The main parameters used by Spikes_Link are:
Some parameters can depend on the specific spike sorting algorithm of choice (\(N_{min}\)\(N_{max}\)) or the expected quality of the clustering (\(p_{out}\)). An initial expected stability is parameterized by \(T_{max}\), with some drifting being allowed within a block without compromising the performance (as seen in Fig. \ref{582093}), although this might depend on the chosen spike sorting algorithm.  In this work, \(N_{OS}\) was set to 500;  a much larger value would increase the computing time used by the spike sorting algorithm, whereas a much smaller value would affect the performance of the overlapping metric. For sparse neurons that could be absent in some blocks, the parameter \(CS\) will cap the number of blocks in which the same waveform will be searched. \(MS\) measures how many mergers of previous classes need to be detected before loss of isolation is confirmed. This is also important, as blocks with high levels of noise can affect the performance on a given block, and having \(MS>1\) gives the chance to the algorithm to continue isolating individual clusters in the following blocks. Finally, \(S_{TH}\) defines the minimum scale in blocks at which a class is considered as valuable. Therefore, the parameters are very intuitive and have a direct relationship with the experimental conditions. Moreover, it is not necessary to define other thresholds like the maximum distance between clusters, which is hard to estimate but commonly used in tracking algorithms.