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Rajesh P. N. Rao
and
Adrienne Fairhall
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/stcszn/computational
What's inside
Syllabus
Introduction & Basic Neurobiology (Rajesh Rao)
This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.
Read more
Syllabus
Introduction & Basic Neurobiology (Rajesh Rao)
This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.
Read more
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.
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.
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!
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.
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!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a solid introduction to the field of computational neuroscience for advanced undergraduates and beginning graduate students
Taught by seasoned professionals in the field, Rajesh P. N. Rao and Adrienne Fairhall, who are recognized for their expertise in computational neuroscience
Offers a thorough examination of fundamental computational principles underlying various aspects of the nervous system, such as vision, sensory-motor control, learning, and memory
Utilizes practical examples, Matlab/Octave/Python demonstrations, and exercises to reinforce concepts and enhance understanding
Covers advanced topics such as information theory, neural coding, biophysics of neurons, and learning algorithms, providing a comprehensive overview of the field
May require some background knowledge in mathematics and computer programming for optimal comprehension
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Reviews summary
Neuroscience for beginners
Learners say this course is an engaging introduction to computational neuroscience. Prerequisites in mathematics and programming are required, and the programming exercises can take a lot of time. The lectures and first few lessons are especially helpful for those who are new to computational neuroscience. Overall, students say the course is a solid overview of the field.
A solid overview of computational neuroscience.
"This is a wonderful start for a biologist, to get an idea of the concepts of learning."
"The first lecture series was very good and interesting."
"It's not an introductory course, but the first few lectures ought to be enough if you're looking for a glance into what CN is."
Programming exercises can be time-consuming.
"Even though I've passed the course with high marks, the programming exercises took a lot of time."
Prerequisites in math and programming are required.
"This course was a huge inspiration, but it required a lot of prerequisites in high-level mathematics and programming."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Computational Neuroscience with these
activities:
Review Basic Neurobiology
Show steps
Review the core components of the nervous system to ensure foundational understanding of computational neuroscience.
Browse courses on
Neurons
Show steps
-
In your own words, define the major types of neurons and their functions.
-
Illustrate how neurons communicate with each other at synapses.
-
Research the brain regions involved in vision, audition, and motor control.
Compile a Glossary of Computational Neuroscience Terms
Show steps
Solidify your understanding of key concepts by creating a comprehensive reference of terminology.
Show steps
-
Review course materials and identify important terms.
-
Research additional sources to expand your list of terms.
-
Define each term concisely and clearly.
-
Organize the terms alphabetically or by category.
Connect with Researchers in Computational Neuroscience
Show steps
Seek guidance and support from experts in the field to enhance your understanding and gain insights.
Show steps
-
Identify potential mentors through conferences, research publications, or university websites.
-
Craft a personalized email expressing your interest in their work.
-
Schedule a meeting to discuss your career aspirations and seek mentorship opportunities.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow a Course on Computational Neuroscience
Show steps
Supplement your learning by exploring additional resources and perspectives on computational neuroscience.
Browse courses on
Computational Neuroscience
Show steps
-
Identify reputable online courses in computational neuroscience.
-
Enroll in a course that aligns with your interests and learning goals.
-
Follow the course lectures, complete assignments, and engage in discussions.
Practice Neural Encoding and Decoding
Show steps
Enhance your understanding of how neural activity relates to sensory information and behavior through decoding challenges.
Show steps
-
Simulate the generation of spike trains from a neuron model.
-
Analyze neural recordings to identify patterns and infer sensory input.
-
Implement a simple neural decoder to predict the direction of a moving object.
Attend a Computational Neuroscience Workshop
Show steps
Immerse yourself in a collaborative environment and learn from industry experts to broaden your knowledge and network.
Browse courses on
Computational Neuroscience
Show steps
-
Research and identify computational neuroscience workshops that align with your interests.
-
Register for a workshop and attend all sessions.
-
Engage in discussions with speakers, attendees, and potential collaborators.
Design a Computational Model of a Neuron
Show steps
Create a virtual representation of a neuron to deepen your comprehension of neuronal function and biophysics.
Browse courses on
Computational Neuroscience
Show steps
-
Choose an appropriate neuron model, such as the Hodgkin-Huxley model.
-
Implement the model using a programming language like Python or MATLAB.
-
Simulate the model's response to different stimuli and analyze the output.
-
Compare your model's behavior to real neuron data.
Develop a Neural Network for Image Recognition
Show steps
Apply the concepts of artificial neural networks to a practical problem to grasp their potential and limitations.
Browse courses on
Neural Networks
Show steps
-
Design the architecture and layers of the neural network.
-
Train the network on a dataset of images and labels.
-
Evaluate the performance of the network on a test set.
