On the Separation of Signals from Neighboring Cells in Tetrode Recordings

Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)

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Maneesh Sahani, John Pezaris, Richard Andersen


We discuss a solution to the problem of separating waveforms pro(cid:173) duced by multiple cells in an extracellular neural recording. We take an explicitly probabilistic approach, using latent-variable mod(cid:173) els of varying sophistication to describe the distribution of wave(cid:173) forms 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.