Cengiz GunayS3:
PANDORA is an
open-source toolbox for Matlab (Mathworks, Natick, MA) has been proposed for analysis and visualization of single-unit intracellular electrophysiology data (
RRID: SCR_001831, Günay et al. 2009
Neuroinformatics, 7(2):93-111.
doi: 10.1007/s12021-009-9048-z). Even though there are more modern and popular environments, such as the Python and Anaconda ecosystem, Matlab still offers an advantage in its simplicity, especially towards those less computationally inclined, for instance for collaboration with experimentalists. PANDORA was originally intended for managing and analyzing brute-force neuronal parameter search databases (Günay et al. 2008
J Neurosci. 28(30): 7476-7491; Günay et al. 2010
J Neurosci. 30: 1686–98). However, it has been proven useful for other types of simulation or experimental data analysis (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). PANDORA’s original motivation was to offer object-oriented analysis specific to neuronal data inside the Matlab environment, in particular with a database table-like object, similar to R and the Python PANDAS toolbox’s “dataframe” object, and a new syntax for a powerful database querying system. The typical workflow would constitute of generating parameter sets for simulations, and then in the resulting output data, finding spikes and 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. Since it’s inception, it has grown with added functionality. In this showcase, we review the toolbox’s standard features and show how to customize them for a given project, and then introduce some of the new and experimental features, such as ion channel fitting, evolutionary/genetic algorithms. Furthermore, we will give a developers’ perspective for those who may be interested in adding modules to this toolbox.
Showcase Website with slides
Discussion page on Neurostars for comments and questions
Feedback survey
