updated sorters_comparison
Spike sorting comparison methods
================================
+
SpikeInterface has a comparison module that can be used for three distinct use cases:
- * compare a spike sorting output with a ground-truth dataset
- * compare the output of two spike sorters (symmetric comparison)
- * compare the output of multiple spike sorters
+
+ 1. compare a spike sorting output with a ground-truth dataset
+ 2. compare the output of two spike sorters (symmetric comparison)
+ 3. compare the output of multiple spike sorters
Even if the three comparison cases share the same underlying idea (they compare spike trains!) the internal
@@ -12,8 +14,8 @@ implementations are slightly different.
-Comparison with ground truth
-----------------------------
+1. Comparison with ground truth
+-------------------------------
A ground-truth dataset can be a paired recording, in which the a neuron is recorded both extracellularly and with
a patch or juxtacellular electrode (either **in vitro** or **in vivo**), or it can be a simulated dataset
@@ -88,10 +90,10 @@ Given:
For matching, a **min_accuracy** threshold is used (0.5 by default). If the agreement is below this threshold,
the possible match is discarded.
- There are two methods to perform the match: **hugarian** and **best** match.
+ There are two methods to perform the match: **hungarian** and **best** match.
- The `hugarian method <https://en.wikipedia.org/wiki/Hungarian_algorithm>`_
+ The `hungarian method <https://en.wikipedia.org/wiki/Hungarian_algorithm>`_
finds the best association between GT and tested units. With this method, both GT and tested units can be matched
only to another unit, or not matched at all.
@@ -182,8 +184,22 @@ More information about **hungarian** or **best** match methods
Classification of identified units
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Compare the output of two spike sorters (symmetric comparison)
----------------------------------------------------------------
+Tested units are classified depending on their performance. We identify three different classes:
+
+ * **well-detected** units
+ * **false positive** units
+ * **redundant** units
+
+A **well-detected** unit is a unit whose performance is good. By default, a good performance is measured by an accuracy
+greater than 0.8-
+
+A **false positive** unit has low agreement scores for all GT units and it is not matched.
+
+A **redundant** unit has a relatively high agreement (>= 0.3 by default), but it is not a best match. This means that
+it could either be an oversplit unit or a duplicate unit.
+
+2. Compare the output of two spike sorters (symmetric comparison)
+------------------------------------------------------------------
The comparison of two sorter is a quite similar to the procedure of **compare to ground truth**.
The difference is that no assumption is done on which is the units are ground-truth.
@@ -198,8 +214,8 @@ As there is no ground-truth information, performance metrics are not computed.
However, the confusion and agreement matrices can be visualised to assess the level of agreement.
-Compare the output of multiple spike sorters
-----------------------------------------------
+3. Compare the output of multiple spike sorters
+------------------------------------------------
Comparison of multiple sorters uses the following procedure:
