We may earn an affiliate commission when you visit our partners.
Wulfram Gerstner
This course gives an introduction to the field of theoretical and computational neuroscience with a focus on models of single neurons. Neurons encode information about stimuli in a sequence of short electrical pulses (spikes). Students will learn how mathematical tools such as differential equations, phase plane analysis, separation of time scales, and stochastic processes can be used to understand the dynamics of neurons and the neural code.
Week 1: A first simple neuron model
Week 2: Hodgkin-Huxley models and biophysical modeling
Week 3: Two-dimensional models and phase plane analysis
Read more
This course gives an introduction to the field of theoretical and computational neuroscience with a focus on models of single neurons. Neurons encode information about stimuli in a sequence of short electrical pulses (spikes). Students will learn how mathematical tools such as differential equations, phase plane analysis, separation of time scales, and stochastic processes can be used to understand the dynamics of neurons and the neural code.
Week 1: A first simple neuron model
Week 2: Hodgkin-Huxley models and biophysical modeling
Week 3: Two-dimensional models and phase plane analysis
Read more
This course gives an introduction to the field of theoretical and computational neuroscience with a focus on models of single neurons. Neurons encode information about stimuli in a sequence of short electrical pulses (spikes). Students will learn how mathematical tools such as differential equations, phase plane analysis, separation of time scales, and stochastic processes can be used to understand the dynamics of neurons and the neural code.
Week 1: A first simple neuron model
Week 2: Hodgkin-Huxley models and biophysical modeling
Week 3: Two-dimensional models and phase plane analysis
Week 4: Two-dimensional models (cont.)/ Dendrites
Week 5: Variability of spike trains and the neural code
Week 6: Noise models, noisy neurons and coding
Week 7: Estimating neuron models for coding and decoding
Before your course starts, try the new edX Demo where you can explore the fun, interactive learning environment and virtual labs. Learn more.
Register for this course and see more details by visiting:
OpenCourser.com/course/rzablr/edx
What's inside
Learning objective
How mathematical tools such as differential equations, phase plane analysis, separation of time scales, and stochastic processes can be used to understand the dynamics of neurons and the neural code
How mathematical tools such as differential equations, phase plane analysis, separation of time scales, and stochastic processes can be used to understand the dynamics of neurons and the neural code
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops a strong foundation in theoretical and computational neuroscience for learners with no background in the subject
Strengthens an existing foundation for intermediate learners wanting to develop their understanding of theoretical and computational neuroscience
Suitable for learners with a strong mathematical background, including differential equations, phase plane analysis, separation of time scales, and stochastic processes
Save this course
Save Neuronal Dynamics to your list so you can find it easily later:
Save
Save
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 Neuronal Dynamics with these
activities:
Familiarize yourself with basic neuroscience concepts
Show steps
This course will cover advanced concepts in neuroscience. Review essential neuroscience concepts to prepare for this course.
Browse courses on
Neuroscience
Show steps
-
Read introductory neuroscience textbooks or articles.
-
Attend an introductory neuroscience lecture or workshop.
Read 'Principles of Neural Science'
Show steps
This comprehensive textbook provides an in-depth foundation in neuroscience. Reading it will enhance your understanding of the course material.
View
Principles of Neural Science, Fifth Edition...
on Amazon
Show steps
-
Read the assigned chapters thoroughly.
-
Take notes and highlight important concepts.
Brush up on Calculus
Show steps
This course relies heavily on mathematical tools, especially Calculus. Refresh your understanding of basic Calculus concepts to prepare for this course.
Browse courses on
Calculus
Show steps
-
Review fundamental concepts of limits, derivatives, and integrals.
-
Practice solving basic Calculus problems.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group for the course
Show steps
Collaborating with peers can enhance your understanding and provide diverse perspectives on course material.
Show steps
-
Find or create a study group with classmates.
-
Set regular meeting times and topics for discussion.
-
Actively participate in group discussions and share insights.
Follow online tutorials on neural coding
Show steps
Neural coding is a key concept in neuroscience. Follow online tutorials to enhance your understanding of how neurons encode information.
Show steps
-
Search for reputable online tutorials on neural coding.
-
Follow the tutorials, taking notes and completing any exercises.
