spiky 1.0.4
Spike sorting based on Gaussian Mixture Model
Keywords: spike sorting

**Spiky** - A Spike Sorting Package
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**Spiky** will allow you to sort spikes from single electrodes. The
clustering is performed by a Gaussian Mixture Model (GMM) and vanilla
Expectation-Maximization (EM) algorithm. To penalize complexity we are
using Bayesian Information Criterion (BIC).

**Spiky** is able to run confusion tests to evaluate how prone to
misclassification the clusters are. And also provides a cuantitative
meassure of how far each cluster is from the rest (in terms of
mahalanobis distance).

Please check our “Turorial section” to get an intuition of how to run
**Spiky**. And don’t forget to keep an eye on the “Description Section”
to understand how **Spiky** works.

**Spiky** is available through pypi so if you are runing python in your
computer, go ahead and type in terminal:

::

    - pip install Spiky

If you need python, we strongly recommend you to install **“conda”**.
(What is conda?: conda is a package and enviroment manager. It will keep
things tight and clean).

::

    "Conda" installation:

        On WINDOWS:
            - go to: https://conda.io/miniconda.html and download miniconda.
            - Double-click the .exe file.
            - Follow the instructions on the screen.
            - When installation is finished, from the Start menu,
              open the Anaconda Prompt.

        On LINUX:
            - go to: https://conda.io/miniconda.html and download miniconda.
            - Open terminal and use the "cd" command to navegate to the folder 
              where you downloaded your miniconda file
            - type: "bash Miniconda3-latest-Linux-x86_64.sh"
            - Follow the prompts on the installer screens.
            - To make the changes take effect, close and then re-open your 
              Terminal window.

        On MAC:
            - go to: https://conda.io/miniconda.html and download miniconda 
            - Open terminal and use the "cd" command to navegate to the folder 
              where you downloaded your miniconda file
            - type: "bash Miniconda3-latest-MacOSX-x86_64.sh"
            - Follow the prompts on the installer screens.
            - To make the changes take effect, close and then re-open your 
              Terminal window.

            NOTE: matplotlib needs a framework build to work properly with conda.
                  A workaround for this problem is obtained by type in terminal:
                - conda install python.app
                - Use "pythonw" rather than "python" to run python code

    Spiky installation:

        Open a terminal and type what comes next:
          - conda create --name snowflake python=3
          - source activate snowflake
          - pip install Spiky

        Note: we encourage you to pick a different name for your virtual 
              environment. We used "snowflake" just as an example

Now you can test **Spiky** by runing one of the available examples. Go
to TUTORIAL for instructions

Buzsaki dataset
^^^^^^^^^^^^^^^

Copy the folder called “buzsaki” that is under “examples” and paste it
in your computer’s desktop. The folder contains a dataset obtained from
BuzsakiLabs. By the way, have you checked his webpage? If you haven’t
done it yet, here is the `link <http://buzsakilab.com/wp/>`__

The dataset we have choosen is the simultaneous intracellular and
extracellular recording from the hippocampus of anesthetized rats hc-1
‘d533101.dat’ which is a good starting point (you can play with other
examples later). You can find the dataset details here:

-  Henze, DA; Harris, KD; Borhegyi, Z; Csicsvari, J; Mamiya, A; Hirase,
   H; Sirota, A; Buzsáki, G (2009): Simultaneous intracellular and
   extracellular recordings from hippocampus region CA1 of anesthetized
   rats. CRCNS.org. `link <http://dx.doi.org/10.6080/K02Z13FP>`__

Now, open a terminal, navegate up to “buzsaki” folder and type:

::

    python buzsaki.py

The terminal will prompt you with some general information like these:

::

    Preprocesing
        Simultaneous spikes deleted:  144
        Interpolated spike deleted:   11
        Threshold:                    130.47
        Detected peaks:               2977
        Extra features:               Energy, Amplitud, Area
        Preprocessing time:           2.45 sec.
        DONE
    Clustering
        100% | Elapsed Time: 0:00:04|################|Time: 0:00:04 | Neurons:      4
        Clusters found:     4
        Clustering time:    3.80 sec.
        L-ratios:
          0: 0.01
          1: 0.00
          2: 10.30
          3: 0.01
        DONE

