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Wulfram Gerstner
What happens in your brain when you make a decision? And what happens if you recall a memory from your last vacation? Why is our perception of simple objects sometimes strangely distorted? How can millions of neurons in the brain work together without a central control unit?
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What happens in your brain when you make a decision? And what happens if you recall a memory from your last vacation? Why is our perception of simple objects sometimes strangely distorted? How can millions of neurons in the brain work together without a central control unit?
Read more
What happens in your brain when you make a decision? And what happens if you recall a memory from your last vacation? Why is our perception of simple objects sometimes strangely distorted? How can millions of neurons in the brain work together without a central control unit?
This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to answer the above questions. The core of the answer to cognition may lie in the collective dynamics of thousands of interacting neurons - and these dynamics are mathematically analyzed in this course using methods such as mean-field theory and non-linear differential equations.
Register for this course and see more details by visiting:
OpenCourser.com/course/9yr3i7/computational
What's inside
Learning objectives
- Analyze connected networks in the mean-field limit
- Formalize biological facts into mathematical models
- Understand a simple mathematical model of memory formation in the brain
- Understand a simple mathematical model of decision processes
- Understand cortical field models of perception
- By the end of the course,you willbe able to:
- Analyze connected networks in the mean-field limit
- Formalize biological facts into mathematical models
- Understand a simple mathematical model of memory formation in the brain
- Understand a simple mathematical model of decision processes
- Understand cortical field models of perception
- By the end of the course,you willbe able to:
Syllabus
Textbook:
Neuronal Dynamics - from single neurons to networks and models of cognition (W. Gerstner, W.M. Kistler, R. Naud and L. Paninski), Cambridge Univ. Press. 2014
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Syllabus
Textbook:
Neuronal Dynamics - from single neurons to networks and models of cognition (W. Gerstner, W.M. Kistler, R. Naud and L. Paninski), Cambridge Univ. Press. 2014
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Understanding cognitive processes through the lens of theoretical neuroscience
Sturdy grounding in mathematics, specifically mean-field theory and non-linear differential equations, is essential
Offered by renowned instructors in the field of theoretical neuroscience, Wulfram Gerstner
Emphasizes the interactions of thousands of neurons and their impact on cognitive functions
Delves into various cognitive models, including memory formation, decision processes, and perception
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Reviews summary
Rigorous computational neuroscience deep dive
According to students, this course offers a profound and mathematically rigorous exploration into the neuronal dynamics of cognition, ideally suited for learners with strong backgrounds in mathematics and physics. Many find the theoretical concepts challenging yet rewarding, appreciating the depth of the computational models presented. However, some note the demanding prerequisites and the heavy reliance on the accompanying textbook. The course is primarily theory-focused, which is excellent for academic pursuits but may not appeal to those seeking hands-on application. Overall, it provides an excellent foundation for advanced studies in computational neuroscience.
Emphasizes theoretical models over practical application.
"The course is very theoretical, which is great for understanding the 'why' behind neural computation."
"I was hoping for more hands-on coding or practical examples, but it's largely conceptual."
"If you're seeking to implement models, you'll need to supplement with other resources, as this is purely theoretical."
The recommended textbook is a vital learning resource.
"I couldn't have followed the course without constantly referring to the recommended chapters in the book."
"The 'Neuronal Dynamics' textbook is absolutely essential and complements the lectures perfectly."
"Lectures make more sense when you read the relevant sections of the textbook beforehand."
Highly valuable for advanced studies and research.
"This course provided an excellent foundation for my graduate studies in computational neuroscience."
"As a researcher, I found the depth and rigor directly applicable to my work."
"It's a top-tier academic course that truly prepares you for advanced topics in the field."
Deep dive into complex mathematical models.
"I found the mathematical treatment of neuronal dynamics to be incredibly thorough and precise."
"This course truly excels in explaining the underlying math for cognitive processes like memory and decision-making."
"The detailed use of mean-field theory and differential equations was exactly what I was looking for."
Significant time commitment required.
"The stated self-learning time is an underestimate; I spent much more time grappling with the concepts."
"Expect to dedicate significant hours each week to truly grasp the material and complete the assignments."
"It's a challenging course, and managing the workload alongside other commitments can be tough."
Requires strong background in math and physics.
"Be prepared with a solid foundation in calculus, linear algebra, and differential equations, or you'll struggle."
"I wish the prerequisites were emphasized more; without a strong physics background, I found parts very difficult."
"This course is not for beginners; it assumes significant prior knowledge in quantitative methods."
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: Neuronal Dynamics of Cognition with these
activities:
Organize course notes, assignments, quizzes, and exams
Show steps
Improve study efficiency and retention by having organized materials.
