analysis:course-w16
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| ~~DISCUSSION~~ | ~~DISCUSSION~~ | ||
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| + | Welcome! This is the home page for the Winter 2016 edition of the "Neural Data Analysis" course. | ||
| === Contents === | === Contents === | ||
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| == Fundamentals == | == Fundamentals == | ||
| - | * [[analysis:course-w16:week1|Module 1: Setting up (MATLAB, paths, GitHub, accessing data)]] | + | * [[analysis:course-w16:week1|Module 1: Setting up (MATLAB, paths, GitHub, accessing data; Week 1)]] |
| - | * [[analysis:course-w16:week2|Module 2: Introduction to neural data formats and preprocessing]] | + | * [[analysis:course-w16:week2|Module 2: Introduction to neural data formats and preprocessing (Week 2)]] |
| - | * Module 3: Visualizing raw neural data in MATLAB ([[analysis:course-w16:week3long|long version]] to learn the guts, [[analysis:course-w16:week3short|short version]] to just get stuff done) | + | * [[analysis:course-w16:week3long|Module 3: Visualizing raw neural data in MATLAB (Week 3)]] |
| - | == Spike data basics == | + | == Time series data data basics == |
| - | * [[analysis:course-w16:week4|Module 4: Spike sorting]] (we will probably skip this one, but you are welcome to go through it yourself.) | + | * [[analysis:course-w16:week4|Module 4: Anatomy of time series data, sampling theory (Week 4)]] |
| - | * [[analysis:course-w16:week11|Module 5: Spike train analysis: firing rate, interspike interval distributions, auto- and crosscorrelations]] | + | * [[analysis:course-w16:week5|Module 5: Fourier series, transforms, power spectra (Week 4)]] |
| - | * [[analysis:course-w16:week12|Module 6: Spike train analysis II: tuning curves, encoding, decoding]] | + | * [[analysis:course-w16:week6|Module 6: Filtering: filter design, use, caveats (Week 5)]] |
| + | * [[analysis:course-w16:week7|Module 7: Time-frequency analysis: spectrograms (Week 5)]] | ||
| - | == Local field potential data basics == | + | == Spike data basics == |
| + | |||
| + | * [[analysis:course-w16:week8|Module 8: Spike sorting]] (we will probably skip this one, but you are welcome to go through it yourself.) | ||
| + | * [[analysis:course-w16:week9|Module 9: Spike train analysis: firing rate, interspike interval distributions, auto- and crosscorrelations (Week 6)]] | ||
| + | * [[analysis:course-w16:week10|Module 10: Spike train analysis II: tuning curves, encoding, decoding (Week 7)]] | ||
| - | * [[analysis:course-w16:week6|Module 7: Fourier series, transforms, power spectra]] | ||
| - | * [[analysis:course-w16:week7|Module 8: Filtering: filter design, use, caveats]] | ||
| - | * [[analysis:course-w16:week8|Module 9: Time-frequency analysis: spectrograms]] | ||
| == Intermediate topics == | == Intermediate topics == | ||
| - | * [[analysis:course-w16:week9|Module 10: Time-frequency analysis II: cross-frequency coupling]] | + | * [[analysis:course-w16:week11|Module 11: Interactions between multiple signals: coherence, Granger causality, and phase-slope index (Week 8)]] |
| - | * [[analysis:course-w16:week10|Module 11: Interactions between multiple signals: coherence and other connectivity measures]] | + | * [[analysis:course-w16:week12|Module 12: Time-frequency analysis II: cross-frequency coupling (Week 9)]] |
| - | * [[analysis:course-w16:week13|Module 12: Spike-field relationships: spike-triggered average, phase locking, phase precession]] | + | * [[analysis:course-w16:week13|Module 13: Spike-field relationships: spike-triggered average, phase locking, phase precession (Week 10)]] |
| - | * [[analysis:course-w16:classify|Module 13: Classification of ensemble spiking patterns]] (we will probably skip this one, but you are welcome to go through it yourself.) | + | * [[analysis:course-w16:week14|Module 14: Classification of ensemble spiking patterns]] (likely skip) |
| == Advanced topics == | == Advanced topics == | ||
| - | * [[analysis:course-w16:module14|Module 14: Two-step Bayesian decoding with dynamic spatial priors]] | + | * [[analysis:course-w16:week15|Module 15: Two-step Bayesian decoding with dynamic spatial priors]] (likely skip) |
| - | * [[analysis:course-w16:module15|Module 15: Co-activation and detection of neural ensembles]] | + | * [[analysis:course-w16:week16|Module 16: Pairwise co-occurrence]] (likely skip) |
| == Other topics == | == Other topics == | ||
| - | * Git: conflict resolution, undo's, writing good commit messages, issue tracking, branching | + | * Git: conflict resolution, undo's, writing good commit messages, issue tracking, branching (on request) |
| - | * Top-level analysis workflows for handling multiple subjects and sessions | + | * Top-level analysis workflows for handling multiple subjects and sessions (on request) |
| - | * Exporting MATLAB data to R | + | * Exporting MATLAB data to R (on request) |
| + | * MATLAB tools: GUI design tool, debugger, profiler (on request) | ||
| === Prerequisites === | === Prerequisites === | ||
