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Introduction to STAToolkit

and data interchange with Chronux. Introduction to STAToolkit. Adapted from MBL Course in Neuro-Informatics. August 2008 and updated as of January 2010. Jonathan D. Victor. Department of Neurology and Neuroscience Weill Cornell Medical College. Credits.

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Introduction to STAToolkit

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  1. and data interchange with Chronux Introduction to STAToolkit Adapted from MBL Course in Neuro-Informatics August 2008 and updated as of January 2010 Jonathan D. Victor Department of Neurology and Neuroscience Weill Cornell Medical College

  2. Credits Laboratory of Neuroinformatics at Weill Cornell Medical College neuroanalysis.org Dan Gardner Michael Repucci David Goldberg Eliza Chan Ajit Jagdale Support: Human Brain Project/Neuroinformatics Grant MH068012 from NIMH, NINDS, NIA, NIBIB, and NSF

  3. STAToolkit<->Chronux Data/Metadata Pathways chronux STAToolkit STAToolkit algorithms chronux algorithms Data and metadata:.stam, stad, stap format STAToolkit results chronux results Data and metadata: MATLAB structure Data and metadata: Xin format Data and metadata: PLX format neurodatabase.org User data and metadata, user format Data and metadata: NEX format Data and metadata: DDT format Data and metadata: xml format User Interface Supplied by user Supplied by neurodatabase.org Supplied by chronux In course demo

  4. STAToolkit Overview • Open source • PC, Mac, Linux • Downloadable or web-based • Modular • High-level modules: matlab • Low-level modules: matlab wrappers around c

  5. Outline: STAToolkit Intro and Demo • Input data structure • Output data structures • Implementation of information-theoretic methods • Key computational modules • Key options • Examples • Chronux -> STAToolkit: this presentation • STAToolkit-> Chronux: see online resources

  6. Input Data Structure http://neuroanalysis.org/neuroanalysis/goto.do?page=.repository.toolkit_inputformat • A dataset has • N labeled recording sites (channels, units, etc.) • M labeled stimulus categories • Each (recording site, stimulus category) pair has a list of trials • Start time, stop time • Event times • Above can be • Created as a matlab structure • Read with staread.m (in STAToolkit) • Imported from Chronux with chronux2statinsp.m

  7. Output Data Structures http://neuroanalysis.org/neuroanalysis/goto.do?page=.repository.toolkit_outputstruct • 1-D count structures (histograms) • e.g., number of occurrences of each response symbol • entropy estimates from count histograms • 2-D structures (tables) • e.g., count of stimulus/response pairings • information estimates from count tables • Entropy and information estimates • for each method, value and estimated variance • entropy methods • plug-in • plug-in with classic (k-1)/N (“Treves-Panzeri-Miller-Carlton”) debiaser • plug-in with jackknife debiaser • Ma bound • Paninski BUB estimator • Chao-Shen estimator • Wolpert-Wolf estimator • NSB • information estimates derived from entropy estimates

  8. Easy way options STAToolkit high-level module information estimates spike trains Other way options information estimates spike trains low-level module low-level module low-level module auxiliary output auxiliary output auxiliary output Modular Implementation: Overview http://neuroanalysis.org/neuroanalysis/goto.do?page=.repository.toolkit_intro

  9. directcat.m: direct method (categorical stimuli) directbin.m: bin the spike trains directcountcond.m: create histograms of word counts infocond.m: calculate information from distributions of word counts directformal.m: direct method (continuous stimuli) directbin.m: bin the spike trains directcondformal.m: create conditional histograms of word counts directcounttotal.m: create histograms of total word counts infocond.m: calculate information from distributions of word counts binless.m: binless embedding method binlesswarp.m: warp time to uniformize firing rate binlessembed.m: embed the spike trains in a Euclidean space binlessinfo.m: calculate information via nearest-neighbor method metric.m: metric space method metricdist.m: calculate distances between pairs of spike trains metricclust.m: cluster spike trains basd on distance matrix matrixhist2d.m: form a confusion matrix from clustering info2d.m: calculate information from confusion matrix Modular Implementation: Details http://neuroanalysis.org/neuroanalysis/goto.do?page=.repository.toolkit_intro

  10. shared start and end time entropy estimation method(s) and parameters variance estimation method(s) direct method bin size multineuronal strategy binless embedding method embedding dimension(s) warping parameters singleton strategy metric space method metric family (spike time or spike interval) shift cost label cost clustering exponent STAToolkit: Options and Parameters http://neuroanalysis.org/neuroanalysis/goto.do?page=.repository.toolkit_infoopts

