  We demonstrate the efficacy of a new spike-sorting method based on a Markov
Chain Monte Carlo (MCMC) algorithm by applying it to real data recorded from
Purkinje cells (PCs) in young rat cerebellar slices. This algorithm is unique
in its capability to estimate and make use of the firing statistics as well as
the spike amplitude dynamics of the recorded neurons. PCs exhibit multiple
discharge states, giving rise to multimodal interspike interval (ISI)
histograms and to correlations between successive ISIs. The amplitude of the
spikes generated by a PC in an "active" state decreases, a feature typical of
many neurons from both vertebrates and invertebrates. These two features
constitute a major and recurrent problem for all the presently available
spike-sorting methods. We first show that a Hidden Markov Model with 3
log-Normal states provides a flexible and satisfying description of the complex
firing of single PCs. We then incorporate this model into our previous MCMC
based spike-sorting algorithm (Pouzat et al, 2004, J. Neurophys. 91, 2910-2928)
and test this new algorithm on multi-unit recordings of bursting PCs. We show
that our method successfully classifies the bursty spike trains fired by PCs by
using an independent single unit recording from a patch-clamp pipette.
