After all these observations, we presented Spikes_Link a method to fully track neurons and handle spike sorting errors and stability fluctuations using ad hoc rules. The method uses a generic spike sorting algorithm to sort a block of spikes and using these results includes in the next block subsets of the spikes of the classes tracked. The addition of these overlapping sets has an extra utility, in the case of sparse neurons, more spikes will be present in each block, favoring the detection of these elusive classes. The proportion of the overlapping set of each class, assigned to a new cluster is used to define the relationship between that cluster and the current classes. Each cluster can be labeled as a new class or a match, split or merger of a known class.  The metric and the relationship between clusters have some similarities to the MONIC framework \cite{Spiliopoulou_2006}, being the main differences that MONIC track the dynamics of classes without considering the issues on the clustering and requires a natural superposition of the samples, nonviable for sparse neurons. After analyzing all the available blocks a graph reduction criteria is applied, this last step of Spikes_link reduces the spurious classes removing them or and merging them to the stable classes when it is possible.    
All the parameters used by Spikes_Link  are:
Some algorithms are obtained directly from the spike sorter used (  \(N_{min}\) ,  \(N_{max}\) ) or the expected quality of the result ( \(p_{out}\)). The expected stability is parametrized by \(T_{max}\), nevertheless some drifting could happen in the block without repercussions, especially if the used spike sorting method can handle it. \(MS\) measure how many mergers of previous classes can be detected until the sorting solution is trusted and loss of isolation confirmed, it has to be taken into consideration that, in some cases, blocks of high noise could occur and contaminate any spike sorter results. 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. Finally, \(S_{TH}\), defines the minimum scale in blocks at which the results will be inspected. The parameters has a direct relationship with the experimental conditions and it is not necessary to define more obscure parameters like the minimun distance between cluster, etc.