  11. Spike Train Analysis Toolkit: Web-based documentation

  12. STAToolkit/Chronux Demo: Importing Data LIP sample data from Chronux chronux2statinsp.m both neurons, all 8 directions

  13. STAToolkit/Chronux Demo: Selecting Data LIP sample data from Chronux chronux2statinsp.m one neuron, directions 3, 5, 6, -1 to +2 sec both neurons, all 8 directions

  14. Spike Train Analysis Server:Web-based data selection and parsing

  15. 2.5 2.5 2 2 1.5 1.5 Information (bits) Information (bits) 1 1 0.5 0.5 Original data ( ± 2 SE via Jackknife) No correction ± Shuffled data ( 2 SD) TPMC correction 0 0 Jackknife correction -0.5 -0.5 0.25 0.5 1 2 4 8 16 0.25 0.5 1 2 4 8 16 Inverse bin size (1/sec) Inverse bin size (1/sec) STAToolkit Demo: Direct Method statoolkit_demo_directcat.m, directcat.m one neuron, directions 3, 5, 6, -1 to +2 sec

  16. Embedded spike trains 2 2 Total 15 Timing Count 10 1 1 5 Dimension 3 Information (bits) Information (bits) 0 0 0 -5 -10 -1 -1 -15 20 50 Original data ( ± 2 SE) 0 -2 -2 ± Shuffled data ( 2 SD) Dimension 2 0 -20 Dimension 1 -40 1 2 3 4 5 1 2 3 4 5 -50 Maximal embedding dimension Maximal embedding dimension STAToolkit Demo: Binless Method statoolkit_demo_binless.m, binless.m one neuron, directions 3, 5, 6, -1 to +2 sec

  17. Distance matrix at maximum information (q=1) No correction TPMC correction 2 2 Jackknife correction 5 10 Information (bits) Information (bits) Spike train index 15 1 1 20 25 0 0 30 Original data ( ± 2 SE) 5 10 15 20 25 30 ± Shuffled data ( 2 SD) Spike train index 0 0.5 1 2 4 8 16 64 256 0 0.5 1 2 4 16 64 256 8 Temporal precision (q, 1/sec) Temporal precision (q, 1/sec) STAToolkit Demo: Metric Space Method statoolkit_demo_metric.m, metric.m one neuron, directions 3, 5, 6, -1 to +2 sec

  18. STAToolkit/Chronux Demo, Multiunit Analysis:Selecting Data LIP sample data from Chronux chronux2statinsp.m both neurons, directions 3, 5, 6, -0.1 to +0.3 sec

  19. 2 2 Information (bits) 1 1 Original data (± 2 SE via Jackknife) No correction TPMC correction Jackknife correction ± Shuffled data ( 2 SD) 0 0 0.25 0.5 1 2 4 8 16 0.25 0.5 1 2 4 8 16 Inverse bin size (1/sec) STAToolkit Demo, Multiunit Analysis:Direct Method LIP sample data from Chronux statoolkit_demo_directcat.m, directcat.m Information (bits) Inverse bin size (1/sec) both neurons, directions 3, 5, 6, -0.1 to +0.3 sec

  20. statoolkit_demo_metric_mu_dbs.m, modules of metric.m plug-in estimate tpmc debiased jackknife debiased k (label cost) k (label cost) k (label cost) q (shift cost, s-1) q (shift cost, s-1) q (shift cost, s-1) from shuffled data distance matrix at q=2.8, k=0.5 STAToolkit Demo, Multiunit Analysis:Mertric Space Method (Debiasing and Shuffling) LIP sample data from Chronux statoolkit_demo_metric_mu_dbs.m, modules of metric.m both neurons, directions 3, 5, 6, -0.1 to +0.3 sec

  21. STAToolkit Demo, Multiunit Analysis:Metric Space Method (Redundancy) LIP sample data from Chronux statoolkit_demo_metric_mu_red.m, modules of metric.m redundancy both neurons vs. each neuron H ( bits) Redundancy Index k (label cost) q (shift cost, sec-1) k (label cost) both neurons, directions 3, 5, 6, -0.1 to +0.3 sec

  22. Electronic Resources • STAToolkit: http://neuroanalysis.org/neuroanalysis/goto.do?page=.repository.toolkit_home • Chronux: http://www.chronux.org • Primary and review articles: • http://www-users.med.cornell.edu/~jdvicto/jdvpubsc.html • Background on metric space method • http://www-users.med.cornell.edu/~jdvicto/metricdf.html • Demos (in this wiki, in STAToolkitChronuxDemos.zip) • chronux2statin_demo.m • statin2chronux_demo.m

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