Show steps
-
Gather all course materials
-
Organize materials into categories
-
Create a study schedule
Attend weekly study groups with classmates
Show steps
Enhance understanding and retention through peer discussions.
Show steps
-
Organize weekly study sessions
-
Discuss course material and concepts
Read: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Show steps
Provide a foundation for the mathematical and computational models used in theoretical neuroscience.
View
Theoretical Neuroscience: Computational And...
on Amazon
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-
Read Chapters 1-3
-
Complete end of chapter exercises
Four other activities
Expand to see all activities and additional details
Show all seven activities
Work through practice problems on mean-field theory
Show steps
Strengthen understanding of mean-field theory, a core concept in the course.
Show steps
-
Identify practice problems in course materials
-
Solve 10 practice problems
-
Review solutions
Develop a visual representation of cortical field models
Show steps
Enhance comprehension of cortical field models by creating a visual aid.
Show steps
-
Review course materials on cortical field models
-
Sketch a visual representation
-
Refine the visual representation
Simulate a simple model of memory formation in the brain
Show steps
Apply course concepts by creating a computational model of memory formation.
Browse courses on
Memory Formation
Show steps
-
Identify the key mechanisms of memory formation
-
Design a computational model
-
Implement the model in a programming language
-
Run simulations and analyze results
Explore online tutorials on synaptic plasticity and learning
Show steps
Expand knowledge of synaptic plasticity and learning beyond course materials.
Browse courses on
Synaptic Plasticity
Show steps
-
Identify relevant online tutorials
-
Watch or read the tutorials
-
Summarize the key points
Organize course notes, assignments, quizzes, and exams
Show steps
Improve study efficiency and retention by having organized materials.
Show steps
Show steps
Show steps
- Gather all course materials
- Organize materials into categories
- Create a study schedule
Attend weekly study groups with classmates
Show steps
Enhance understanding and retention through peer discussions.
Show steps
Show steps
Show steps
- Organize weekly study sessions
- Discuss course material and concepts
Read: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Show steps
Provide a foundation for the mathematical and computational models used in theoretical neuroscience.
View
Theoretical Neuroscience: Computational And...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3
- Complete end of chapter exercises
Four other activities
Expand to see all activities and additional details
Show all seven activities
Work through practice problems on mean-field theory
Show steps
Strengthen understanding of mean-field theory, a core concept in the course.
Show steps
Show steps
Show steps
- Identify practice problems in course materials
- Solve 10 practice problems
- Review solutions
Develop a visual representation of cortical field models
Show steps
Enhance comprehension of cortical field models by creating a visual aid.
Show steps
Show steps
Show steps
- Review course materials on cortical field models
- Sketch a visual representation
- Refine the visual representation
Simulate a simple model of memory formation in the brain
Show steps
Apply course concepts by creating a computational model of memory formation.
Browse courses on
Memory Formation
Show steps
Show steps
Show steps
- Identify the key mechanisms of memory formation
- Design a computational model
- Implement the model in a programming language
- Run simulations and analyze results
Explore online tutorials on synaptic plasticity and learning
Show steps
Expand knowledge of synaptic plasticity and learning beyond course materials.
Browse courses on
Synaptic Plasticity
Show steps
Show steps
Show steps
- Identify relevant online tutorials
- Watch or read the tutorials
- Summarize the key points
Career center
Learners who complete Computational Neuroscience: Neuronal Dynamics of Cognition will develop knowledge and skills
that may be useful to these careers:
Neuroscientist
Neuroscientist
Neuroscientists study the nervous system, including the brain, spinal cord, and nerves. This course provides a comprehensive overview of the field of neuroscience, covering topics such as neuronal dynamics, memory formation, decision processes, and perception. By taking this course, you will gain a strong foundation in the principles of neuroscience and develop the skills necessary to succeed in this field.
Computational Neuroscientist
Computational Neuroscientist
Computational neuroscientists develop mathematical and computational models to simulate and understand the function of the brain. This course provides a comprehensive overview of the field, covering topics such as mean-field theory, non-linear differential equations, and cortical field models. By taking this course, you will gain the theoretical and practical skills necessary to succeed as a computational neuroscientist.
Research Scientist
Research Scientist
Research scientists conduct research in various fields, including neuroscience. This course provides a comprehensive overview of the field of neuroscience, covering topics such as neuronal dynamics, memory formation, decision processes, and perception. By taking this course, you will gain the knowledge and skills necessary to conduct research in neuroscience.
