Course Summary
This course provides an introduction to the field of computational neuroscience, with a focus on the theoretical and mathematical foundations.Key Learning Points
- Gain a deep understanding of the principles of computational neuroscience
- Learn how to apply mathematical and computational tools to study neural systems
- Explore the latest research in the field
Related Topics for further study
Learning Outcomes
- Understand the mathematical and computational foundations of neuroscience
- Develop the ability to apply these tools to study neural systems
- Become familiar with current research in the field
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of calculus and linear algebra
- Familiarity with programming in Python
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Assignments
- Quizzes
Similar Courses
- Neuroscience and Behavior
- Brain and Cognitive Sciences
Related Education Paths
Notable People in This Field
- President and Chief Scientist of the Allen Institute for Brain Science
- Francis Crick Professor at the Salk Institute for Biological Studies
Related Books
Description
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.
Outline
- Introduction & Basic Neurobiology (Rajesh Rao)
- 1.1 Course Introduction
- 1.2 Computational Neuroscience: Descriptive Models
- 1.3 Computational Neuroscience: Mechanistic and Interpretive Models
- 1.4 The Electrical Personality of Neurons
- 1.5 Making Connections: Synapses
- 1.6 Time to Network: Brain Areas and their Function
- Welcome Message & Course Logistics
- About the Course Staff
- Syllabus and Schedule
- Matlab & Octave Information and Tutorials
- Python Information and Tutorials
- Week 1 Lecture Notes
- Matlab/Octave Programming
- Python Programming
- What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall)
- 2.1 What is the Neural Code?
- 2.2 Neural Encoding: Simple Models
- 2.3 Neural Encoding: Feature Selection
- 2.4 Neural Encoding: Variability
- Vectors and Functions (by Rich Pang)
- Convolutions and Linear Systems (by Rich Pang)
- Change of Basis and PCA (by Rich Pang)
- Welcome to the Eigenworld! (by Rich Pang)
- Welcome Message
- Week 2 Lecture Notes and Tutorials
- IMPORTANT: Quiz Instructions
- Spike Triggered Averages: A Glimpse Into Neural Encoding
- Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)
- 3.1 Neural Decoding and Signal Detection Theory
- 3.2 Population Coding and Bayesian Estimation
- 3.3 Reading Minds: Stimulus Reconstruction
- Fred Rieke on Visual Processing in the Retina
- Gaussians in One Dimension (by Rich Pang)
- Probability distributions in 2D and Bayes' Rule (by Rich Pang)
- Welcome Message
- Week 3 Lecture Notes and Supplementary Material
- Neural Decoding
- Information Theory & Neural Coding (Adrienne Fairhall)
- 4.1 Information and Entropy
- 4.2 Calculating Information in Spike Trains
- 4.3 Coding Principles
- What's up with entropy? (by Rich Pang)
- Information theory? That's crazy! (by Rich Pang)
- Welcome Message
- Week 4 Lecture Notes and Supplementary Material
- Information Theory & Neural Coding
- Computing in Carbon (Adrienne Fairhall)
- 5.1 Modeling Neurons
- 5.2 Spikes
- 5.3 Simplified Model Neurons
- 5.4 A Forest of Dendrites
- Eric Shea-Brown on Neural Correlations and Synchrony
- Dynamical Systems Theory Intro Part 1: Fixed points (by Rich Pang)
- Dynamical Systems Theory Intro Part 2: Nullclines (by Rich Pang)
- Welcome Message
- Week 5 Lecture Notes and Supplementary Material
- Computing in Carbon
- Computing with Networks (Rajesh Rao)
- 6.1 Modeling Connections Between Neurons
- 6.2 Introduction to Network Models
- 6.3 The Fascinating World of Recurrent Networks
- Welcome Message
- Week 6 Lecture Notes and Tutorials
- Computing with Networks
- Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao)
- 7.1 Synaptic Plasticity, Hebb's Rule, and Statistical Learning
- 7.2 Introduction to Unsupervised Learning
- 7.3 Sparse Coding and Predictive Coding
- Gradient Ascent and Descent (by Rich Pang)
- Welcome Message
- Week 7 Lecture Notes and Tutorials
- Networks that Learn
- Learning from Supervision and Rewards (Rajesh Rao)
- 8.1 Neurons as Classifiers and Supervised Learning
- 8.2 Reinforcement Learning: Predicting Rewards
- 8.3 Reinforcement Learning: Time for Action!
- Eb Fetz on Bidirectional Brain-Computer Interfaces
- Welcome Message and Concluding Remarks
- Week 8 Lecture Notes and Supplementary Material
- Learning from Supervision and Rewards
Summary of User Reviews
This course on computational neuroscience has received positive reviews from many users. The course teaches students how the brain processes information and how it can be modeled using mathematical and computational techniques. Many users have praised the course's comprehensive coverage of the subject matter.Key Aspect Users Liked About This Course
Comprehensive coverage of the subject matterPros from User Reviews
- Excellent introduction to computational neuroscience
- Great instructor who explains concepts clearly
- Engaging and interactive assignments
- Helpful community of learners
- Good balance between theory and practical applications
Cons from User Reviews
- Some lectures can be difficult to follow without prior knowledge
- The course can be challenging for beginners
- The amount of information can be overwhelming at times
- Some technical issues with the platform
- The course could benefit from more real-world examples
Rajesh P. N. Rao
4.6 Raiting
Computational Neuroscience: Neuronal Dynamics of Cognition
- 0.0
-
![]()
![]()
![]()
![]()
![]()
This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to analyze the collective dynamics of thousands of interacting neurons....
Quantitative Biology Workshop
- 0.0
-
![]()
![]()
![]()
![]()
![]()
A workshop-style introduction to tools used in biological research. Discover how to analyze data using computational methods. Do you have an interest in biology and quantitative tools?...
Neuronal Dynamics
- 0.0
-
![]()
![]()
![]()
![]()
![]()
The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail....
Computational Vision
- 0.0
-
![]()
![]()
![]()
![]()
![]()
In this course, we will expand on vision as a cognitive problem space and explore models that address various vision tasks....
