Comparing offline decoding performance in physiologically defined neuronal classes

Matthew D. Best, Kazutaka Takahashi, Aaron J. Suminski, Christian Ethier, Lee E. Miller, Nicholas G. Hatsopoulos

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Objective: Recently, several studies have documented the presence of a bimodal distribution of spike waveform widths in primary motor cortex. Although narrow and wide spiking neurons, corresponding to the two modes of the distribution, exhibit different response properties, it remains unknown if these differences give rise to differential decoding performance between these two classes of cells. Approach: We used a Gaussian mixture model to classify neurons into narrow and wide physiological classes. Using similar-size, random samples of neurons from these two physiological classes, we trained offline decoding models to predict a variety of movement features. We compared offline decoding performance between these two physiologically defined populations of cells. Main results: We found that narrow spiking neural ensembles decode motor parameters better than wide spiking neural ensembles including kinematics, kinetics, and muscle activity. Significance: These findings suggest that the utility of neural ensembles in brain machine interfaces may be predicted from their spike waveform widths.

Original languageEnglish (US)
Article number026004
JournalJournal of Neural Engineering
Issue number2
StatePublished - Jan 29 2016


  • motor cortex
  • offline decoding
  • spike width

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience


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