Neural NetworkMatlabMathematical ModelPython Programming
Syllabus - What you will learn from this course
1
Section
Introduction & Basic Neurobiology (Rajesh Rao)
This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.
6 videos (Total 89 min), 6 readings, 2 quizzes
6 videos
1.2 Computational Neuroscience: Descriptive Models11m
1.3 Computational Neuroscience: Mechanistic and Interpretive Models12m
1.4 The Electrical Personality of Neurons23m
1.5 Making Connections: Synapses20m
1.6 Time to Network: Brain Areas and their Function17m
6 readings
Welcome Message & Course Logistics10m
About the Course Staff10m
Syllabus and Schedule10m
Matlab & Octave Information and Tutorials10m
Python Information and Tutorials10m
Week 1 Lecture Notes10m
2 practice exercises
Matlab/Octave Programming0m
Python Programming0m
2
Section
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 videos (Total 167 min), 3 readings, 1 quiz
8 videos
2.2 Neural Encoding: Simple Models12m
2.3 Neural Encoding: Feature Selection22m
2.4 Neural Encoding: Variability23m
Vectors and Functions (by Rich Pang)30m
Convolutions and Linear Systems (by Rich Pang)16m
Change of Basis and PCA (by Rich Pang)18m
Welcome to the Eigenworld! (by Rich Pang)24m
3 readings
Welcome Message10m
Week 2 Lecture Notes and Tutorials10m
IMPORTANT: Quiz Instructions10m
1 practice exercises
Spike Triggered Averages: A Glimpse Into Neural Encoding0m
3
Section
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 videos (Total 114 min), 2 readings, 1 quiz
6 videos
3.2 Population Coding and Bayesian Estimation24m
3.3 Reading Minds: Stimulus Reconstruction11m
Fred Rieke on Visual Processing in the Retina14m
Gaussians in One Dimension (by Rich Pang)30m
Probability distributions in 2D and Bayes' Rule (by Rich Pang)13m
2 readings
Welcome Message10m
Week 3 Lecture Notes and Supplementary Material10m
1 practice exercises
Neural Decoding30m
4
Section
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 videos (Total 98 min), 2 readings, 1 quiz
5 videos
4.2 Calculating Information in Spike Trains17m
4.3 Coding Principles19m
What's up with entropy? (by Rich Pang)25m
Information theory? That's crazy! (by Rich Pang)16m
2 readings
Welcome Message10m
Week 4 Lecture Notes and Supplementary Material10m
1 practice exercises
Information Theory & Neural Coding0m
5
Section
Computing in Carbon (Adrienne Fairhall)
This module takes you into the world of biophysics of neurons, where you will meet one of the most famous mathematical models in neuroscience, the Hodgkin-Huxley model of action potential (spike) generation. We will also delve into other models of neurons and learn how to model a neuron's structure, including those intricate branches called dendrites.
7 videos (Total 114 min), 2 readings, 1 quiz
7 videos
5.2 Spikes14m
5.3 Simplified Model Neurons18m
5.4 A Forest of Dendrites19m
Eric Shea-Brown on Neural Correlations and Synchrony22m
Dynamical Systems Theory Intro Part 1: Fixed points (by Rich Pang)11m
Dynamical Systems Theory Intro Part 2: Nullclines (by Rich Pang)13m
2 readings
Welcome Message10m
Week 5 Lecture Notes and Supplementary Material10m
1 practice exercises
Computing in Carbon30m
6
Section
Computing with Networks (Rajesh Rao)
This module explores how models of neurons can be connected to create network models. The first lecture shows you how to model those remarkable connections between neurons called synapses. This lecture will leave you in the company of a simple network of integrate-and-fire neurons which follow each other or dance in synchrony. In the second lecture, you will learn about firing rate models and feedforward networks, which transform their inputs to outputs in a single "feedforward" pass. The last lecture takes you to the dynamic world of recurrent networks, which use feedback between neurons for amplification, memory, attention, oscillations, and more!
3 videos (Total 72 min), 2 readings, 1 quiz
3 videos
6.2 Introduction to Network Models21m
6.3 The Fascinating World of Recurrent Networks25m
2 readings
Welcome Message10m
Week 6 Lecture Notes and Tutorials10m
1 practice exercises
Computing with Networks0m
7
Section
Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao)
This module investigates models of synaptic plasticity and learning in the brain, including a Canadian psychologist's prescient prescription for how neurons ought to learn (Hebbian learning) and the revelation that brains can do statistics (even if we ourselves sometimes cannot)! The next two lectures explore unsupervised learning and theories of brain function based on sparse coding and predictive coding.
4 videos (Total 86 min), 2 readings, 1 quiz
4 videos
7.2 Introduction to Unsupervised Learning22m
7.3 Sparse Coding and Predictive Coding23m
Gradient Ascent and Descent (by Rich Pang)15m
2 readings
Welcome Message10m
Week 7 Lecture Notes and Tutorials10m
1 practice exercises
Networks that Learn0m
8
Section
Learning from Supervision and Rewards (Rajesh Rao)
In this last module, we explore supervised learning and reinforcement learning. The first lecture introduces you to supervised learning with the help of famous faces from politics and Bollywood, casts neurons as classifiers, and gives you a taste of that bedrock of supervised learning, backpropagation, with whose help you will learn to back a truck into a loading dock.The second and third lectures focus on reinforcement learning. The second lecture will teach you how to predict rewards à la Pavlov's dog and will explore the connection to that important reward-related chemical in our brains: dopamine. In the third lecture, we will learn how to select the best actions for maximizing rewards, and examine a possible neural implementation of our computational model in the brain region known as the basal ganglia. The grand finale: flying a helicopter using reinforcement learning!
4 videos (Total 79 min), 2 readings, 1 quiz
4 videos
8.2 Reinforcement Learning: Predicting Rewards13m
8.3 Reinforcement Learning: Time for Action!19m
Eb Fetz on Bidirectional Brain-Computer Interfaces20m
2 readings
Welcome Message and Concluding Remarks10m
Week 8 Lecture Notes and Supplementary Material10m
1 practice exercises
Learning from Supervision and Rewards0m
4.6
Top Reviews
By 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
By 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.
Instructors
Rajesh P. N. Rao
Professor
Adrienne Fairhall
Associate ProfessorAbout 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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