-
Optimize the hyperparameters of the network to improve accuracy.
Review Basic Neurobiology
Show steps
Review the core components of the nervous system to ensure foundational understanding of computational neuroscience.
Browse courses on
Neurons
Show steps
Show steps
Show steps
- In your own words, define the major types of neurons and their functions.
- Illustrate how neurons communicate with each other at synapses.
- Research the brain regions involved in vision, audition, and motor control.
Compile a Glossary of Computational Neuroscience Terms
Show steps
Solidify your understanding of key concepts by creating a comprehensive reference of terminology.
Show steps
Show steps
Show steps
- Review course materials and identify important terms.
- Research additional sources to expand your list of terms.
- Define each term concisely and clearly.
- Organize the terms alphabetically or by category.
Connect with Researchers in Computational Neuroscience
Show steps
Seek guidance and support from experts in the field to enhance your understanding and gain insights.
Show steps
Show steps
Show steps
- Identify potential mentors through conferences, research publications, or university websites.
- Craft a personalized email expressing your interest in their work.
- Schedule a meeting to discuss your career aspirations and seek mentorship opportunities.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow a Course on Computational Neuroscience
Show steps
Supplement your learning by exploring additional resources and perspectives on computational neuroscience.
Browse courses on
Computational Neuroscience
Show steps
Show steps
Show steps
- Identify reputable online courses in computational neuroscience.
- Enroll in a course that aligns with your interests and learning goals.
- Follow the course lectures, complete assignments, and engage in discussions.
Practice Neural Encoding and Decoding
Show steps
Enhance your understanding of how neural activity relates to sensory information and behavior through decoding challenges.
Show steps
Show steps
Show steps
- Simulate the generation of spike trains from a neuron model.
- Analyze neural recordings to identify patterns and infer sensory input.
- Implement a simple neural decoder to predict the direction of a moving object.
Attend a Computational Neuroscience Workshop
Show steps
Immerse yourself in a collaborative environment and learn from industry experts to broaden your knowledge and network.
Browse courses on
Computational Neuroscience
Show steps
Show steps
Show steps
- Research and identify computational neuroscience workshops that align with your interests.
- Register for a workshop and attend all sessions.
- Engage in discussions with speakers, attendees, and potential collaborators.
Design a Computational Model of a Neuron
Show steps
Create a virtual representation of a neuron to deepen your comprehension of neuronal function and biophysics.
Browse courses on
Computational Neuroscience
Show steps
Show steps
Show steps
- Choose an appropriate neuron model, such as the Hodgkin-Huxley model.
- Implement the model using a programming language like Python or MATLAB.
- Simulate the model's response to different stimuli and analyze the output.
- Compare your model's behavior to real neuron data.
Develop a Neural Network for Image Recognition
Show steps
Apply the concepts of artificial neural networks to a practical problem to grasp their potential and limitations.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Design the architecture and layers of the neural network.
- Train the network on a dataset of images and labels.
- Evaluate the performance of the network on a test set.
- Optimize the hyperparameters of the network to improve accuracy.
Career center
Learners who complete Computational Neuroscience will develop knowledge and skills
that may be useful to these careers:
Neuroscientist
Neuroscientist
As a Neuroscientist, you will research the nervous system and its function. This course is an excellent starting point for aspiring Neuroscientists who want to learn more about computational methods used to understand how the brain processes information and makes decisions.
Neurologist
Neurologist
As a Neurologist, you will diagnose and treat disorders of the nervous system. This course will enhance your understanding of the neural processes underlying brain function and disorders, which is important for making informed clinical decisions and developing effective treatments.
Computational Biologist
Computational Biologist
As a Computational Biologist, you will use computational tools and techniques to study biological systems. This course will provide you with a solid foundation in computational methods that are used to understand the brain and nervous system.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
As an Artificial Intelligence Researcher, you will develop new algorithms and models for artificial intelligence systems. The modules on neural networks, computing with networks, and learning from supervision and rewards provide a foundation for understanding how neural networks can be used to solve complex problems and develop new technologies.
Biostatistician
Biostatistician
As a Biostatistician, you will collect, analyze, and interpret data about health-related issues. This course will provide foundational knowledge in neural information coding and statistical models, which are utilized by Biostatisticians to understand biological systems.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will design and implement machine learning algorithms to solve real-world problems. The modules on neural networks, computing with networks, and learning from supervision and rewards will be especially useful for Machine Learning Engineers who want to apply their skills to neural network-based systems.
