Spike sorting with support vector machines.
Spike sorting of neural data from single electrode recordings is a hard problem in machine learning that relies on significant input by human experts. We approach the task of learning to detect and classify spike waveforms in additive noise using two stages of large margin kernel classification and probability regression. Controlled numerical experiments using spike and noise data extracted from neural recordings indicate significant improvements in detection and classification accuracy over linear amplitude- and template-based spike sorting techniques.
Jacob Vogelstein
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Dept. of Biomedical Eng., Johns Hopkins Univ., Baltimore, MD 21218, USA. jvogelst@jhu.edu
Murari
Kartikeya
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Thakur
Pramodsingh H
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Diehl
Chris
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Chakrabartty
Shantanu
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Cauwenberghs
Gert
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