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Published 1998 | Published
Book Section - Chapter Open

On the Separation of Signals from Neighboring Cells in Tetrode Recordings

Abstract

We discuss a solution to the problem of separating waveforms produced by multiple cells in an extracellular neural recording. We take an explicitly probabilistic approach, using latent-variable models of varying sophistication to describe the distribution of waveforms produced by a single cell. The models range from a single Gaussian distribution of waveforms for each cell to a mixture of hidden Markov models. We stress the overall statistical structure of the approach, allowing the details of the generative model chosen to depend on the specific neural preparation.

Additional Information

© 1998 Massachusetts Institute of Technology. This work has benefited considerably from important discussions with both Bill Bialek and Sam Roweis. John Hopfield has provided invaluable advice and mentoring to MS. We thank Jennifer Linden and Philip Sabes for useful comments on an earlier version of the manuscript. Funding for various components of the work has been provided by the Keck Foundation, the Sloan Center for Theoretical Neuroscience at Caltech, the Center for Neuromorphic Systems Engineering at Caltech, and the National Institutes of Health.

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August 19, 2023
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January 13, 2024