~~DISCUSSION~~

=== Contents ===

== Fundamentals ==

   * [[analysis:nsb2015:week1|Module 1: Setting up (MATLAB, paths, GitHub, lab database)]]
   * [[analysis:nsb2015:week2|Module 2: Introduction to Neuralynx data formats and preprocessing]]
   * Module 3: Visualizing raw neural data in MATLAB ([[analysis:nsb2015:week3long|long version]] to learn the guts, [[analysis:nsb2015:week3short|short version]] to just get stuff done)
 
== Spike data basics ==

   * [[analysis:nsb2015:week4|Module 4: Spike sorting]]
   * [[analysis:nsb2015:week11|Module 11: Spike train analysis: firing rate, interspike interval distributions, auto- and crosscorrelations]]
   * [[analysis:nsb2015:week12|Module 12: Spike train analysis II: tuning curves, encoding, decoding]]

== Local field potential data basics ==

   * [[analysis:nsb2015:week6|Module 6: Fourier series, transforms, power spectra]]
   * [[analysis:nsb2015:week7|Module 7: Filtering: filter design, use, caveats]]
   * [[analysis:nsb2015:week8|Module 8: Time-frequency analysis: spectrograms]]

== Intermediate topics ==

   * [[analysis:nsb2015:week9|Module 9: Time-frequency analysis II: cross-frequency coupling]]
   * [[analysis:nsb2015:week10|Module 10: Interactions between multiple signals: coherence and other connectivity measures]] 
   * [[analysis:nsb2015:week13|Module 13: Spike-field relationships: spike-triggered average, phase locking, phase precession]]

== Advanced topics ==

  * [[analysis:cosmo2014|Two-step Bayesian decoding with dynamic spatial priors]]
 
== Reference ==

   * [[analysis:nsb2015:week0|Coda: Principles of careful data analysis]]

   
=== Prerequisites ===

Basic familiarity with MATLAB. Depending on your background and programming experience you might find the following resources helpful:

  * Textbook: {{|Wallisch, MATLAB for Neuroscientists}}
  * [[http://www.mathworks.com/academia/student_center/tutorials/launchpad.html|"Getting Started with MATLAB" Primer]]. 

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 ===

This course is "standalone", but the following textbooks provide more in-depth treatment of some of the topics.

  * Textbook: {{|Leis, Digital Signal Processing using MATLAB for Students and Researchers}}
  * Textbook: {{|Johnston and Wu, Foundations of Cellular Neurophysiology}}
  * Textbook: {{|Dayan & Abbott, Theoretical Neuroscience}}

=== What this tutorial is ===

A hands-on introduction to basic management, visualization, and analysis of neurophysiology data (spike trains and LFPs from behaving rodents acquired using Neuralynx systems) using MATLAB. 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.

=== What this tutorial is not ===

This course 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!

=== Note for Linux and OS X users ===

The lab codebase is set up for machines running 64-bit Windows 7. To make the low-level loading functions work on OSX or Linux, you'll need to download them yourself from the Neuralynx website. Some more specific instructions are provided in subsequent modules when loading is introduced.