-
Discuss your findings with classmates or instructors.
Solve practice problems on neuron models
Show steps
Mathematical modeling is crucial for understanding neuron dynamics. Practice solving problems involving neuron models to strengthen your understanding.
Show steps
-
Find practice problems online or in textbooks.
-
Attempt to solve the problems independently.
-
Check your solutions with the provided answer key.
Develop a presentation on different neuron models
Show steps
Creating a presentation will help you synthesize your knowledge of different neuron models and improve your communication skills.
Show steps
-
Research and gather information on neuron models.
-
Organize the information into a logical flow.
-
Design visually appealing slides using appropriate software.
-
Practice presenting your content to an audience.
Build a simple neural network model
Show steps
Hands-on experience with neural networks can reinforce your understanding of their principles. Building a simple model will provide practical knowledge.
Browse courses on
Neural Networks
Show steps
-
Choose a neural network architecture and programming language.
-
Implement the model and train it on a small dataset.
-
Evaluate the model's performance and make adjustments if necessary.
Familiarize yourself with basic neuroscience concepts
Show steps
This course will cover advanced concepts in neuroscience. Review essential neuroscience concepts to prepare for this course.
Browse courses on
Neuroscience
Show steps
Show steps
Show steps
- Read introductory neuroscience textbooks or articles.
- Attend an introductory neuroscience lecture or workshop.
Read 'Principles of Neural Science'
Show steps
This comprehensive textbook provides an in-depth foundation in neuroscience. Reading it will enhance your understanding of the course material.
View
Principles of Neural Science, Fifth Edition...
on Amazon
Show steps
Show steps
Show steps
- Read the assigned chapters thoroughly.
- Take notes and highlight important concepts.
Brush up on Calculus
Show steps
This course relies heavily on mathematical tools, especially Calculus. Refresh your understanding of basic Calculus concepts to prepare for this course.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Review fundamental concepts of limits, derivatives, and integrals.
- Practice solving basic Calculus problems.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group for the course
Show steps
Collaborating with peers can enhance your understanding and provide diverse perspectives on course material.
Show steps
Show steps
Show steps
- Find or create a study group with classmates.
- Set regular meeting times and topics for discussion.
- Actively participate in group discussions and share insights.
Follow online tutorials on neural coding
Show steps
Neural coding is a key concept in neuroscience. Follow online tutorials to enhance your understanding of how neurons encode information.
Show steps
Show steps
Show steps
- Search for reputable online tutorials on neural coding.
- Follow the tutorials, taking notes and completing any exercises.
- Discuss your findings with classmates or instructors.
Solve practice problems on neuron models
Show steps
Mathematical modeling is crucial for understanding neuron dynamics. Practice solving problems involving neuron models to strengthen your understanding.
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Attempt to solve the problems independently.
- Check your solutions with the provided answer key.
Develop a presentation on different neuron models
Show steps
Creating a presentation will help you synthesize your knowledge of different neuron models and improve your communication skills.
Show steps
Show steps
Show steps
- Research and gather information on neuron models.
- Organize the information into a logical flow.
- Design visually appealing slides using appropriate software.
- Practice presenting your content to an audience.
Build a simple neural network model
Show steps
Hands-on experience with neural networks can reinforce your understanding of their principles. Building a simple model will provide practical knowledge.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Choose a neural network architecture and programming language.
- Implement the model and train it on a small dataset.
- Evaluate the model's performance and make adjustments if necessary.
Career center
Learners who complete Neuronal Dynamics will develop knowledge and skills
that may be useful to these careers:
Computational Neuroscientist
Computational Neuroscientist
A Computational Neuroscientist develops mathematical and computational models of neurons and neural systems. This course in Neuronal Dynamics would be very useful in developing the skills needed to build these models.
Neuroengineer
Neuroengineer
A Neuroengineer designs and builds devices that interact with the nervous system. This course in Neuronal Dynamics may be useful in understanding the dynamics of neurons and the neural code, which are important for designing devices that interact with the nervous system.
Neuroscientist
Neuroscientist
A Neuroscientist studies the nervous system. This course in Neuronal Dynamics may be useful in understanding the dynamics of neurons and the neural code, which are fundamental concepts in neuroscience.
