title: Kalman filter mixture model for spike sorting of non-stationary data.
abstract: Nonstationarity in extracellular recordings can present a major problem during in vivo experiments. In this paper we present automatic methods for tracking time-varying spike shapes. Our algorithm is based on a computationally efficient Kalman filter model; the recursive nature of this model allows for on-line implementation of the method. The model parameters can be estimated using a standard expectation-maximization approach. In addition, refractory effects may be incorporated via closely related hidden Markov model techniques. We present an analysis of the algorithm's performance on both simulated and real data.
authors.affiliation: Center for Theoretical Neuroscience, Columbia University, 1051 Riverside Dr., New York, NY 10032, United States. amc2257@columbia.edu
authors.forename: Ana
authors.initials: A
authors.lastname: Calabrese
authors.forename: Liam
authors.initials: L
authors.lastname: Paninski
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D001330:Electronic Data Processing; D008390:Markov Chains; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D016011:Normal Distribution; D009862:Online Systems; D013997:Time Factors