About this course: 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.
Who is this class for: 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.
Taught by: Rajesh P. N. Rao, Professor
Computer Science & EngineeringTaught by: Adrienne Fairhall, Associate Professor
Physiology and Biophysics
| Level | Beginner |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Reading: Welcome Message & Course Logistics
- Reading: About the Course Staff
- Reading: Syllabus and Schedule
- Reading: Matlab & Octave Information and Tutorials
- Reading: Python Information and Tutorials
- Reading: Week 1 Lecture Notes
- Video: 1.2 Computational Neuroscience: Descriptive Models
- Video: 1.3 Computational Neuroscience: Mechanistic and Interpretive Models
- Video: 1.4 The Electrical Personality of Neurons
- Video: 1.5 Making Connections: Synapses
- Video: 1.6 Time to Network: Brain Areas and their Function
- Practice Quiz: Matlab/Octave Programming
- Practice Quiz: Python Programming
- Reading: Welcome Message
- Reading: Week 2 Lecture Notes and Tutorials
- Video: 2.2 Neural Encoding: Simple Models
- Video: 2.3 Neural Encoding: Feature Selection
- Video: 2.4 Neural Encoding: Variability
- Video: Vectors and Functions (by Rich Pang)
- Video: Convolutions and Linear Systems (by Rich Pang)
- Video: Change of Basis and PCA (by Rich Pang)
- Video: Welcome to the Eigenworld! (by Rich Pang)
- Reading: IMPORTANT: Quiz Instructions
- Reading: Welcome Message
- Reading: Week 3 Lecture Notes and Supplementary Material
- Video: 3.2 Population Coding and Bayesian Estimation
- Video: 3.3 Reading Minds: Stimulus Reconstruction
- Video: Fred Rieke on Visual Processing in the Retina
- Video: Gaussians in One Dimension (by Rich Pang)
- Video: Probability distributions in 2D and Bayes' Rule (by Rich Pang)
- Reading: Welcome Message
- Reading: Week 4 Lecture Notes and Supplementary Material
- Video: 4.2 Calculating Information in Spike Trains
- Video: 4.3 Coding Principles
- Video: What's up with entropy? (by Rich Pang)
- Video: Information theory? That's crazy! (by Rich Pang)
- Reading: Welcome Message
- Reading: Week 5 Lecture Notes and Supplementary Material
- Video: 5.2 Spikes
- Video: 5.3 Simplified Model Neurons
- Video: 5.4 A Forest of Dendrites
- Video: Eric Shea-Brown on Neural Correlations and Synchrony
- Video: Dynamical Systems Theory Intro Part 1: Fixed points (by Rich Pang)
- Video: Dynamical Systems Theory Intro Part 2: Nullclines (by Rich Pang)
- Reading: Welcome Message
- Reading: Week 6 Lecture Notes and Tutorials
- Video: 6.2 Introduction to Network Models
- Video: 6.3 The Fascinating World of Recurrent Networks
- Reading: Welcome Message
- Reading: Week 7 Lecture Notes and Tutorials
- Video: 7.2 Introduction to Unsupervised Learning
- Video: 7.3 Sparse Coding and Predictive Coding
- Video: Gradient Ascent and Descent (by Rich Pang)
- Reading: Welcome Message and Concluding Remarks
- Reading: Week 8 Lecture Notes and Supplementary Material
- Video: 8.2 Reinforcement Learning: Predicting Rewards
- Video: 8.3 Reinforcement Learning: Time for Action!
- Video: Eb Fetz on Bidirectional Brain-Computer Interfaces
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This is a wonderful start for a biologist , to get idea of concepts of learning . An advanced course focused more on brain circuitry is suggested.
Thanks a lot
The course over all was very good but I didnt given it five because of the following : in course 2-5 the lectures were not coherent and the there was no expalantion for how certain experiments or measurments were done and hence natural progression to associate the mathematics. The lecturer tends to speak fast and sometimes eat her words so there was absence of clarity . The lectures were not well structured . on the otherhand lectures 6-8 were much clearer in presenation and scope and more linked with the quizes.
I have really liked this course,but there is a lot of statistics I didn't expect to find at the beginning. Ihave given me exactly the flavor of what Computational Neuroscience is and what are the field of applications, which are REALLY interesting. Honestly I have found a bit too condensed the part regarding the description of "cause" and all the related statistic stuff which I think should deserve some 1 or 2 videos with solved problems. All summed up, I think this course is really worth of taking. Best regards to the professors and to the mentors and to those who have given me a lot of help with their posting on the forum. Their doubts and the relative answers have really been enlightening for driving me towards a better understanding of the matter. Thank you to all of you.
fk
very effective course for Researchers