  We propose a construction for joint feature learning and clustering of
multichannel extracellular electrophysiological data across multiple recording
periods for action potential detection and discrimination ("spike sorting").
Our construction improves over the previous state-of-the art principally in
four ways. First, via sharing information across channels, we can better
distinguish between single-unit spikes and artifacts. Second, our proposed
"focused mixture model" (FMM) elegantly deals with units appearing,
disappearing, or reappearing over multiple recording days, an important
consideration for any chronic experiment. Third, by jointly learning features
and clusters, we improve performance over previous attempts that proceeded via
a two-stage ("frequentist") learning process. Fourth, by directly modeling
spike rate, we improve detection of sparsely spiking neurons. Moreover, our
Bayesian construction seamlessly handles missing data. We present
state-of-the-art performance without requiring manually tuning of many
hyper-parameters on both a public dataset with partial ground truth and a new
experimental dataset.