Mathematician
Mathematician
Mathematicians develop and use mathematical theories and techniques to solve problems in various fields, including neuroscience. This course provides a comprehensive overview of the mathematical principles used in neuroscience, covering topics such as mean-field theory, non-linear differential equations, and cortical field models. By taking this course, you will develop the mathematical skills necessary to succeed as a mathematician specializing in neuroscience.
Neurotechnology Engineer
Neurotechnology Engineer
Neurotechnology engineers design, develop, and test technologies that interface with the nervous system. This course provides a comprehensive overview of the mathematical and computational principles used in neurotechnology, with a focus on topics such as neuronal dynamics, brain-computer interfaces, and neural prosthetics. By taking this course, you will gain the skills necessary to become a successful neurotechnology engineer.
Professor
Professor
Professors teach and conduct research at universities and colleges. This course provides a comprehensive overview of the field of neuroscience, covering topics such as neuronal dynamics, memory formation, decision processes, and perception. By taking this course, you will gain the knowledge and skills necessary to teach and conduct research in neuroscience.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design, develop, and deploy machine learning models to solve complex problems. This course provides a solid foundation in the mathematical and computational principles of machine learning, with a focus on applications in neuroscience. By taking this course, you will gain the skills necessary to become a successful machine learning engineer in the field of neuroscience.
Software Engineer
Software Engineer
Software engineers design, develop, and test software systems. This course provides a strong foundation in the mathematical and computational principles used in software engineering, with a focus on applications in neuroscience. By taking this course, you will develop the skills necessary to become a successful software engineer in the field of neuroscience.
Systems Engineer
Systems Engineer
Systems engineers design, develop, and test complex systems, including those in the field of neuroscience. This course provides a comprehensive overview of the mathematical and computational principles used in systems engineering, with a focus on applications in neuroscience. By taking this course, you will gain the skills necessary to become a successful systems engineer in the field of neuroscience.
Data Scientist
Data Scientist
Data scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course provides a strong foundation in the mathematical and computational techniques used in data science, with a focus on applications in neuroscience. By taking this course, you will develop the skills necessary to excel as a data scientist in the field of neuroscience.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course provides a strong foundation in the mathematical and computational techniques used in statistics, with a focus on applications in neuroscience. By taking this course, you will develop the skills necessary to become a successful statistician in the field of neuroscience.
Pharmaceutical Researcher
Pharmaceutical Researcher
Pharmaceutical researchers discover, develop, and test new drugs and treatments for diseases. This course provides a strong foundation in the mathematical and computational techniques used in pharmaceutical research, with a focus on applications in neuroscience. By taking this course, you will gain the skills necessary to contribute to the development of new treatments for neurological disorders.
Biostatistician
Biostatistician
Biostatisticians are problem-solvers who use mathematical analyses to address research questions in the medical and healthcare fields. Their work is increasingly influenced by data science and computational methods, particularly in the field of neuroscience. This course helps build a foundation for biostatisticians who want to specialize in neuroscience-related research. An understanding of neuronal dynamics is crucial for developing and interpreting statistical models that accurately represent the complexities of the brain.
Physicist
Physicist
Physicists study the fundamental laws of nature and the behavior of matter and energy. This course provides a comprehensive overview of the mathematical and computational principles used in physics, with a focus on applications in neuroscience. By taking this course, you will develop the skills necessary to succeed as a physicist specializing in neuroscience.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course provides a strong foundation in the mathematical and computational techniques used in quantitative finance, with a focus on applications in neuroscience. By taking this course, you will develop the skills necessary to become a successful quantitative analyst in the field of neuroscience.
For more career information including salaries, visit:
OpenCourser.com/course/9yr3i7/computational
Reading list
We've selected six 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: Neuronal Dynamics of Cognition.
Provides a comprehensive overview of the principles of neural computation, including an introduction to the basic concepts of neurons and neural networks, as well as more advanced topics such as learning, memory, and perception.
Save
Provides a comprehensive overview of the theory and practice of computational neuroscience, with a particular focus on neuronal dynamics.
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Provides a comprehensive overview of the theory and practice of neural networks and machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of the theory and practice of theoretical neuroscience, with a particular focus on computational and mathematical modeling of neural systems.
Save
Provides a thorough introduction to computational modeling in neuroscience, covering a wide range of topics from single-neuron models to network models. It would be a valuable resource for students looking to gain a deeper understanding of the mathematical and computational foundations of the field.
Provides a mathematical introduction to computational neuroscience, including topics such as neural networks, neural dynamics, and neural coding.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9yr3i7/computational
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Effort
4 to 6 hours per week for 6 weeks
Level
Advanced
Via
edX
Institution
École Polytechnique Fédérale de Lausanne
Instructor
Wulfram Gerstner
Language
English
Transcript
English
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