Format

Send to

Choose Destination
J Neural Eng. 2016 Apr;13(2):026004. doi: 10.1088/1741-2560/13/2/026004. Epub 2016 Jan 29.

Comparing offline decoding performance in physiologically defined neuronal classes.

Author information

1
Committee on Computational Neuroscience, University of Chicago, Chicago, IL 60637, USA.

Abstract

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.

PMID:
26824791
PMCID:
PMC4855848
DOI:
10.1088/1741-2560/13/2/026004
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for IOP Publishing Ltd. Icon for PubMed Central
Loading ...
Support Center