When the process is finished, you should see a picture like the one
below showing the different spikes grouped by cluster:

.. figure:: https://raw.githubusercontent.com/rodriguez-facundo/Spiky/master/examples/buzsaki/images/spikes.png
   :alt: alt text

   alt text

The algorithm has found 4 clusters. We know from ground truth (provided
by BuzsakiLabs in the form of intracellular recording) that the
efficiency of the result is arround 90% (because we have found 860
spikes under the fourth label but the intercellular record shows that
there were actually 960 spikes). What happened with the rest? Well some
of the spikes just don’t show up in the extracellular recording and a
small fraction have been misclassified due to their low amplitud.

Lets now imagine for one second that we have no information about the
grown truth. So, the first thing we should keep an eye on are the
L-ratios displayed above. We can see that all of them except the third
one are very low (which is good, it means that the clusters are far away
from each other in terms of mahalanobis distance). So, to understand
what is really going on, we will have to run a blur test.

Please, close the previews plot and wait for the blur test to finish. A
print like this will be shown:

::

    Bluring
        100% | Elapsed Time: 0:00:04|################|Time: 0:00:04 | Neurons:      4
        DONE

And finally, a confusion matrix will appear on screen:

.. figure:: https://raw.githubusercontent.com/rodriguez-facundo/Spiky/master/examples/buzsaki/images/confusion.png
   :alt: alt text

   alt text

After blurring each spike with the noise of its own cluster, the
algorithm is able to reproduce the results for clusters 0, 1 and 3 but
is confusing labels on cluster number 2, so we got our liar.

Quiroga dataset
^^^^^^^^^^^^^^^

Copy the folder called “Quiroga” that is inside “examples” and paste it
in your computer’s desktop. The folder contains a dataset obtained from
the Centre for Systems Neuroscience at the University of Leicester. Take
a moment to check their `webpage <https://www2.le.ac.uk/centres/csn>`__

The dataset we have choosen is from simulated recording and are
available
`here <https://www2.le.ac.uk/centres/csn/research-2/spike-sorting>`__:

Now, open a terminal, navegate up to “Quiroga” folder and type:

::

    `python quiroga.py`

The terminal will prompt you with some general information like these:

::

    parameters/parameters.json file loaded correctly.
    Preprocesing
        Simultaneous spikes deleted:    85
        Interpolated spike deleted:     4
        Threshold:          106.75
        Detected peaks:     3336
        Extra features:     Energy, Amplitud, Area
        Preprocessing time:         2.91 sec.
        DONE
    Clustering
        100% | Elapsed Time: 0:00:03|################|Time: 0:00:03 | Neurons:      5
        Clusters found:     5
        CLustering time:        6.61 sec.
        L-ratios:
        0: 29.42
        1: 0.00
        2: 0.00
        3: 0.00
        4: 17.12
        DONE

When the process is finished, you should see a picture like the one
below showing the different spikes grouped by cluster:

.. figure:: https://raw.githubusercontent.com/rodriguez-facundo/Spiky/master/examples/Quiroga/images/spikes.png
   :alt: alt text

   alt text

The algorithm has found 5 clusters, but ones again, the l-ratios are
telling us that 2 of the clusters have spikes that are very close to
them, so let’s run a blurring test. Please, close the previews plot and
wait for the blur test to finish. A print like this will be shown:

::

    Bluring
        100% | Elapsed Time: 0:00:02|################|Time: 0:00:02 | Neurons:      6
        DONE

And finally, a confusion matrix will appear on screen:

.. figure:: https://raw.githubusercontent.com/rodriguez-facundo/Spiky/master/examples/Quiroga/images/confusion.png
   :alt: alt text

   alt text

We can see that two of the clusters are mixing spikes.

spiky.New(pfile=‘None’, rfile=‘None’):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

::

    This is the class constructor. It will create 
    an instance of the main spiky class.

    PARAMETERS
      pfile : str
        Path to the ‘.json’ file containing the parameters setting. 
        The name is a contraction for parameters_file

      rfile : str
        Path to the ‘.dat’ or ‘.mat’ file containing the raw data. 
        The name is a contraction for raw_data_file.

