ArticleJournal of neural engineering2016
Comparing offline decoding performance in physiologically defined neuronal classes.
Article in Journal of neural engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Volitional control of individual neurons in the human brain.Brain : a journal of neurology · 2021Article
- Operant conditioning of motor cortex neurons reveals neuron-subtype-specific responses in a brain-machine interface task.Scientific reports · 2020Article
- Neuron-Type-Specific Utility in a Brain-Machine Interface: a Pilot Study.The journal of spinal cord medicine · 2017Article
- Neural Representation and Causal Models in Motor Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- The Cluster Variation Method: A Primer for Neuroscientists.Brain sciences · 2016Article
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Authors and funding
6 authors.
Funding
Abstract
objectiveRecently, 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
resultsWe 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.
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