Spike Sorting: Bayesian Clustering of Non-Stationary Data

Spike sorting involves clustering spike trains recorded by a microelectrode according to the source neuron. It is a complicated problem, which requires a lot of human labor, partly due to the non-stationary nature of the data. We propose an automated technique for the clustering of non-stationary Gaussian sources in a Bayesian framework. At a first search stage, data is divided into short time frames and candidate descriptions of the data as a mixture of Gaussians are computed for each frame. At a second stage transition probabilities between candidate mixtures are computed, and a globally optimal clustering is found as the MAP solution of the resulting probabilistic model. Transition probabilities are computed using local stationarity assumptions and are based on a Gaussian version of the Jensen-Shannon divergence. The method was applied to several recordings. The performance appeared almost indistinguishable from humans in a wide range of scenarios, including movement, merges, and s...

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Aharon Bar-Hillel, Adam Spiro, Eran Stark
• Keywords: Involves Clustering Spike, NIPS 2004, NIPS 2007, Non-stationary Gaussian Sources, Transition Probabilities