Annotated list of parameters
WaveClus
max_spk	If there are more than max_spk spikes, use template matching instead of SPC
template_type	Type of template matching method used - template matching is used for spike sorting speed up in the case of large number of spikes or for assigning spikes in the noise cluster to the existing clusters (if force_auto is set).
template_sdnum	Max radius of a cluster in standard deviations.
template_k	Number of nearest neighbours in case of template_type set to nn (nearest neighbour).
features	Type of spike features to use - wav (wavelets) or pca.
inputs	Number of wavelet coefficients to use as features for clustering.
scales	Number of wavelet decomposition levels used.
mintemp	SPC minimum temperature - a lower temperature value groups all data into a single cluster, while higher values allow the data to split into more clusters
maxtemp	SPC maximum temperature.
tempstep	How large is a SPC temperature step, when determining an optimal temperature.
num_temp	How many temperatures to try,  when determining an optimal temperature.
SWCycles	Number of Monte Carlo iterations used by SPC.
KNearNeighb	Number of data points used for the nearest neighbors interactions in the SPC.
randomseed	If 0, timestamp is used. Otherwise it can be used for reproducibility.
fname_in	Temporary file name.
min_clus_stop	Minimum size of a cluster (cluster will be deleted if the number of spikes it contains is lower than this value).
temp_plot	What scale to use for SPC temperature plot (GUI only)
force_auto	Automatically force membership of spikes assigned to noise cluster using template matching.
max_spikes	Maximum number of spikes to plot (GUI only).
segments	Number of segments into which is the data cut.
KlustaKwik
noDim	Number of PCA dimensions used for clustering.
exportMode	What type of spike features to cluster - 1 PCA, 2 raw datapoints
ChangedThresh	All log-likelihoods are recalculated if the fraction of instances changing class exeeds f (see DistThresh).
DistThresh	Time-saving paramter.  If a point has log likelihood more than d worse for a given class than for the best class, the log likelihood for that class is not recalculated.  This saves an awful lot of time.
FullStepEvery	All log-likelihoods are recalculated every n steps (see DistThresh).
MaxClusters	The random initial assignment will have no more than n clusters.
MinClusters	The random initial assignment will have no less than $MinClusters$ clusters.  The final number may be different, since clusters can be split or deleted during the course of the algorithm.
MaxIter	Maximum number of iteration from any initial point.
PenaltyMix	Amount of Bayesian information content (BIC) or Akaike information content (AIC) to use as a penalty for more clusters. Default of 0 sets to use all AIC. Use 1.0 to use all BIC (this generally produces fewer clusters).
SplitEvery	Test to see if any clusters should be split every n steps. 0 means don't split.
Osort
minNrSpikes	Minimum size of a cluster (cluster will be deleted if the number of spikes it contains is lower than this value).
correctionFactorThreshold	Value correcting a signal noise estimate used as a clustering threshold.