Satellite tutorial SA3: Signal processing and data analysis in Matlab



Dr. Cengiz Gunay
School of Science and Technology, Georgia Gwinnett College, USA

Tutorial time:

Time zone: Los Angeles New York Berlin Sydney
June 30, 2021 noon - 3 pm 3 - 6 pm 21:00 - 24:00 05:00 am - 08:00 am (July 1)

Draft schedule

NY Time Sessions with 5 minute break between
3:00 pm Introduction & installation
3:45 pm Practice: Signal processing
4:30 pm Practice: Tabular analysis
5:15 pm Practice: Plotting
6:00 pm End of tutorial

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Matlab (Mathworks, Natick, MA) is a popular computing environment that offers an alternative to more advanced environments with its simplicity, especially for those less computationally inclined or for collaborating with experimentalists. In this tutorial, we will focus on the following tasks in Matlab:

  1. Signal processing of recorded or simulated traces (e.g., filtering noise, spike and burst finding in single-unit intracellular electrophysiology data in current-clamp, and extracting numerical characteristics);
  2. analyzing tabular data (e.g. obtained from Excel or from the result of other analyses);
  3. plotting and visualization.

For all of these, we will take advantage of the PANDORA toolbox, which is an open-source project that has been proposed for analysis and visualization ( RRID: SCR_001831, [1]). PANDORA was initially developed for managing and analyzing brute-force neuronal parameter search databases. However, it has proven useful for various other types of simulation or experimental data analysis [2-7]. PANDORA’s original motivation was to offer an object-oriented program for analyzing neuronal data inside the Matlab environment, in particular with a database table-like object, similar to the “dataframe” object offered in the R ecosystem and the pandas Python module. PANDORA offers a similarly convenient syntax for a powerful database querying system. A typical workflow would constitute of generating parameter sets for simulations, and then analyze the resulting simulation output and other recorded data, to find spikes and to measure additional characteristics to construct databases, and finally analyze and visualize these database contents. PANDORA provides objects for loading datasets, controlling simulations, importing/exporting data, and visualization. In this tutorial, we use the toolbox’s standard features and show how to customize them for a given project.

Software tools:


  • Günay et al. 2009 Neuroinformatics, 7(2):93-111. doi: 10.1007/s12021-009-9048-z
  • Doloc-Mihu et al. 2011 Journal of biological physics, 37(3), 263–283. doi:10.1007/s10867-011-9215-y
  • Lin et al. 2012 J Neurosci 32(21): 7267–77
  • Wolfram et al. 2014 J Neurosci, 34(7): 2538–2543; doi: 10.1523/JNEUROSCI.4511-13.2014
  • Günay et al. 2015 PLoS Comp Bio. doi: 10.1371/journal.pcbi.1004189
  • Wenning et al. 2018 eLife 2018;7:e31123 doi: 10.7554/eLife.31123
  • Günay et al. 2019 eNeuro, 6(4), ENEURO.0417-18.2019. doi:10.1523/ENEURO.0417-18.2019

See all CNS*2021 Tutorials here