Biophysicist
Biophysicist
A Biophysicist studies the physical properties of biological systems. This course in Neuronal Dynamics may be useful in understanding the biophysical properties of neurons.
Data Scientist
Data Scientist
A Data Scientist analyzes data to extract meaningful insights. This course in Neuronal Dynamics may be useful in developing the skills needed to analyze data from neuroscience experiments.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models. This course in Neuronal Dynamics may be useful in developing the skills needed to build machine learning models for neuroscience applications.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course in Neuronal Dynamics may be useful in developing the skills needed to build mathematical models for financial applications.
Electrical Engineer
Electrical Engineer
An Electrical Engineer designs, develops, and maintains electrical systems. This course in Neuronal Dynamics may be useful in developing the skills needed to build electrical systems for neuroscience applications.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course in Neuronal Dynamics may be useful in developing the skills needed to build software systems for neuroscience applications.
Mechanical Engineer
Mechanical Engineer
A Mechanical Engineer designs, develops, and maintains mechanical systems. This course in Neuronal Dynamics may be useful in developing the skills needed to build mechanical systems for neuroscience applications.
Chemist
Chemist
A Chemist studies the properties and reactions of matter. This course in Neuronal Dynamics may be useful in understanding the chemical properties of neurons.
Statistician
Statistician
A Statistician develops and applies statistical methods to analyze data. This course in Neuronal Dynamics may be useful in developing the skills needed to analyze data from neuroscience experiments.
Physicist
Physicist
A Physicist studies the laws of nature. This course in Neuronal Dynamics may be useful in understanding the physical properties of neurons.
Biologist
Biologist
A Biologist studies living organisms. This course in Neuronal Dynamics may be useful in understanding the biology of neurons.
Mathematician
Mathematician
A Mathematician studies the properties of numbers and shapes. This course in Neuronal Dynamics may be useful in developing the skills needed to build mathematical models for neuroscience applications.
For more career information including salaries, visit:
OpenCourser.com/course/rzablr/edx
Reading list
We've selected 35 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
Neuronal Dynamics.
Save
Provides a comprehensive overview of the neural code, covering both the theoretical and experimental aspects. It valuable resource for students and researchers in neuroscience.
Covers the field of neuroscience, including the nervous system, its structure, function, development, and evolution. It would be particularly useful for those seeking background knowledge of the field or those taking the course to prepare for further study. It is commonly used as a textbook at academic institutions.
Provides a comprehensive introduction to computational neuroscience and would be particularly useful to those interested in learning more about the mathematical and computational aspects of the field. It covers a range of topics, including neural networks, learning and memory, and decision-making.
Provides a comprehensive overview of the field of neuroscience, covering a wide range of topics relevant to the course, including neuronal dynamics, biophysical modeling, and the neural code.
This classic textbook provides a comprehensive overview of the field of neuroscience. It valuable resource for students and researchers in neuroscience.
Offers a comprehensive guide to computational modeling of single neurons, covering a wide range of models and techniques.
Provides a solid foundation in the computational principles underlying neuronal dynamics, with a focus on mathematical models and simulations.
Provides a comprehensive overview of theoretical neuroscience. It valuable resource for students and researchers in neuroscience.
Covers stochastic processes and their applications in cell biology, providing a foundation for understanding variability in neuronal dynamics.
Offers a comprehensive overview of artificial intelligence, with a focus on its applications in neuroscience and neural modeling.
Provides a gentle introduction to neural networks, with a focus on their applications in modeling neuronal dynamics.
Save
Provides a comprehensive overview of neuronal dynamics. It valuable resource for students and researchers in neuroscience.
Save
Is useful for those interested in learning about the field of cognitive psychology. It covers a range of topics, including perception, attention, memory, language, and problem-solving.
Save
Provides an introduction to the computational approach to understanding the brain and would be of interest to those interested in the mathematical and computational aspects of neuroscience. It covers topics such as neural networks, learning, and memory.
This textbook provides an introduction to the field of neural engineering, which focuses on the interaction between the nervous system and artificial devices.
Provides a comprehensive overview of the biophysics of computation. It valuable resource for students and researchers in neuroscience.
Provides a comprehensive overview of the field of deep learning for coders. It covers a wide range of topics, from the basics of deep learning to the latest advances in artificial intelligence.
