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.
|Number of pages||4|
|Journal||Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings|
|State||Published - 2004|
|Event||Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States|
Duration: Sep 1 2004 → Sep 5 2004