[ Bayesian Blocks]
\cite{Bar_Hillel_2006} Bayesian framework, with the source neurons modeled as a non-stationary mixture-of-Gaussians. At a first search stage, the data are divided into short time frames, and candidate descriptions of the data as mixtures-of-Gaussians are computed for each frame separately. At a second stage, transition probabilities
between candidate mixtures are computed, and a globally optimal clustering solution is found as the maximum-a-posteriori solution of the resulting
probabilistic model. It requires large samples of spikes with gaussian distribution.
High computational cost
\cite{Wolf_2009} A sequential extension of expectation maximization to fit a Gaussians mixture model (GMM) in each time frame using the clustering results from the previous time interval as a prior. The cluster means obtained in one time frame to seed the EM algorithm in the next time frame, while still allowing for signal non-stationarity and changing numbers of recorded neurons in transition between time frames.
Looks like can't handle sparse neurons, too few spikes per interval
\cite{Shalchyan_2014} Bayesian clustering approach that makes no assumptions on the distribution of the clusters and use kernel-based density estimation of the clusters in every time interval as a prior for Bayesian classification of the data in the subsequent time interval. The PCA basis of the first segment was fixed for extraction of features from spike waveforms of all time frames. It was also assumed that the spikes were classified into their neuronal sources in the first time segment.
*\cite{Shan_2017} Mixture of a fixed number of cluster with a t-distribution using windows of 1 minute. Has limitation with overclustering. The quality of the initial fit is an important factor in determining the user workload.
Loss of isolation occurs when two drifting clusters occupy the same region of feature space at different times, which appears as cluster overlap under a stationary analysis
\cite{Dhawale_2017} Sucesives SPC with all the detected spikes
LO LLAMAMOS SPIKELINK
[Spike sorting que soporta difting]
Mountainsort, support drift if the cluster doesn't have discontinuities and the clusters don't overlap in the feature spaces in any time \cite{Chung_2017}
Ironclus (new version of JRclus under development)
Kilosort
Combinato \cite{Niediek_2016} , using euclidean distances to join clusters
Other methods use a gradual adaptation of a fixed number of neurons \cite{Franke_2009,Calabrese_2011,Pouzat_2004}. This allow them to track neurons even when the are not isolated, but they are sensitive to errors produced in the spike sorting algorithm used to detecte the number of classes, and dynamic changes in that number.