title: Spike sorting with support vector machines.
abstract: 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.
authors.affiliation: Dept. of Biomedical Eng., Johns Hopkins Univ., Baltimore, MD 21218, USA. jvogelst@jhu.edu
authors.forename: R
authors.initials: R
authors.lastname: Jacob Vogelstein
authors.forename: Kartikeya
authors.initials: K
authors.lastname: Murari
authors.forename: Pramodsingh H
authors.initials: PH
authors.lastname: Thakur
authors.forename: Chris
authors.initials: C
authors.lastname: Diehl
authors.forename: Shantanu
authors.initials: S
authors.lastname: Chakrabartty
authors.forename: Gert
authors.initials: G
authors.lastname: Cauwenberghs