.. spruce-docs documentation master file, created by
   sphinx-quickstart on Mon Jun  8 10:38:14 2020.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

Welcome to Spruce's documentation!
==================================

Spruce (2018-2020) was a project by `LeafLabs, LLC`_ and the `Synthetic Neurobiology Group`_ at MIT to gather high-quality neurophysiological data, with simultaneous extracellular and intracellular recordings, and use it to provide a ground-truth benchmark for new and existing spike sorting algorithms. Details about the data collection can be found in Brian Allen's paper_, and the raw data can be downloaded from {link TBD}. This website exists to describe the benchmarking results and some of their conclusions, as well as to provide an interactive viewer for analyzing them.

.. _`LeafLabs, LLC`: https://www.leaflabs.com/
.. _`Synthetic Neurobiology Group`: http://syntheticneurobiology.org/
.. _paper: http://syntheticneurobiology.org/publications/publicationdetail/298/25

The top-line results are as follows:

* **MountainSort4's performance is strongly dependent on electrode density**; it requires a low density to be effective.
* **Kilosort and KiloSort2 tend to have a "plateau" of performance**; adding more data does not improve performance, once they have enough channels to reach their peak.
* **KiloSort2 seems to be a "high-risk, high-reward" improvement of Kilosort**; it performs worse on more difficult datasets, but better on higher-quality datasets.
* **The traditional spike-sorting pipeline (detection, feature extraction, clustering) is not as powerful as more sophisticated approaches**. We explored using deep learning to improve traditional spike-sorting performance, and it still did not beat Kilosort.

.. toctree::
   :maxdepth: 2
   :caption: Contents:
   
   main
   plotly
   plotly2
   deep

Indices and tables
==================

* :ref:`genindex`
* :ref:`search`

.. * :ref:`modindex`