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Igor pro

Manufactured by MathWorks
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Sourced in Germany

IGOR Pro is a data analysis and visualization software used for scientific and engineering applications. It provides a comprehensive set of tools for data acquisition, analysis, and presentation. IGOR Pro is designed to handle a wide range of data types and offers advanced features for signal processing, curve fitting, and image analysis.

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Lab products found in correlation

Matlab 2014b 2015a, by MathWorks (2 mentions) Matlab 2012 and 2014a, by MathWorks (2 mentions) Matlab, by MathWorks (1 mentions) Bx51 upright microscope, by Olympus (1 mentions) Vaa3d, by MathWorks (1 mentions) Matlab 2013a, by MathWorks (1 mentions)

6 protocols using igor pro

1

Retinal Functomics Data Analysis

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Data analysis was performed using Matlab 2014b/2015a (The Mathworks Inc., Ismaning, Germany), and IGOR Pro. Data were organised in a custom written schema using the DataJoint for Matlab framework (github.com/datajoint/datajoint-matlab)54 (link). All data as well as basic visualisation routines are available at www.retinal-functomics.org.
Franke K., Berens P., Schubert T., Bethge M., Euler T, & Baden T. (2017). Inhibition decorrelates visual feature representations in the inner retina. Nature, 542(7642), 439-444.
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2

Calcium Imaging of Neuronal Activity

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All data are shown as mean ± SEM, where “n” refers to the number of cells (for electrophysiological recording experiments) or boutons or spines (for Ca imaging experiments). Summary data and the name and results of statistical tests are interspersed with text in parentheticals in the Results section. Preliminary analysis and data visualization was performed in Igor Pro and MATLAB (MathWorks). Statistical tests used Kruskal-Wallis with Wilcoxon’s rank-sum test and the Holm-Sidak correction for multiple comparisons. Significance was set to p < 0.05 unless otherwise stated.
Burke KJ J.r., Keeshen C.M, & Bender K.J. (2018). Two forms of synaptic depression produced by differential neuromodulation of presynaptic calcium channels. Neuron, 99(5), 969-984.e7.
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3

Matlab-based Data Analysis with DataJoint

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Data analysis was performed using Matlab 2012 and 2014a (The Mathworks Inc., Ismaning, Germany), and IGOR Pro. Data were organised in a custom written schema using the DataJoint for Matlab framework (https://github.com/datajoint/datajoint-matlab; D. Yatsenko, Tolias lab, Baylor College of Medicine).
Baden T., Berens P., Franke K., Rosón M.R., Bethge M, & Euler T. (2016). The functional diversity of retinal ganglion cells in the mouse. Nature, 529(7586), 345-350.
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4

Matlab-based Data Analysis with DataJoint

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Data analysis was performed using Matlab 2012 and 2014a (The Mathworks Inc., Ismaning, Germany), and IGOR Pro. Data were organised in a custom written schema using the DataJoint for Matlab framework (https://github.com/datajoint/datajoint-matlab; D. Yatsenko, Tolias lab, Baylor College of Medicine).
Baden T., Berens P., Franke K., Rosón M.R., Bethge M, & Euler T. (2016). The functional diversity of retinal ganglion cells in the mouse. Nature, 529(7586), 345-350.
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5

Retinal Functomics Data Analysis

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Data analysis was performed using Matlab 2014b/2015a (The Mathworks Inc., Ismaning, Germany), and IGOR Pro. Data were organised in a custom written schema using the DataJoint for Matlab framework (github.com/datajoint/datajoint-matlab)54 (link). All data as well as basic visualisation routines are available at www.retinal-functomics.org.
Franke K., Berens P., Schubert T., Bethge M., Euler T, & Baden T. (2017). Inhibition decorrelates visual feature representations in the inner retina. Nature, 542(7642), 439-444.
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6

Two-Photon Microscopy for Tissue Imaging

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All imaging experiments were carried out using a dual scan head raster scanning confocal microscope and control software developed by Prairie Systems (Middleton, WI) and incorporated into a BX51 upright microscope (Olympus America, Inc., Center Valley, PA). A x-y translation stage with integrated RIVETS system (Figure 4c) was used to position the tissue for imaging. Two-photon excitation was used with a Mai Tai pulsed Ti:Sapphire laser (Spectraphysics). Custom linescan patterns (Figure 5) were implemented using software generously provided by Bill Karsh (Applied Physics and Instrumentation Group, Janelia Farm Research Campus). All image analysis and presentation was performed using ImageJ (rsbweb.nih.gov/ij/), Matlab 2013a (www.mathworks.com) and plotted using IgorPro with the exception of Figure 6d which was produced using Vaa3D (penglab.janelia.org/proj/Vaa3D/Vaa3D/About_Vaa3D.html).
Osborne J.E, & Dudman J.T. (2014). RIVETS: A Mechanical System for In Vivo and In Vitro Electrophysiology and Imaging. PLoS ONE, 9(2), e89007.
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