Provides a comprehensive overview of stochastic processes in cell biology. It valuable resource for students and researchers in neuroscience.
Save
Covers the field of neural networks and provides an overview of the different types of neural networks and their applications. It would be useful for those interested in learning more about the mathematical and computational aspects of neural networks.
This textbook provides a comprehensive overview of the neural basis of cognition.
Save
Provides a comprehensive overview of the field of neurobiology of computation, the study of the brain as a computational system. It covers a wide range of topics, from the basics of neural networks to the latest advances in artificial intelligence.
Introduces the field of reinforcement learning and is appropriate for those interested in learning about the subject. It covers topics such as Markov decision processes, value functions, and reinforcement learning algorithms.
Save
Serves as a comprehensive introduction to deep learning and is suitable for those interested in learning about the field. It covers topics such as supervised and unsupervised learning, convolutional neural networks, and recurrent neural networks.
Save
Provides a comprehensive overview of neural networks. It valuable resource for students and researchers in neuroscience.
Provides a comprehensive overview of machine learning. It valuable resource for students and researchers in neuroscience.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It valuable resource for students and researchers in neuroscience.
Delves into the field of statistical learning and is recommended for those interested in learning about the topic. It covers topics such as linear regression, generalized linear models, and support vector machines.
Save
This textbook provides a comprehensive overview of the field of cellular and molecular neurophysiology.
This textbook provides a comprehensive overview of the field of molecular neuropharmacology.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It valuable resource for students and researchers in neuroscience.
Save
Provides a comprehensive overview of pattern recognition and machine learning. It valuable resource for students and researchers in neuroscience.
Provides a practical approach to machine learning and is recommended for those interested in implementing machine learning algorithms using popular libraries such as Scikit-Learn, Keras, and TensorFlow.
This atlas provides a detailed overview of the anatomy of the nervous system.
Provides a comprehensive overview of the elements of statistical learning. It valuable resource for students and researchers in neuroscience.
Save
Provides an introduction to computational models of cognitive processes, making it a valuable resource for students interested in this topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rzablr/edx
Share
Similar courses
Here are nine courses similar to
Neuronal Dynamics.
Building Regression Models Using TensorFlow 1
OpenCourser.com/course/oz5lus/building
Building Regression Models Using TensorFlow 1
Most relevant
Computational Neuroscience: Neuronal Dynamics of Cognition
OpenCourser.com/course/9yr3i7/computational
Computational Neuroscience: Neuronal Dynamics of Cognition
Most relevant
Fundamentals of Neuroscience, Part 2: Neurons and Networks
OpenCourser.com/course/n5vt4u/edx
Fundamentals of Neuroscience, Part 2: Neurons and Networks
Most relevant
Simulation Neuroscience
OpenCourser.com/course/rzhuk7/simulation
Simulation Neuroscience
Most relevant
High-Dimensional Data Analysis
OpenCourser.com/course/w6qmt0/high
High-Dimensional Data Analysis
Fundamentals of Neuroscience, Part 1: The Electrical Properties of the Neuron
OpenCourser.com/course/6jqe9v/edx
Fundamentals of Neuroscience, Part 1: The Electrical...
Deep Learning Topics with Computer Vision and NLP
OpenCourser.com/course/wm939a/deep
Deep Learning Topics with Computer Vision and NLP
Two-Phase Pipe Hydraulics and Pipe Sizing
OpenCourser.com/course/dp68lo/two
Two-Phase Pipe Hydraulics and Pipe Sizing
Deep Learning with Keras 2
OpenCourser.com/course/12ilh1/deep
Deep Learning with Keras 2
Effort
5 to 7 hours per week for 7 weeks
Level
Advanced
Via
edX
Institution
École Polytechnique Fédérale de Lausanne
Instructor
Wulfram Gerstner
Language
English
Transcript
English
Good to know
Develops a strong foundation in theoretical and computational neuroscience for learners with no background in the subject
Strengthens an existing foundation for intermediate learners wanting to develop their understanding of theoretical and computational neuroscience
Suitable for learners with a strong mathematical background, including differential equations, phase plane analysis, separation of time scales, and stochastic processes
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser