This study introduces a new method for detecting and sorting spikes from multiunit
recordings. The method combines the wavelet transform, which localizes distinctive
spike features, with superparamagnetic clustering, which allows automatic classification
of the data without assumptions such as low variance or gaussian distributions. Moreover,
an improved method for setting amplitude thresholds for spike detection is proposed.
We describe several criteria for implementation that render the algorithm unsupervised
and fast. The algorithm is compared to other conventional methods using several simulated
data sets whose characteristics closely resemble those of in vivo recordings. For
these data sets, we found that the proposed algorithm outperformed conventional methods.
