Kalman filter mixture model for spike sorting of non-stationary data

Ana Calabrese; Liam Paninski

doi:10.1016/j.jneumeth.2010.12.002

Kalman filter mixture model for spike sorting of non-stationary data

J Neurosci Methods. 2011 Mar 15;196(1):159-69. doi: 10.1016/j.jneumeth.2010.12.002. Epub 2010 Dec 21.

Authors

Ana Calabrese¹, Liam Paninski

Affiliation

¹ Center for Theoretical Neuroscience, Columbia University, 1051 Riverside Dr., New York, NY 10032, United States. amc2257@columbia.edu

PMID: 21182868
DOI: 10.1016/j.jneumeth.2010.12.002

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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Action Potentials / physiology*
Algorithms
Animals
Computer Simulation*
Electronic Data Processing
Markov Chains
Models, Neurological*
Neurons / physiology*
Normal Distribution
Online Systems
Time Factors