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analysis:course [2013/09/27 10:15]
mvdm [List of Topics]
analysis:course [2018/07/07 10:19] (current)
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    - [[analysis:​course:​week3|Anatomy of neural data: time series, sampling, aliasing]]    - [[analysis:​course:​week3|Anatomy of neural data: time series, sampling, aliasing]]
    - [[analysis:​course:​week4|Fourier series, transforms, power spectra]]    - [[analysis:​course:​week4|Fourier series, transforms, power spectra]]
-   - Filtering: filter design, use, caveats +   ​- ​[[analysis:​course:​week5|Filtering: filter design, use, caveats]] 
-   - Time-frequency analysis: spectrograms +   ​- ​[[analysis:​course:​week6|Time-frequency analysis: spectrograms]] 
-   - Time-frequency analysis II: autoregressive models, ​cross-frequency coupling +   ​- ​[[analysis:​course:​week7|Time-frequency analysis II: cross-frequency coupling]] 
-   ​- ​Coherence (if time permitspartial ​coherence, Grainger causality) +   ​- ​[[analysis:​course:​week8|Interactions between multiple signals: coherence ​and other connectivity measures]] 
-   - Spike train analysis: firing rate, interspike interval distributions, ​autocorrelation,​ spike spectrum +   ​- ​[[analysis:​course:​week9|Spike train analysis: firing rate, interspike interval distributions, ​auto- and crosscorrelations]] 
-   - Spike train analysis II: tuning curves, encoding, decoding +   ​- ​[[analysis:​course:​week10|Spike train analysis II: tuning curves, encoding, decoding]] 
-   - Spike-field relationships:​ phase locking, phase precession +   ​- ​[[analysis:​course:​week11|Spike-field relationships: ​spike-triggered average, ​phase locking, phase precession]] 
-   ​- ​Spike sorting +   ​- ​[[analysis:​course:​week12|Basic hypothesis testing: parametric, ​nonparametric, bootstrapping]] 
-   ​- ​Basic parametric ​hypothesis testing +   ​- ​[[analysis:​course:​week13|Basic model fitting: regression, general linear ​models]] 
-   - Bootstrapping and nonparametric ​tests +   ​- ​[[analysis:​course:​week14|Dimensionality reduction methods, classification]] 
-   ​- ​Regression ​models +   - [[analysis:​course:​week15|Spike sorting]]
-   ​- ​Principal component ​analysis, classification ​+
 === Resources === === Resources ===
  
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 === What this course is not === === What this course is not ===
  
-Formalthoroughcomplete. +This course will provide a brief introduction to a number of concepts which are themselves the subject of multiple courses and thick textbooks. These include signal processing topics such as Fourier analysis and filter designcomputer 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 researchyou should consider taking more in-depth coursework and/or working through relevant textbooks on your own.
 === Assessment (only relevant if you are taking this course for credit) === === Assessment (only relevant if you are taking this course for credit) ===
  
analysis/course.1380291355.txt.gz · Last modified: 2018/07/07 10:19 (external edit)