Cognitive Scientist
Cognitive Scientist
As a Cognitive Scientist, you will study the mind and its functions, including perception, memory, and learning. This course offers an introduction to computational methods that are used to study the neural basis of cognition, providing a strong foundation for aspiring Cognitive Scientists.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you will use mathematical and statistical models to assess the risk of financial investments. This course provides a framework for applying statistical methods to complex biological systems like the human brain. The modules on representation of information, neural encoding, and information theory in particular can be helpful for building a necessary foundation in mathematical modeling.
Data Scientist
Data Scientist
As a Data Scientist, you will use your skills in statistics and coding to analyze data for patterns and trends. This course can serve as an introduction to computational methods utilized by Data Scientists to understand the brain's function. The course modules on neural encoding and decoding, information theory, as well as network models can build a useful foundation for aspiring Data Scientists.
Professor
Professor
As a Professor, you will teach and conduct research in a specialized field. This course can be especially beneficial for Professors teaching computational neuroscience or related fields.
Software Engineer
Software Engineer
As a Software Engineer, you will apply engineering principles to design, develop, and maintain software systems. The modules on neural networks and computing with networks can be useful for Software Engineers, specifically for those interested in artificial intelligence and machine learning.
Science Editor
Science Editor
As a Science Editor, you will edit and review scientific manuscripts. This course will provide you with the knowledge you need to understand and evaluate computational neuroscience research.
Science Writer
Science Writer
As a Science Writer, you will write about scientific topics for a general audience. This course will provide you with a deep understanding of computational neuroscience, which will enable you to effectively communicate complex scientific concepts to non-experts.
Research Scientist
Research Scientist
As a Research Scientist, you will conduct scientific research in a specialized field. This course can provide a foundation for Research Scientists working in the field of computational neuroscience. The introduction to neural encoding and information theory can be useful for understanding how information is processed in the brain.
Technical Writer
Technical Writer
As a Technical Writer, you will create user manuals, technical reports, and other documentation. This course will provide you with the technical knowledge you need to write about computational neuroscience topics.
For more career information including salaries, visit:
OpenCourser.com/course/stcszn/computational
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Computational Neuroscience.
An Introduction to Neural Networks popular textbook for understanding the principles and applications of neural networks. It provides comprehensive coverage of different network architectures, learning algorithms, and applications in areas such as image recognition, natural language processing, and reinforcement learning.
Focuses on the principles of neural coding, which is the study of how the brain represents information using patterns of neural activity. It provides a comprehensive overview of the field, including topics such as spike trains, population codes, and decoding algorithms.
Neural Information Processing graduate-level textbook that covers the mathematical foundations of neural information processing, including topics such as information theory, statistical learning, and neural networks. It provides a solid theoretical background for understanding the computational principles of the brain.
Provides a comprehensive overview of information theory, inference, and learning algorithms, which are fundamental concepts in computational neuroscience. It covers topics such as probability distributions, entropy, Bayesian inference, and machine learning.
Save
Introduces machine learning techniques for signal processing, including topics such as supervised learning, unsupervised learning, and deep learning. It provides practical examples and applications of machine learning in areas such as image processing, speech recognition, and natural language processing.
Classic introduction to reinforcement learning, which type of machine learning that involves learning through interaction with the environment. It covers topics such as Markov decision processes, value functions, and policy optimization.
Save
Deep Learning provides a comprehensive overview of deep learning, which subfield of machine learning that uses artificial neural networks with multiple layers. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Save
Provides an overview of computational models of brain and behavior, which are mathematical and computer-based models that simulate the structure and function of the brain. It covers topics such as neural networks, cognitive architectures, and artificial intelligence.
Save
The Handbook of Brain Theory and Neural Networks comprehensive reference book that covers a wide range of topics in computational neuroscience, including neural networks, learning algorithms, and cognitive architectures. It provides a valuable resource for researchers and students in the field.
Fundamentals of Computational Neuroscience provides a comprehensive overview of the field, including topics such as neuronal dynamics, network models, and learning algorithms. It useful textbook for undergraduate and graduate students in computational neuroscience.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/stcszn/computational
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Level
Introductory
Via
Coursera
Institution
University of Washington
Instructors
Rajesh P. N. Rao
Adrienne Fairhall
Language
English
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Good to know
Provides a solid introduction to the field of computational neuroscience for advanced undergraduates and beginning graduate students
Taught by seasoned professionals in the field, Rajesh P. N. Rao and Adrienne Fairhall, who are recognized for their expertise in computational neuroscience
Offers a thorough examination of fundamental computational principles underlying various aspects of the nervous system, such as vision, sensory-motor control, learning, and memory
Utilizes practical examples, Matlab/Octave/Python demonstrations, and exercises to reinforce concepts and enhance understanding
Covers advanced topics such as information theory, neural coding, biophysics of neurons, and learning algorithms, providing a comprehensive overview of the field
May require some background knowledge in mathematics and computer programming for optimal comprehension
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