Background noise and spike overlap pose problems in contemporary spike-sorting strategies. In this paper, both issues are addressed by a hybrid scheme that combines the robust representation of spike waveforms to facilitate the reliable identification of contributing neurons with efficient data learning to enable the precise decomposition of coactivations.
The classic Tenenbaum‘s paper that introduces ISOMAP, a manifold learning approach featuring non-linear dimensionality reduction.
J. B. Tenenbaum, V. De Silva and J. C. Langford (2000). Science 290 (5500), 2319-2323
Template matching is a popular method in spike sorting, mostly employed in the overlap resolution task. Several algorithms have been proposed during the last 5 years, some of them featuring online implementations.
A description of NeuroML (http://www.neuroml.org) has been published in PLoS Computational Biology.
Padraig Gleeson, Sharon Crook, Robert C. Cannon, Michael L. Hines, Guy O. Billings, Matteo Farinella, Thomas M. Morse, Andrew P. Davison,
Subhasis Ray, Upinder S. Bhalla, Simon R. Barnes, Yoana D. Dimitrova, R.
Angus Silver
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