analysis:nsb2016
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analysis:nsb2016 [2016/07/03 23:08] mvdm |
analysis:nsb2016 [2018/07/07 10:19] (current) |
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| ~~DISCUSSION~~ | ~~DISCUSSION~~ | ||
| - | Welcome! This is the home page for the data management and analysis component of the NS&B 2016 hippocampus cycle. | + | Welcome! This is the home page for the data management and analysis tutorials for the NS&B 2016 hippocampus cycle. |
| === Contents === | === Contents === | ||
| - | == Reference == | + | == Reference: read this first, and then again later == |
| * [[analysis:nsb2015:week0|Principles of (neural) data analysis]] | * [[analysis:nsb2015:week0|Principles of (neural) data analysis]] | ||
| Line 13: | Line 13: | ||
| * [[analysis:nsb2016:week1|Module 1: Setting up (MATLAB, paths, GitHub, accessing data)]] | * [[analysis:nsb2016:week1|Module 1: Setting up (MATLAB, paths, GitHub, accessing data)]] | ||
| * [[analysis:nsb2016:week2|Module 2: Introduction to neural data formats and preprocessing]] | * [[analysis:nsb2016:week2|Module 2: Introduction to neural data formats and preprocessing]] | ||
| - | * [[analysis:nsb2016:week3long|Module 3: Visualizing raw neural data in MATLAB]] | + | * [[analysis:nsb2016:week3long|Module 3: Visualizing raw neural data in MATLAB]] ([[analysis:nsb2016:week3short|Short version]]) |
| - | * [[analysis:nsb2016:week3short|Short version of Module 3 to just get stuff done]] | + | |
| * [[analysis:nsb2016:week8|Module 4: Spike sorting]] | * [[analysis:nsb2016:week8|Module 4: Spike sorting]] | ||
| - | == Time series data data basics == | + | == Time series data data basics: do as needed == |
| * [[analysis:nsb2016:week4|Module 5: Anatomy of time series data, sampling theory]] | * [[analysis:nsb2016:week4|Module 5: Anatomy of time series data, sampling theory]] | ||
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| * [[analysis:nsb2016:week7|Module 8: Time-frequency analysis: spectrograms]] | * [[analysis:nsb2016:week7|Module 8: Time-frequency analysis: spectrograms]] | ||
| - | == Spike data basics == | + | == Spike data basics: do as needed == |
| * [[analysis:nsb2016:week9|Module 9: Spike train analysis: firing rate, interspike interval distributions, auto- and crosscorrelations]] | * [[analysis:nsb2016:week9|Module 9: Spike train analysis: firing rate, interspike interval distributions, auto- and crosscorrelations]] | ||
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| - | == Intermediate topics == | + | == Intermediate topics: do as needed == |
| * [[analysis:nsb2016:week11|Module 11: Interactions between multiple signals: coherence, Granger causality, and phase-slope index]] | * [[analysis:nsb2016:week11|Module 11: Interactions between multiple signals: coherence, Granger causality, and phase-slope index]] | ||
| * [[analysis:nsb2016:week12|Module 12: Time-frequency analysis II: cross-frequency coupling]] | * [[analysis:nsb2016:week12|Module 12: Time-frequency analysis II: cross-frequency coupling]] | ||
| * [[analysis:nsb2016:week13|Module 13: Spike-field relationships: spike-triggered average, phase locking, phase precession]] | * [[analysis:nsb2016:week13|Module 13: Spike-field relationships: spike-triggered average, phase locking, phase precession]] | ||
| - | * [[analysis:nsb2016:week14|Module 14: Classification of ensemble spiking patterns]] (likely skip) | + | * [[analysis:nsb2016:week14|Module 14: Classification of ensemble spiking patterns]] |
| - | == Advanced topics == | + | == Advanced topics: do as needed == |
| - | * [[analysis:nsb2016:week15|Module 15: Two-step Bayesian decoding with dynamic spatial priors]] (likely skip) | + | * [[analysis:nsb2016:week15|Module 15: Two-step Bayesian decoding with dynamic spatial priors]] |
| - | * [[analysis:nsb2016:week16|Module 16: Pairwise co-occurrence]] (likely skip) | + | * [[analysis:nsb2016:week16|Module 16: Pairwise co-occurrence (replay)]] |
| == Other topics == | == Other topics == | ||
| Line 65: | Line 64: | ||
| 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. | 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 === | ||
analysis/nsb2016.1467601715.txt.gz · Last modified: 2018/07/07 10:19 (external edit)
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