        Notes : 
            Use integer 16 to represent the data (float is just a waste of resources). 
                The file must contain the data of one dataset, so if you have multiple electrodes 
            within the same file, split them up into different files.

    ATTRIBUTES
      Note: This attributes will be available ones you call "run" within the spiky object that you created.

      prms : dict
        Dictionary containing the parameters setting.

      raw : ndarray
        Dataset array

      thres : float
        Threshold level for spike detection

      pks : ndarray
        Array containing the time of spikes

      spks : ndarray
        Spikes time series

      wvSpks : ndarray
        Wavelet decomposition of spikes

      extFeat : ndarray
        Array containing extra features such as Amplitud, Energy, Area

      X : ndarray
        Array containing normalized features for clustering

      gmm : Gaussian mixture class object
        The gaussian mixture object

      labels : ndarray
        Array containing the labels for each spike

      lr : ndarray
        L-ratios for each cluster

spiky.New.loadParams(pfile=‘None’):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

::

    Loads the ‘.json’ file containing the parameters setting.

    pfile : str
      Path to parameters '.json' file

spiky.New.loadRawArray(rarray):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

::

    Loads an array containing the data set.

    rarray : ndarray
      Array containing the dataset

spiky.New.loadRawFile(rfile):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

::

    Loads a ‘.mat’ or ‘.dat’ file containing the data set.

    rfile : str
      Path to the ‘.dat’ or ‘.mat’ file containing the raw data.

spiky.New.filter():
^^^^^^^^^^^^^^^^^^^

::

    Filters dataset using cascaded second-order sections digital 
    IIR filter defined by sos. The parameters are taken from the 
    ‘.json’ configuration file. The filter is zero phase-shift

spiky.New.run():
^^^^^^^^^^^^^^^^

::

    Main clustering method. The parameters are set as specified by ‘.json’ file.

spiky.New,plotClusters():
^^^^^^^^^^^^^^^^^^^^^^^^^

::

    Plots spike clusters as found by “run” method.

spiky.New.blur():
^^^^^^^^^^^^^^^^^

::

    Re-run the clustering algorithm after performing a 
    blur of spikes within same labels, and plots the 
    confusion matrix.

