https://github.com/ahbarnett/validspike
Spike-sorting and validation metric codes in MATLAB and C
https://github.com/ahbarnett/validspike
Last synced: 3 months ago
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Spike-sorting and validation metric codes in MATLAB and C
- Host: GitHub
- URL: https://github.com/ahbarnett/validspike
- Owner: ahbarnett
- License: mit
- Created: 2015-08-13T20:27:26.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2017-01-11T22:13:20.000Z (over 8 years ago)
- Last Synced: 2025-01-23T18:51:36.224Z (4 months ago)
- Language: Matlab
- Size: 346 KB
- Stars: 12
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# validspike: spike sorting and validation tools in MATLAB
This is a package for spike sorting of extracellular multichannel neuronal recordings, computing validation metrics, and generating synthetic datasets.
Alex Barnett and Jeremy Magland, November 2014 - August 2015, SCDA, Simons Foundation
[email protected]
This accompanies the paper, "Validation of neural spike sorting algorithms without ground-truth information", Alex H. Barnett, Jeremy F. Magland, and Leslie F. Greengard (2015)
## Dependencies
This has been developed on a linux environment running MATLAB R2014a without any toolboxes.
The `mex` compiler is used. Also needed: GNU make, GCC, openmp.
(It has not been tested on Mac OSX; here you may need to adjust compiler options and the location of the `mex` compiler command.)
Optional dependencies: [spikespy](https://github.com/magland/spikespy) for time-series viewing, and
[MWrap](http://www.cs.cornell.edu/~bindel/sw/mwrap/) if you want to generate your own MEX interfaces.
## Setting up
1. If you don't have a MATLAB `startup.m` file, create one as explained [here](http://www.mathworks.com/help/matlab/ref/startup.html). If you want to be able to use the optional `spikespy` viewer in the example scripts, edit the path to it in `vs_startup.m`. Now include the following line in your `startup.m`, replacing `VSHOME` by the full path for this `validspike` package:
run VSHOME/vs_startup
1. In the top level (`validspike`) directory, run `make` to compile various C/MEX codes and contributed codes. You may need to adjust compiler flags in the file `mexopts.sh` in MATLAB's installation. For instance, on an ubuntu 12.04 system running MATLAB R2012a I needed to add the flag `--openmp` to `LDFLAGS` in the `glnxa64)` case.
1. From MATLAB, run `driver_clips` which should take less than 5 s and complete without error, reporting around 98% correct. It will generate synthetic data files of 100 MB in size in the `data/` directory from the example waveforms there. These synthetic files are then used in the self-tests of various functions. Also try the other `driver_*` functions in `examples/`
1. To perform a full test, run `testall` from MATLAB, which should take 2 minutes, produce a lot of figures, but complete without error.
1. Troubleshooting: if MATLAB says functions `sf` or `gf` are undefined, you have not correctly compiled the MEX interfaces.
## Basic usage
To spike sort time series data, we assume you have `Y` an M-by-N data array with M channels and N time points. Then call `[t l] = spikesort_timeseries(Y,samplefreq)` where `samplefreq` is the sampling rate in Hz. This will output firing times `t` (in sample units) and corresponding labels `l`. There are plenty of options and other outputs; see the help documentation for this function, and the options in `examples/demo_spikesort_timeseries_buzsaki.m` (which however relies on not-supplied data). For clip-based sorting, see documentation for `spikesort_clips`.
See examples of usage in `examples/driver_*` and for the expert see `paper_fig_drivers/*`
## Directories in this package
The directories are as follows, organized by task. We also list some but not all of the main routines in each directory.
In MATLAB you can also use eg `help stageA` to see a list of routines in directory `stageA`.
Most routines can be self-tested by running without arguments. See `testall.m` for a list of all tests.
The acronym EC means extra-cellular, the type of time-series recording that spike sorting
applies to.
####synthesis
Codes for generating artificial datasets given a set of waveform shapes
`synth_Poissonspiketrain` make synthetic time-series with noise
`synth_singleeventclips` make synthetic single-spike events (clips) with noise
`loaddemodata` loads to memory a default synthetic extracellular times-series
`loaddemoclips` loads to memory a default synthetic set of upsampled, aligned clips
####stageA
Algorithms for filtering, detection of events, basic preprocessing (eg measuring noise)
####stageB
Spike sorting algorithms for clustering, extraction of waveforms, clip-based sorting
####stageC_fitlib
Time-series fitting algorithms (greedy, etc) that assume a known waveform set
####validation
Algorithms for validating spike-sorting accuracy and stability
`labels_accuracy` summarize accuracy of a list of labels against "truth" list
`times_labels_accuracy` summarize accuracy of a list of times and labels against "truth" such lists
`labels_similarity` report confusion matrix and accuracy stats between two lists of labels
`getbestshuffling` find best label permutation between two integer lists
`times_labels_confusion_matrix` best-permutated confusion matrix between two lists of times and labels
####utilities
Assorted low-level functions
####visualization
Note: many better visualization features are available in the `spikespy' package.
`plot_spike_shapes` plots a set of multi-channel waveforms
`listenraw` play raw EC data as audio & write out .WAV file
`viewraw` plot raw EC data; replaced by `spikespy` package
####examples
These are driver scripts that can be called without arguments, and run complete experiments
`driver_clips` demonstrate clip-based spike sorting on synthetic clips data
`driver_timeseries` demonstrate spike sorting on synthetic time series data
`driver_clips_stability` shows validation of clip-based spike sorting
`driver_timeseries_stability` shows validation of time series spike sorting
`loaddata` examples of loading EC timeseries objects from various (not supplied) datasets
####contrib
3rd-party codes and utilites from other sources
## Other directories
`data/` example waveforms, and where around 100 MB of synthetic data is placed.
`paper_fig_drivers/` figure-generating codes for the above preprint and other research. EPS figure output is written to `~/spikesorting/validpaper/` which you should create if you want the EPS outputs.
`data_external/` and `data_valid/` are data directories used by us, that is accessed by some advanced examples and figure-generating codes.
`devel/` experimental codes.
## To do
* generalize the stability metric evaluation to variable K (# of neurons)
* better documentation of I/O interfaces, usage
* make demo data with more variation in neuron quality