SIMNETS-and-Maps-Analysis-Toolbox

Quick-Guide

The purpose of this toolbox is to generate dimensionality-reduced Spike train Similarity (SSIM) and Neuron Similarity Network (SIMNET) Maps from spike train 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 SIMNETS_Live_tutorial.mlx' in Matlab: for detailed guidance on how to use SIMNETS (dim-reduced Neuron Similarity Map) and all of its sub-functions with two different demo datasets.

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

6. For more details on both methods, see pre-print [2], publication [3], or our webpage: Donoghue Lab - Github Page - Analysis Toolbox

INSTALLATION OF C/C++ OPTIMIZED MATLAB VERSION OF TOOLBOX (optional):

The toolbox is completely implemented in MATLAB. To get started, just add the sub-folder for_MATLAB to your MATLAB path. However, certain basic MATLAB functions are rather slow. Therefore, core functionality has ALSO been implemented in C/C++ and can be compiled as mex files. This will improve performance dramatically (~2 orders of magnitude).

CITATION OF WORK:

Please cite the DOI for the SIMNETS pre-print and the DOI for the Software Repository (doi: 'assignment pending') when using this software and/or this analysis framework for analyzing your own data. Please contact the authors listed below if you are considering using the demo datasets included in this repository for purposes other than testing the code.

[1] SSIM and SIMNETS Analysis Toolbox: DOI: ( 'assignment pending')

[2] bioRxiv Pre-print: Jacqueline Hynes, David Brandman, Jonas Zimmerman, John Donoghue, Carlos Vargas-Irwin (2018). SIMNETS: a computationally efficient and scalable framework for identifying networks of functionally similar neurons . DOI: https://doi.org/10.1101/463364)

[3] Vargas-Irwin, C. E., Brandman, D. M., Zimmermann, J. B., Donoghue, J. P., & Black, M. J. (2015). Spike Train SIMilarity Space (SSIMS): A Framework for Single Neuron and Ensemble Data Analysis (2014).

This toolbox integrates two algorithms to achieve the dimensionality-reduced Similarity Maps:

[4] 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.

[6]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.

Version history

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• 4.0.0: 22 Feb 2019 . @JBHynes Added SIMNETS (NEURON SIMILARITY)TOOLBOX, updated build/install instructions. Added toolbox to Github. First public release of SIMNETS. First public release.

QUESTIONS:

@author Jacqueline Hynes. Copyright (c) Jacqueline Hynes, Brown University. All rights reserved.

Questions? Contact Carlos_Vargas_irwin@brown.edu or Jacqueline_Hynes@Brown.edu We are happy to help with any trouble shooting and provide guidance on how to best analyze/interpret your own data.