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analysis:nsb2014 [2014/07/21 23:40]
mvdm [Contents]
analysis:nsb2014 [2018/07/07 10:19] (current)
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    * [[analysis:​nsb2014:​week0|Introduction:​ Principles of careful data analysis]]    * [[analysis:​nsb2014:​week0|Introduction:​ Principles of careful data analysis]]
-   * [[analysis:​nsb2014:​week1|Module 1: Good habits and tools (paths, backups, versioning, annotation)]]+   * [[analysis:​nsb2014:​week1|Module 1: Good data management ​habits and tools (paths, backups, versioning, annotation)]]
    * [[analysis:​nsb2014:​week2|Module 2: Introduction to Neuralynx data formats and preprocessing]]    * [[analysis:​nsb2014:​week2|Module 2: Introduction to Neuralynx data formats and preprocessing]]
    * [[analysis:​nsb2014:​week3|Module 3: Anatomy of neural data: time series, sampling, aliasing]]    * [[analysis:​nsb2014:​week3|Module 3: Anatomy of neural data: time series, sampling, aliasing]]
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 FIXME Modules still to be adapted for NS&B by %%MvdM%% (can look as a preview, but will not work): FIXME Modules still to be adapted for NS&B by %%MvdM%% (can look as a preview, but will not work):
  
-   - [[analysis:​course:​week5|Filtering:​ filter design, use, caveats]] 
-   - [[analysis:​course:​week6|Time-frequency analysis: spectrograms]] 
-   - [[analysis:​course:​week7|Time-frequency analysis II: cross-frequency coupling]] 
    - [[analysis:​course:​week8|Interactions between multiple signals: coherence and other connectivity measures]]    - [[analysis:​course:​week8|Interactions between multiple signals: coherence and other connectivity measures]]
-   - [[analysis:​course:​week9|Spike train analysis: firing rate, interspike interval distributions,​ auto- and crosscorrelations]] 
-   - [[analysis:​course:​week10|Spike train analysis II: tuning curves, encoding, decoding]] 
    - [[analysis:​course:​week11|Spike-field relationships:​ spike-triggered average, phase locking, phase precession]]    - [[analysis:​course:​week11|Spike-field relationships:​ spike-triggered average, phase locking, phase precession]]
    - [[analysis:​course:​week12|Basic hypothesis testing: parametric, nonparametric,​ bootstrapping]]    - [[analysis:​course:​week12|Basic hypothesis testing: parametric, nonparametric,​ bootstrapping]]
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 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]]. ​
  
-If you can solve these exercises ​you are ready for this course.+If you are unsure, take a look at the table of contents of the Primer. If there are things you don't recognize, use the Primer itself, or Chapter 2 of the MATLAB ​for Neuroscientists book to get up to speed.
  
 === Resources === === Resources ===
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 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 ===
analysis/nsb2014.1406000414.txt.gz · Last modified: 2018/07/07 10:19 (external edit)