3.1: Added rate-independent SPIKE-distance to SPIKY-loop (and to SPIKY but there it is not yet included in the GUI) (April 2021)
3.0: Added SPIKE-Order and Spike train order algorithm that allows to quantify consistency of spatio-temporal propagation patterns in sequences of discrete events (e.g. spike trains). This includes a sorting of the spike trains from leader to follower. (March 2017)
2.3: Updates and Bug fixes (August 2016)
2.2: Added a few more SPIKY_loop programs. Some minor corrections. Lots of testing (June 2015)
2.1: Applies the same edge correction originally introduced for the SPIKE-distance (see Kreuz et al., 2015) also to the ISI-distance. (May 2015)
2.0: Added the event detector which enables the application of the SPIKY-measures to continuous data. SPIKY now also includes a new program 'SPIKY_loop_trigger' which allows to easily compare different epochs of the same length within one dataset, typically triggered by the onset of a stimulus. From our point of view this is the first complete version of SPIKY. (March 2015)
1.3: SPIKE synchronization replaces event synchronization. In comparison with event synchronization this measure has a more intuitive normalization which also extends to the multivariate case. (February 2015)
1.2: Added a third and complementary measure, event synchronization (September 2014)
1.1: Simpler folder structure plus some improvements regarding the input of spike train groups and the extraction of dissimilarity matrices (July 2014)
1.0: Initial release (June 2014)
2.4: Lots of testing (May 2014)
2.3: Final improvements regarding the memory management. Now it should in principle be possible to analyze datasets of almost any size (May 2014)
2.2: Possibility within SPIKY to also select other variables (such as markers, separators, instants, selective and triggered averages) from a given variable / field stored in a Matlab-file; Protection against datasets that are very large (~ >100000 spikes).
The new program 'SPIKY_loop_surro' allows to compare the results obtained for a dataset against results obtained for spike train surrogates generated from that dataset. This can help in estimating the statistical significance (not yet completely finished). (April 2014)
2.1: Possibility within SPIKY to select spike train data from a given variable / field stored in a Matlab-file (March 2014)
2.0: New program 'SPIKY_loop' replaces 'SPIKY_no_plot'. The scope is the same but it adds the full functionality of SPIKY (access to time instants, selective and triggered averages as well as averages over spike train groups).
Correction of edge effect (spurious decrease of S to zero at the beginning resp. at the end of the spike trains due to a lack of previous resp. following spikes) (suggested by Conor Houghton) (March 2014)
1.8: Added Peri-Stimulus Time Histogram (PSTH) as well as possibility to edit spikes in STG with the keyboard, also simplified layout and menu (February 2014) 1.7: Improved figure layout including context menus which allow to shift subplots and to edit objects individually and collectively. Possibility to include colorbars for the dissimilarity matrices. Added second reset button which allows to reset SPIKY to the state where the data have been loaded but the dissimilarity measures have not yet been calculated (January 2014)1.6: Ability to select and sort spike trains and spike train groups. One can either sort by hand (deleting and shifting) or sort according to some predefined criteria (such as number of spikes and latency). Furthermore, once you have plotted some dendrograms you can also sort the spike trains according to the clustering obtained. (December 2013)
1.5: Improved memory management for piecewise linear SPIKE-distance (November 2013)
1.4: Generalization of allowed input formats for spikes: Three different options (October 2013)
- cell arrays (ca) with just the spike times (this is the preferred format used by SPIKY since it is most memory efficient. The two other formats will internally be converted into this format)
- regular matrices with each row being a spike train and zero padding (zp) in case the spike numbers are different.
- matrices representing time bins where each zero/one (01) indicates the absence/presence of a spike
In addition to this you can also load data in text format. Here spike times should be written as a matrix with each row being one spike train. The package now contains one example file for each format (‘testdata_ca.mat’, ‘testdata_zp.mat’, ‘testdata_01.mat’ as well as ‘testdata.txt’).
1.3: Added the program 'SPIKY_no_plot' which is complementary to the graphical user interface 'SPIKY'. Both programs can be used to calculate time-resolved spike train distances (ISI and SPIKE) between two (or more) spike trains. However, whereas SPIKY was mainly designed to facilitate the detailed analysis of one dataset, 'SPIKY_no_plot' is meant to be used in order to compare the results for many different datasets (e.g. in some kind of loop). The source codes are stripped-down and use a minimum number of input and output variables. (September 2013)
1.2: New input masks (keyboard and mouse) for selecting time markers, spike train separators and spike train groups (August 2013)
1.1: New input mask (keyboard and mouse) for selecting frames and selective/triggered averages (July 2013)
1.0: Initial release (June 2013)