TY - GEN
T1 - Spike Sorting
T2 - 18th Annual Conference on Neural Information Processing Systems, NIPS 2004
AU - Bar-Hillel, Aharon
AU - Spiro, Adam
AU - Stark, Eran
N1 - Funding Information:
We are grateful to Moshe Abeles and Itay Asher for their help in spike sorting and for insightful comments. We thank Hila Zadka and Yifat Prut for providing sorted spike data. We thank Daphna Weinshall for valuable discussions and for her continuing support. Aharon Bar-Hillel was supported by a Horowitz foundation grant.
PY - 2005/1/1
Y1 - 2005/1/1
N2 - 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 splits of clusters.
AB - 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 splits of clusters.
UR - http://www.scopus.com/inward/record.url?scp=37749005108&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:37749005108
SN - 0262195348
SN - 9780262195348
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
PB - Neural information processing systems foundation
Y2 - 13 December 2004 through 16 December 2004
ER -