+--------------+
| ####         |
| PARAMETERS   |
| FILE:        |
+--------------+
| Traces:      |
+--------------+
| -            |
| prms[“trace” |
| ][“name”]    |
| : Defines a  |
| name for     |
| this set of  |
| parameters   |
+--------------+
| Spike        |
| detection:   |
+--------------+
| -            |
| prms[“spkD”] |
| [“thres”]    |
| : Defines    |
| the          |
| threshold    |
| level        |
| (default =   |
| 4.           |
| max/min=3.9- |
| 4.1          |
| as defined   |
| by           |
| Quian-Quirog |
| a            |
| paper) -     |
| prms[“spkD”] |
| [“way”]      |
| : Defines if |
| the          |
| algorithm    |
| will search  |
| for maximum  |
| or minimums  |
| in the       |
| dataset.     |
| (values:     |
| “valley” -   |
| “peaks”) -   |
| prms[“spkD”] |
| [“minD”]     |
| : Defines    |
| how many     |
| spaces       |
| between two  |
| consecutive  |
| peaks there  |
| should be in |
| order to     |
| take them as |
| separated    |
| peaks. -     |
| prms[“spkD”] |
| [“before”]:  |
| Defines how  |
| many spaces  |
| after the    |
| peak will be |
| taken to     |
| build the    |
| spike. -     |
| prms[“spkD”] |
| [“after”]    |
| : Defines    |
| how many     |
| spaces       |
| before the   |
| peak will be |
| taken to     |
| build the    |
| spike.       |
+--------------+
| Filtering:   |
+--------------+
| -            |
| prms[“filt”] |
| [“q”]        |
| : Filters    |
| order. -     |
| prms[“filt”] |
| [“hz”]       |
| : Nysquit    |
| frecuency. - |
| prms[“filt”] |
| [“low”]      |
| : Defines    |
| low          |
| frequency    |
| cut. -       |
| prms[“filt”] |
| [“high”]     |
| : Defines    |
| High         |
| frequency    |
| cut.         |
+--------------+
| Spike        |
| alignment:   |
+--------------+
| -            |
| prms[“spkA”] |
| [“resol”]    |
| : Defines    |
| the          |
| resolution   |
| used to      |
| compute      |
| interpolatio |
| n            |
| and          |
| alignment    |
| (equal to    |
| the number   |
| of           |
| intermediate |
| point taken  |
| between two  |
| consecutive  |
| points in    |
| the spike    |
+--------------+
| Spike        |
| errase:      |
+--------------+
| -            |
| prms[“spkE”] |
| [“minD”]     |
| : Delete     |
| spike if it  |
| contains 2   |
| peaks        |
| separated    |
| less than    |
| “minD”       |
| positions    |
| and the      |
| relative     |
| amplitud of  |
| each one is  |
| bigger than  |
| “lvl”. -     |
| prms[“spkE”] |
| [“lvl”]      |
| : Delete     |
| spike if it  |
| contains 2   |
| peaks        |
| separated    |
| less than    |
| “minD”       |
| positions    |
| and the      |
| relative     |
| amplitud of  |
| each one is  |
| bigger than  |
| “lvl”.       |
+--------------+
| Wavelet      |
| decompositio |
| n:           |
+--------------+
| -            |
| prms[“wv”][“ |
| lvl”]        |
| : Level of   |
| decompositio |
| n            |
| for          |
| multilevel   |
| wavelet      |
| decompositio |
| n.           |
| -            |
| prms[“wv”][“ |
| func”]       |
| : Function   |
| to be used   |
| for wavelet  |
| decompositio |
| n.           |
| -            |
| prms[“wv”][“ |
| mode”]       |
| : Boundary   |
| condition to |
| use in       |
| wavelet      |
| decompositio |
| n            |
+--------------+
| Clustering:  |
+--------------+
| -            |
| prms[“gmm”][ |
| “maxK”]      |
| : Maximum    |
| number of    |
| clusters to  |
| look for     |
| solutions. - |
| prms[“gmm”][ |
| “ftrs”]      |
| : Number of  |
| features to  |
| take into    |
| account. -   |
| prms[“gmm”][ |
| “maxCorr”]:  |
| Maximum      |
| correlation  |
| allowed      |
| between      |
| features -   |
| prms[“gmm”][ |
| “inits”]     |
| : Number of  |
| random       |
| weights      |
| initializati |
| ons          |
+--------------+
| Blurring:    |
+--------------+
| -            |
| prms[“blur”] |
| [“alpha”]    |
| : Blurring   |
| intensity    |
| (0-1)        |
+--------------+

### ACKNOWLEDGMENT
------------------

I would like to thank Eugenio
Urdapilleta\ `1 <https://www.researchgate.net/profile/Eugenio_Urdapilleta>`__
and Damian
Dellavale\ `2 <https://www.researchgate.net/profile/Damian_Dellavale2>`__
for their guidance.

Preprosesing of data is handled as described by:

::

    - Quian Quiroga R, Nadasdy Z, Ben-Shaul Y (2004) **Unsupervised Spike Detection and Sorting with 
    Wavelets and Superparamagnetic Clustering**. Neural Comp 16:1661-1687.

L-ratio calculation is computed following:

::

    - Schmitzer-Torbert et al. **Quantitative measures of cluster quality for use in extracellular recordings** 
    Neuroscience 131 (2005) 1–11 11

Confusion Matrix calculation is computed acording to:

::

    - Alex H. Barnetta, Jeremy F. Maglandb, Leslie F. Greengardc **Validation of neural spike sorting 
    algorithms without ground-truth information** Journal of Neuroscience Methods 264 (2016) 65–77

Example dataset was obtained from:

::

    - Henze, DA; Harris, KD; Borhegyi, Z; Csicsvari, J;  Mamiya, A; Hirase, H; Sirota, A; Buzsáki, G (2009): 
    **Simultaneous intracellular and extracellular recordings from hippocampus region CA1 of anesthetized rats**. 
    CRCNS.org.http://dx.doi.org/10.6080/K02Z13FP