User Tools

Site Tools


analysis:nsb2016

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
analysis:nsb2016 [2016/07/03 22:43]
mvdm
analysis:nsb2016 [2018/07/07 10:19] (current)
Line 1: Line 1:
 ~~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 ​(Week 4)]] +   * [[analysis:​nsb2016:​week4|Module 5: Anatomy of time series data, sampling theory]] 
-   * [[analysis:​nsb2016:​week5|Module 6: Fourier series, transforms, power spectra ​(Week 4)]] +   * [[analysis:​nsb2016:​week5|Module 6: Fourier series, transforms, power spectra]] 
-   * [[analysis:​nsb2016:​week6|Module 7: Filtering: filter design, use, caveats ​(Week 5)]] +   * [[analysis:​nsb2016:​week6|Module 7: Filtering: filter design, use, caveats]] 
-   * [[analysis:​nsb2016:​week7|Module 8: Time-frequency analysis: spectrograms ​(Week 5)]]+   * [[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 ​(Week 6)]] +   * [[analysis:​nsb2016:​week9|Module 9: Spike train analysis: firing rate, interspike interval distributions,​ auto- and crosscorrelations]] 
-   * [[analysis:​nsb2016:​week10|Module 10: Spike train analysis II: tuning curves, encoding, decoding ​(Week 7)]]+   * [[analysis:​nsb2016:​week10|Module 10: Spike train analysis II: tuning curves, encoding, decoding]]
  
  
-== Intermediate topics ==+== Intermediate topics: do as needed ​==
  
-   * [[analysis:​nsb2016:​week11|Module 11: Interactions between multiple signals: coherence, Granger causality, and phase-slope index (Week 8)]] +   * [[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 ​(Week 9)]]  +   * [[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 ​(Week 10)]] +   * [[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 ===
  
Line 73: Line 73:
   * Textbook: {{:​analysis:​dayanabbott_theoneuro.pdf|Dayan & Abbott, Theoretical Neuroscience}}   * Textbook: {{:​analysis:​dayanabbott_theoneuro.pdf|Dayan & Abbott, Theoretical Neuroscience}}
  
-=== What this material ​provides ===+=== What this tutorial ​provides ===
  
-Overall, this material ​is designed to provide hands-on experience with management, visualization,​ and analysis of neural data. Becoming skilled at these things is a rate-limiting step for many graduate projects requiring analysis. Even if your work only requires rudimentary analysis, awareness of what else can be done and how to do it well is valuable, for instance when evaluating the work of others in the literature!+Overall, this tutorial ​is designed to provide hands-on experience with management, visualization,​ and analysis of neural data. Becoming skilled at these things is a rate-limiting step for many graduate projects requiring analysis. Even if your work only requires rudimentary analysis, awareness of what else can be done and how to do it well is valuable, for instance when evaluating the work of others in the literature!
  
 To do so, the focus is on introducing some commonly used tools, such as %%GitHub%% and relevant functionality within MATLAB -- and then to actually use these on real data sets you collect yourself. To do so, the focus is on introducing some commonly used tools, such as %%GitHub%% and relevant functionality within MATLAB -- and then to actually use these on real data sets you collect yourself.
Line 81: Line 81:
 We will make contact with a few concepts from computer science, signal processing, and statistics. However, the focus is on making initial steps that work and getting pointers to more complete treatment, rather than a thorough theoretical grounding. Nevertheless,​ to make sure that what you learn is not tied to specific data sets only, a number of [[analysis:​nsb2015:​week0|principles]] of data analysis -- applicable to any project of sufficient complexity -- will be referenced throughout the material. You are invited to think of these and others, not only as you progress through the modules, but especially as you organize your own data analyses! We will make contact with a few concepts from computer science, signal processing, and statistics. However, the focus is on making initial steps that work and getting pointers to more complete treatment, rather than a thorough theoretical grounding. Nevertheless,​ to make sure that what you learn is not tied to specific data sets only, a number of [[analysis:​nsb2015:​week0|principles]] of data analysis -- applicable to any project of sufficient complexity -- will be referenced throughout the material. You are invited to think of these and others, not only as you progress through the modules, but especially as you organize your own data analyses!
  
-=== What this material ​is not ===+=== What this tutorial ​is not ===
  
 This material will provide a brief introduction to a number of concepts which are themselves the subject of multiple courses and voluminous textbooks. These include signal processing topics such as Fourier analysis and filter design, computer science concepts such as object-oriented programming and binary data formats, and a number of statistical ideas and tools. Be aware that if any of these are particularly important to your research, you should consider taking more in-depth coursework and/or working through relevant textbooks on your own: this short tutorial cannot replace such courses! This material will provide a brief introduction to a number of concepts which are themselves the subject of multiple courses and voluminous textbooks. These include signal processing topics such as Fourier analysis and filter design, computer science concepts such as object-oriented programming and binary data formats, and a number of statistical ideas and tools. Be aware that if any of these are particularly important to your research, you should consider taking more in-depth coursework and/or working through relevant textbooks on your own: this short tutorial cannot replace such courses!
analysis/nsb2016.1467600212.txt.gz · Last modified: 2018/07/07 10:19 (external edit)