Nonparametric Bayesian Discrete Latent Variable Models for ...

3 Dirichlet Process Mixture Models
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Figure 3.21: Manual spike sorting results. The two-dimensional projections of the peak-to-peak
amplitudes of spikes are plotted for all combinations of 4 channels. The expert
works on this representation, manually drawing boundaries around regions that
may correspond to the activity of a single neuron. After cluster assignments,
refractoriness is checked to verify the clustering. Each color represents a different
cluster.
should belong to different clusters. Note that the waveforms assigned to the clusters
c6 and c7 appear to be very similar, thus we may assume that due to the incremental
updates, the sampler fails to merge these two components together which in fact belong
to the same cluster (this is the case also for the the waveforms assigned to c8 and c9).
3.4.3 Conclusions
We presented experimental results for spike sorting using the conjugate DPMoG, the
conditionally conjugate DPMoG and the DPMFA models on different data representations.
Although the modeled density differs, the clustering results of all three models are
similar on the amplitude data and on the PCA projections. In all models, the mixing of
the Markov chain was poor when the whole waveforms were used as inputs. This result
can be attributed to the high dimensionality of the data. Although none of the models
seem to be mixing, the DPMFA model could move enough to explore a mode of the
posterior whereas the DPMoG could not move at all from the initial point. This shows
that the DPMFA can handle higher dimensional data more easily. However, the mixing
is not good enough for practical use of the model due to the incremental updates using
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