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Computational Neuroscience

4.6 Filled star Filled star Filled star Filled star Half star
Based on 1,125 ratings
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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.

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What's inside

Syllabus

Introduction & Basic Neurobiology (Rajesh Rao)
This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.
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Traffic lights

Read about what's good
what should give you pause
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

Rigorous introduction to computational neuroscience

According to learners, this course offers a comprehensive and rigorous introduction to computational neuroscience. Students frequently praise the excellent quality of the lectures and explanations provided by the instructors, finding them clear and informative. The integration of practical examples and exercises using Python is highlighted as particularly useful for solidifying understanding. However, a significant point of feedback is the course's high level of difficulty and the need for a strong background in mathematics (linear algebra, calculus) and programming, which some learners felt was not adequately stressed beforehand. While challenging, many who come prepared find the course highly rewarding.
Covers wide range of important topics.
"A very comprehensive introduction. The topics covered are vast and relevant."
"A deep dive into the computational aspects. Covers a lot of ground from single neurons to networks and learning."
"Excellent content, covers foundational models and modern approaches."
Hands-on coding solidifies understanding.
"The use of Python demos was incredibly helpful for understanding the concepts."
"The blend of theory and practical examples using Python is perfect."
"The practical implementation (using Python) is key."
"The Python exercises are good, but the assignments can be frustratingly difficult."
Professors explain complex topics clearly.
"Absolutely fantastic course! The lectures are clear and build upon each other nicely."
"Excellent course! Prof. Rao and Fairhall explain complex topics with clarity."
"Hands down the best online course I've taken... The professors are experts and explain things very well."
"The theoretical concepts are explained rigorously..."
Exercises are difficult but rewarding.
"The assignments were challenging but fair, and really solidified my understanding."
"The assignments are very difficult if you don't have this background."
"Wish there were more guided examples or less abstract problems in assignments."
"The assignments are tough, pushing you to really understand the material."
Very challenging, needs solid math/coding.
"The course assumes a higher level of prerequisite knowledge than advertised, especially in linear algebra and differential equations."
"The mathematical depth is significant, so be prepared."
"As a biology student, I found the mathematical parts very intimidating and hard to grasp."
"Requires significant effort and prior knowledge in math (calc, linear algebra) and programming."
"Be prepared for a steep learning curve if your math/programming isn't strong."

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
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
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
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
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
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
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
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
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.

Career center

Learners who complete Computational Neuroscience will develop knowledge and skills that may be useful to these careers:
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.
See salaries and explore the career path for Neuroscientist
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.
See salaries and explore the career path for Neurologist
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.
See salaries and explore the career path for Artificial Intelligence Researcher
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.
See salaries and explore the career path for Computational Biologist
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.
See salaries and explore the career path for Biostatistician
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.
See salaries and explore the career path for Machine Learning Engineer
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.
See salaries and explore the career path for Cognitive Scientist
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.
See salaries and explore the career path for Quantitative Analyst
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Professor
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.
See salaries and explore the career path for Software Engineer
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.
See salaries and explore the career path for Science Editor
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.
See salaries and explore the career path for Science Writer
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.
See salaries and explore the career path for Research Scientist
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.
See salaries and explore the career path for Technical Writer

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.
Cover image
An Introduction to Neural Networks
Save
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.
An Introduction to Neural Networks
Paperback
$$$
An Introduction to Neural Networks
Kindle Edition
$$$
Cover image
Principles of Neural Coding
Save
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.
Principles of Neural Coding
Hardcover
$$$$
Principles of Neural Coding
Kindle Edition
$$$$
Cover image
Advances in Neural Information Processing Systems 17
Save
Neural Information Processing graduate-level textbook that covers the mathematical foundations of neural information processing, including topics such as information theory, statistical learning, and neural networks. It provides a solid theoretical background for understanding the computational principles of the brain.
Advances in Neural Information Processing Systems 17
Paperback
Check
Advances in Neural Information Processing Systems 17
Kindle Edition
Check
Cover image
Information Theory, Inference and Learning...
Save
Provides a comprehensive overview of information theory, inference, and learning algorithms, which are fundamental concepts in computational neuroscience. It covers topics such as probability distributions, entropy, Bayesian inference, and machine learning.
Information Theory, Inference and Learning...
Hardcover
$$$
Cover image
Nonlinear Filters
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.
Nonlinear Filters: Theory and Applications
Hardcover
$$$$
Nonlinear Filters: Theory and Applications
Kindle Edition
$$$$
Cover image
Reinforcement Learning, second edition
Save
Classic introduction to reinforcement learning, which type of machine learning that involves learning through interaction with the environment. It covers topics such as Markov decision processes, value functions, and policy optimization.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Deep Learning
Save
Deep Learning provides a comprehensive overview of deep learning, which subfield of machine learning that uses artificial neural networks with multiple layers. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Neural Organization
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.
Neural Organization: Structure, Function, and...
Hardcover
$$$
Cover image
After Phrenology
Save
The Handbook of Brain Theory and Neural Networks comprehensive reference book that covers a wide range of topics in computational neuroscience, including neural networks, learning algorithms, and cognitive architectures. It provides a valuable resource for researchers and students in the field.
After Phrenology: Neural Reuse and the Interactive...
Kindle Edition
$$
Cover image
Fundamentals of Computational Neuroscience
Save
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.
Fundamentals of Computational Neuroscience: Third...
Paperback
$$
Fundamentals of Computational Neuroscience: Third...
Kindle Edition
$$

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Course image
Rating
4.6 Filled star Filled star Filled star Filled star Half star
Effort
Approx. 26 hours
Level
Introductory
Via
Coursera
Institution
University of Washington
Instructors
Rajesh P. N. Rao
Adrienne Fairhall
Language
English
Transcripts
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Traffic lights
Read about what's good
what should give you pause
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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