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Introduction to Computational Neuroscience
- Instructor
- Prof. Sebastian Seung
- Term
- Spring 2004
- Level
- Undergraduate
- What's inside
- 1 lecture note · 1 reading · 22 other files · 3 web pages
- Materials open
- MIT OCW
About this course
Source: MIT OpenCourseWare 9.29J Introduction to Computational Neuroscience, Spring 2004, Prof. Sebastian Seung — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.
Materials are grouped the way MIT organized them. A verified, course-wide order has not been established here.
Before you start
Prerequisites are not listed here. Check MIT's own syllabus for this course.
How this course was organized
What's here, and what isn't
- A lecture sequence of 26 items is confirmed. Assignment and exam materials exist too, but MIT's own order for them could not be verified, so they're shown as materials below rather than a sequence.
Not included here
4 items from the source could not be safely included:
- MIT's page format for this item wasn't recognized clearly enough to place it safely. (3 items)
- Referenced in a table, but without MIT's own verified order, so it isn't used to claim a sequence.
Materials by section
Grouped by section, as MIT organized them. Not sequenced or ordered.
Readings
- Seung, H. S. “Matching and maximizing are two ends of a spectrum of policy search algorithms.” Manuscript (January 2, 2004.) Download
- This resource contains information on formulas for calculus, sources of numerical error, ordinary… (whnotes.pdf) Download
- Professor Seung’s notes on the Hopfield Model Download
- More of Professor Seung’s notes on Associative Memory Download
Lecture Notes
- Introduction Examples of Neural Coding, Simple Linear Regression Download
- Convolution and Correlation 1 Firing Rate Download
- Optional Lecture 1 Initializing and Using Vectors and Matrices in MATLAB®, Matrix Shortcuts, Plots in MATLAB®, Useful… (Courtesy of Justin Werfel. Used with permission.) Download
- Convolution and Correlation 2 Spike-triggered Average Wiener-Hopf Equations and White Noise Analysis Download
- Operant Matching 1 Download
Assignments
13 files
- If you have a slow data link, you might want to download the abridged version fish10000.mat Download
- Problem Set 1: Linear Regression, Convolution, and Correlation Download
- Problem Set 2: More Convolution and Correlation Download
- Problem Set 3: Visual Receptive Fields and Least Squares (Courtesy of Prof. Clay Reid of Harvard Medical School. Used with permission.) Download
- Problem Set 4: Operant Matching and Games Download
- Problem Set 5: Passive Models of Neurons Download
- Problem Set 6: Extensions of the Hodgkin-Huxley Model Download
- Problem Set 7: Simulation of Synaptic Inputs Download
- Problem Set 8: Models of Associative Memory Download
- To do the homework, you will need to download 4.7 MB of data fish.mat (Courtesy of Professor Fabrizio Gabbiani of Baylor College of Medicine. Used with permission.) Download
- Data for #5 ganglion.mat (Courtesy of Prof. Clay Reid of Harvard Medical School. Used with permission.) Download
- Code cc.m Download
- If you have trouble loading the file on windows (“file may be corrupt”), then try downloading the zipped version fish.zip Download
Course outline
Calendar · Lecture Notes
Lecture 2: Convolution and Correlation 1 Firing Rate
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Lecture 3: Convolution and Correlation 2 Spike-triggered Average Wiener-Hopf Equations and White Noise Analysis
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Lecture 4: Visual Receptive Fields 1 Basics of the Visual System, Center-surround Receptive Fields, Simple and Complex Cortical Cells
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Lecture 5: Visual Receptive Fields 2
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Lecture 6: Operant Matching 1
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Lecture 7: Operant Matching 2
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Lecture 8: Games 1
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Lecture 9: Games 2
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Lecture 10: Project Meeting 1 Discussion of Topics, Choice of Projects, Work Begins
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Lecture 11: Project Meeting 2
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Lecture 12: Project Meeting 3
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Lecture 13: Project Meeting 4
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Lecture 14: Project Presentations 1
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Lecture 15: Project Presentations 2
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Lecture 16: Ion Channels, Nernst Equation, Passive Electrical Properties of Neurons
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Lecture 17: The Action Potential, Hodgkin-Huxley Model 1
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Lecture 18: Hodgkin-Huxley Model 2
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Lecture 19: A-type Potassium Channels, Calcium-Dependent Potassium Channels
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Lecture 20: Synapses
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Lecture 21: Associative Memory 1
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Lecture 22: Associative Memory 2
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Lecture 23: Decisionmaking
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Lecture 24: Projects
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Lecture 25: Projects (cont.)
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Lecture 26: Review
MIT OpenCourseWare, Prof. Sebastian Seung, 9.29J Introduction to Computational Neuroscience, Spring 2004.
License: CC BY-NC-SA 4.0 · Edition: Spring 2004 · Open the original course on OCW ↗
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