| Basic familiarity with MATLAB. Depending on your background and programming experience you might find the following resources helpful: | Basic familiarity with MATLAB. Depending on your background and programming experience you might find the following resources helpful: | ||
| - | * Textbook: {{:analysis:wallisch_matlabforneuro.pdf|Wallisch, MATLAB for Neuroscientists}} | + | * Textbook: Wallisch, MATLAB for Neuroscientists |
| - | * [[http://www.mathworks.com/academia/student_center/tutorials/launchpad.html|"Getting Started with MATLAB" Primer]]. | + | * [[http://www.mathworks.com/help/matlab/getting-started-with-matlab.html?s_cid=learn_doc|"Getting Started with MATLAB" Primer]]. |
| * [[http://www.mathworks.com/matlabcentral/about/cody/ | Cody]], a continually expanding set of problems with solutions to work through, with a points system to track your progress | * [[http://www.mathworks.com/matlabcentral/about/cody/ | Cody]], a continually expanding set of problems with solutions to work through, with a points system to track your progress | ||
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| * [[http://stackoverflow.com/questions/tagged/matlab | MATLAB questions on StackOverflow]], a Q&A site where you can browse previous questions and add new ones | * [[http://stackoverflow.com/questions/tagged/matlab | MATLAB questions on StackOverflow]], a Q&A site where you can browse previous questions and add new ones | ||
| + | If you have no formal training in computer programming (i.e. you have never taken a "Intro to Computer Science" or "Introductory Programming" type course) you will almost certainly find what follows in this course less frustrating if you do the pen-and-paper exercises in this [[http://sites.tufts.edu/rodrego/files/2011/03/Secrets-of-Computer-Power-Revealed-2008.pdf | short chapter]] by Daniel Dennett ("The Secrets of Computer Power Revealed") before you embark on the MATLAB primer linked to above. | ||
| === Resources === | === Resources === | ||
| This course is "standalone", but the following textbooks provide more in-depth treatment of some of the topics. | This course is "standalone", but the following textbooks provide more in-depth treatment of some of the topics. | ||
| - | * Textbook: {{:analysis:leis_dspusingmatlab.pdf|Leis, Digital Signal Processing using MATLAB for Students and Researchers}} | + | * Textbook: Leis, Digital Signal Processing using MATLAB for Students and Researchers |
| - | * Textbook: {{:analysis:johnstonwu.pdf|Johnston and Wu, Foundations of Cellular Neurophysiology}} | + | * Textbook: Johnston and Wu, Foundations of Cellular Neurophysiology |
| - | * Textbook: {{:analysis:dayanabbott_theoneuro.pdf|Dayan & Abbott, Theoretical Neuroscience}} | + | * Textbook: Dayan & Abbott, Theoretical Neuroscience |
| === What this course is === | === What this course is === | ||
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| === Evaluation === | === Evaluation === | ||
| - | Most modules finish with a challenge (or several), in which you are invited to implement some of the ideas in the module yourself. Pick one such challenge from the first half of modules (1-7) and another from the second half (8-14). Submit your code for two chosen challenges to a %%GitHub%% repository you created, along with documentation: instructions on what it is supposed to do, how to make it run if applicable, and comments explaining how the code works. | + | Most modules finish with a challenge (or several), in which you are invited to implement some of the ideas in the module yourself. Pick one such challenge from the first half of modules (1-7) and another from the second half (9-16). Submit your code for two chosen challenges to a %%GitHub%% repository you created, along with documentation: instructions on what it is supposed to do, how to make it run if applicable, and comments explaining how the code works. |
| === Note for Linux users === | === Note for Linux users === | ||
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| === Acknowledgments === | === Acknowledgments === | ||
| - | The architecture of the code used in this course was inspired by a similar set of code by my post-doctoral mentor, [[http://redishlab.neuroscience.umn.edu/ | A. David Redish]]; several of the data types and functions are re-implementations of Redish lab functions of the same name. Major contributions to the codebase were made by Alyssa Carey (a %%MSc%% student and research assistant in the lab) and Youki Tanaka (current PhD student), | + | The architecture of the code used in this course was inspired by a similar set of code by my post-doctoral mentor, [[http://redishlab.neuroscience.umn.edu/ | A. David Redish]]; several of the data types and functions are re-implementations of Redish lab functions of the same name. Major contributions to the codebase were made by Alyssa Carey (a %%MSc%% student and research assistant in the lab) and Youki Tanaka (current PhD student). |
analysis/course-w16.1452119345.txt.gz · Last modified: 2018/07/07 10:19 (external edit)
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