SSIMS Toolbox v4.0

Carlos Vargas-Irwin Jonas B. Zimmermann Jacqueline Hynes

Donoghue Lab, Brown University, 2012-2019.

https://github.com/DonoghueLab/SSIMS-Analysis-Toolbox

Quick-Guide

The purpose of this toolbox is to generate dimensionality-reduced Spike train Similarity (SSIM) Maps from discrete or continuous neural data, facilitating visualization and further analysis.

To get started:

1. Download the tool box from Github: Toolbox Repository

2. Add toolbox to your MATLAB path.

3. Optional, but recommended Installation of complied Matlab code for significantly(!) increased performance see ‘install.md’.

4. Open SSIMS_democenter_out.m in Matlab: for guidance on using SSIMS (dim-reduced Ensemble Activity Spiketrain Simliarty Maps) with a single demo dataset (discrete data).

5. For more details on both methods see publication [1] or our webpage: Donoghue Lab - Github Page - Analysis Toolbox.

Any questions should be directed to carlos_vargas-irwin@brown.edu

Version history

• 4.0.0: March 2019 Added Public SSIMS toolbox to Github; updated readME and install instructions; created webpage
• 3.0.11: 24 May 2017 Added getCSIMS function for continuous data
• 3.0.10: 3 November 2016 Add build instructions for macOS 12 and MATLAB 2016b
• 3.0.9: 15 September 2016 Major overhaul of the toolbox structure. Removed legacy functions, improved function signatures Add example with real data Improved installation instructions This is a pre-release to test functionality before wider distribution
• 3.0: 11 May 2016 Rewrite of most of the toolbox. We now use armadillo for linear algebra functions. There are also efficient functions to extract spike trains in windows, based on custom C++ classes efficiently handling spike trains. Build instructions for Windows greatly improved
• 2.2: 17 Novemeber 2014 First public release.

References

[1] Vargas-Irwin CE, Brandman DM, Zimmermann JB, Donoghue JP, Black MJ (Jan. 2015). “Spike Train SIMilarity Space (SSIMS): A Framework for Single Neuron and Ensemble Data Analysis”. In: Neural Computation. 27(1), pp.1–31.

[2] Van der Maaten, Laurens J P and Geoffrey E Hinton (Nov. 2008). “Visualizing High-Dimensional Data Using t-SNE”. In: Journal of Machine Learning Research 9, pp. 2579–2605.

[3] Victor, J D and K P Purpura (1996). “Nature and precision of temporal coding in visual cortex: a metric-space analysis”. In: Journal of Neurophysiology 76.2, pp. 1310–26.